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Introducing Claude Fable 5.1 and Claude Mythos 5.1 · Anthropic News
<!DOCTYPE html><html lang="en" class="anthropicsans_dce02d96-module__tEBbKW__variable anthropicserif_e7e46c4-module__uImRTq__variable anthropicmono_fae19af3-module__c5XAsG__variable copernicus_e225fe92-module__gIXlfG__variable styrenea_ba30709d-module__X1taUa__variable styreneb_ef815608-module__QsOdUa__variable tiempostext_b1e9a056-module__lQ_52W__variable jetbrainsmono_4a81325a-module__1wasHq__variable"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/_next/static/chunks/2gepcixj9k_19.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/176anidfv50rd.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/39_4h3-nzxpcm.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/06kopkblj3cwa.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/0hpj44a23iwyo.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/138-1_49eoue5.js"/><script src="/_next/static/chunks/3nc6x0_y5iwnk.js" async=""></script><script src="/_next/static/chunks/0qt_yb7tkmhnh.js" async=""></script><script src="/_next/static/chunks/1xyeebhobpx1t.js" async=""></script><script src="/_next/static/chunks/turbopack-3a3ubgu7ap1i1.js" async=""></script><script src="/_next/static/chunks/2yl1jv4w6po1z.js" async=""></script><script src="/_next/static/chunks/1ntn7efqc-iiw.js" async=""></script><script src="/_next/static/chunks/0e3-jir0v5afw.js" async=""></script><script src="/_next/static/chunks/0gn4ie1d0fyv-.js" async=""></script><script src="/_next/static/chunks/3k_473ybucr6n.js" async=""></script><script src="/_next/static/chunks/1o_u397p26ij6.js" async=""></script><script src="/_next/static/chunks/1rl656pqd0bzv.js" async=""></script><script src="/_next/static/chunks/1dxzjrw9eglfx.js" async=""></script><script src="/_next/static/chunks/2hm_c-8yq3epn.js" async=""></script><script src="/_next/static/chunks/1m2tuo2yn9z9q.js" async=""></script><script src="/_next/static/chunks/3-fnghd0zoogo.js" async=""></script><script src="/_next/static/chunks/0od98pyc9u2nj.js" async=""></script><script src="/_next/static/chunks/1-9e3zcgh8gk1.js" async=""></script><script src="/_next/static/chunks/0b2dmya4y8ovs.js" async=""></script><link rel="preload" href="/_next/static/chunks/1plsl93aq761e.css" as="style"/><meta name="next-size-adjust" content=""/><meta name="theme-color" content="#141413"/><title>Introducing Claude Fable 5.1 and Claude Mythos 5.1 \ Anthropic</title><meta name="description" content="Our most advanced models for coding and knowledge work. 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frontier</span></a></li><li><a href="#scientific-research" class="LaunchSideToc-module-scss-module__3hrKvq__link" draggable="false"><span class="LaunchSideToc-module-scss-module__3hrKvq__pill">Scientific research</span></a></li><li><a href="#safety-security-and-alignment" class="LaunchSideToc-module-scss-module__3hrKvq__link" draggable="false"><span class="LaunchSideToc-module-scss-module__3hrKvq__pill">Safety</span></a></li><li><a href="#mythos" class="LaunchSideToc-module-scss-module__3hrKvq__link" draggable="false"><span class="LaunchSideToc-module-scss-module__3hrKvq__pill">Claude Mythos 5.1</span></a></li><li><a href="#cost-and-availability" class="LaunchSideToc-module-scss-module__3hrKvq__link" draggable="false"><span class="LaunchSideToc-module-scss-module__3hrKvq__pill">Cost and availability</span></a></li></ol></nav><header class="LaunchHero-module-scss-module__RCsuha__launch-hero"><div class="LaunchHero-module-scss-module__RCsuha__words"><p class="text-prehead-tracked LaunchHero-module-scss-module__RCsuha__date">September 2026</p><h1 class="LaunchHero-module-scss-module__RCsuha__title" aria-label="Claude Fable 5.1 and Mythos 5.1"><span class="LaunchHero-module-scss-module__RCsuha__title-line" aria-hidden="true"><span class="LaunchHero-module-scss-module__RCsuha__title-claude">:Claude:</span> Fable<!-- --> 5.1</span><span class="LaunchHero-module-scss-module__RCsuha__title-line LaunchHero-module-scss-module__RCsuha__title-line-second" aria-hidden="true">and Mythos 5.1</span></h1><nav class="LaunchHero-module-scss-module__RCsuha__index" aria-label="Contents"><a href="#introduction" class="LaunchHero-module-scss-module__RCsuha__index-row" style="--fx-row:0"><span class="LaunchHero-module-scss-module__RCsuha__index-row-inner"><span class="LaunchHero-module-scss-module__RCsuha__index-number">[<!-- -->1<!-- -->]</span><span class="LaunchHero-module-scss-module__RCsuha__index-leader" aria-hidden="true">................................................................................................................................................................</span><span>Introduction</span></span></a><a href="#frontier" class="LaunchHero-module-scss-module__RCsuha__index-row" style="--fx-row:1"><span class="LaunchHero-module-scss-module__RCsuha__index-row-inner"><span class="LaunchHero-module-scss-module__RCsuha__index-number">[<!-- -->2<!-- -->]</span><span class="LaunchHero-module-scss-module__RCsuha__index-leader" aria-hidden="true">................................................................................................................................................................</span><span>A new performance frontier</span></span></a><a href="#scientific-research" class="LaunchHero-module-scss-module__RCsuha__index-row" style="--fx-row:2"><span class="LaunchHero-module-scss-module__RCsuha__index-row-inner"><span class="LaunchHero-module-scss-module__RCsuha__index-number">[<!-- -->3<!-- -->]</span><span class="LaunchHero-module-scss-module__RCsuha__index-leader" aria-hidden="true">................................................................................................................................................................</span><span>Scientific research</span></span></a><a href="#safety-security-and-alignment" class="LaunchHero-module-scss-module__RCsuha__index-row" style="--fx-row:3"><span class="LaunchHero-module-scss-module__RCsuha__index-row-inner"><span class="LaunchHero-module-scss-module__RCsuha__index-number">[<!-- -->4<!-- -->]</span><span class="LaunchHero-module-scss-module__RCsuha__index-leader" aria-hidden="true">................................................................................................................................................................</span><span>Safety, security, and alignment</span></span></a><a href="#mythos" class="LaunchHero-module-scss-module__RCsuha__index-row" style="--fx-row:4"><span class="LaunchHero-module-scss-module__RCsuha__index-row-inner"><span class="LaunchHero-module-scss-module__RCsuha__index-number">[<!-- -->5<!-- -->]</span><span class="LaunchHero-module-scss-module__RCsuha__index-leader" aria-hidden="true">................................................................................................................................................................</span><span>Claude Mythos 5.1</span></span></a></nav></div><span class="LaunchHero-module-scss-module__RCsuha__credit">Made with Fable 5.1</span></header><article class="ManateeSanityContainer-module-scss-module__6Ouj0G__article"><div class="Body-module-scss-module__z40yvW__body LaunchBody-module-scss-module__nlZCva__launch-body" data-theme="ivory"><span id="introduction" class="LaunchBody-module-scss-module__nlZCva__anchor"></span><p class="Body-module-scss-module__z40yvW__reading-column Body-module-scss-module__z40yvW__article-summary">We’re introducing Claude Fable 5.1 and Claude Mythos 5.1. They’re the world’s most advanced models for coding and knowledge work—and their research capabilities offer an early glimpse of how AI models will contribute to scientific progress.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Claude Fable 5.1 and Claude Mythos 5.1 are the same model, but with different levels of safeguards. Fable 5.1 is generally available, while Mythos 5.1 is available only through our trusted access programs; its safeguards are specifically designed to support work in cybersecurity and the life sciences.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Alongside its increased capabilities, Fable 5.1 takes important steps towards addressing the feedback we’ve received from customers on price, data retention, and safeguards.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Price<em>.</em></strong> Fable 5.1 will cost an estimated 25% less than Fable 5 for typical workloads, wherever usage is billed by token. This is because we’re reducing our pricing on cache reads (where the model reads inputs that have already been processed and stored). For highly agentic work, the savings will often be much larger—up to approximately 45%.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Data retention<em>. </em></strong>Our new system of Enterprise Frontier Safeguards (EFS) gives customers complete privacy (the same as a zero data retention policy) while still being state-of-the-art at preventing adversarial use. EFS works by storing data in cloud infrastructure controlled entirely by the customer, not Anthropic. It will be made available to enterprise customers in phases, beginning later this fall. Until EFS is available, eligible customers will be able to use Fable 5.1 with zero data retention.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Safeguards<em>. </em></strong>We’ve improved our safeguards to reduce false positives (where the system flags benign content). In cybersecurity, our newest safeguards block 60% fewer false positives than before. In part, this is because Fable 5.1 can now be used to discover software vulnerabilities—though not to develop exploits for them. In biology, we’ve established an access program, developed in partnership with the US government, to enable access to Claude Mythos 5.1’s advanced biology capabilities. We expect to open enrollment for scientists soon.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="frontier">A new performance frontier</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Claude Fable 5.1 sets a new standard for coding, knowledge work, and long-running problem-solving tasks. The charts below show that Fable 5.1 is capable of much higher performance than its predecessor, Fable 5. And when set to Low or Medium effort, Fable 5.1 achieves results similar to or better than Fable 5’s at a much lower cost. (Note that Fable 5.1 defaults to High effort in Claude Code, and to Medium in Claude Cowork and on Claude.ai.)</p><div class="Body-module-scss-module__z40yvW__media-column"><section class="ViewSwitcher-module-scss-module__kN30kq__root"><div class="ViewSwitcher-module-scss-module__kN30kq__tabs" role="tablist" aria-label="Views"><button type="button" role="tab" id="_R_meqnpfibrb_-tab-0" aria-selected="true" aria-controls="_R_meqnpfibrb_-panel-0" tabindex="0" class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__tabActive">Agentic scientific research</button><button type="button" role="tab" id="_R_meqnpfibrb_-tab-1" aria-selected="false" aria-controls="_R_meqnpfibrb_-panel-1" tabindex="-1" class="ViewSwitcher-module-scss-module__kN30kq__tab">Agentic terminal coding</button><button type="button" role="tab" id="_R_meqnpfibrb_-tab-2" aria-selected="false" aria-controls="_R_meqnpfibrb_-panel-2" tabindex="-1" class="ViewSwitcher-module-scss-module__kN30kq__tab">Multidisciplinary reasoning</button><button type="button" role="tab" id="_R_meqnpfibrb_-tab-3" aria-selected="false" aria-controls="_R_meqnpfibrb_-panel-3" tabindex="-1" class="ViewSwitcher-module-scss-module__kN30kq__tab">Agentic coding</button><div class="ViewSwitcher-module-scss-module__kN30kq__more" hidden=""><button type="button" class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__moreButton" aria-label="More views" aria-haspopup="menu" aria-expanded="false"><svg class="ViewSwitcher-module-scss-module__kN30kq__moreIcon" viewBox="0 0 12 12" aria-hidden="true"><line x1="1" y1="6" x2="11" y2="6"></line><line class="ViewSwitcher-module-scss-module__kN30kq__moreIconVertical" x1="6" y1="1" x2="6" y2="11"></line></svg></button><div class="ViewSwitcher-module-scss-module__kN30kq__menu" role="menu" hidden=""></div></div><div class="ViewSwitcher-module-scss-module__kN30kq__measure" aria-hidden="true"><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Agentic scientific research</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Agentic terminal coding</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Multidisciplinary reasoning</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Agentic coding</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__moreButton"><svg class="ViewSwitcher-module-scss-module__kN30kq__moreIcon" viewBox="0 0 12 12" aria-hidden="true"><line x1="1" y1="6" x2="11" y2="6"></line><line class="ViewSwitcher-module-scss-module__kN30kq__moreIconVertical" x1="6" y1="1" x2="6" y2="11"></line></svg></span></div></div><div class="ViewSwitcher-module-scss-module__kN30kq__stage"><div id="_R_meqnpfibrb_-panel-0" role="tabpanel" aria-labelledby="_R_meqnpfibrb_-tab-0" class="ViewSwitcher-module-scss-module__kN30kq__panel" data-panel="true"><div 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class="chart-module-scss-module__3ia3wq__mark" cx="371" cy="198" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="371" y="183" text-anchor="middle">med</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.08s" tabindex="0" aria-label="Fable 5.1 · high: 40.0% $20.3"><circle class="chart-module-scss-module__3ia3wq__mark" cx="519" cy="165" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="519" y="150" text-anchor="middle">high</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.12s" tabindex="0" aria-label="Fable 5.1 · xhigh: 49.5% $31.8"><circle class="chart-module-scss-module__3ia3wq__mark" cx="732" cy="92" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="732" y="77" text-anchor="middle">xhigh</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.16s" tabindex="0" aria-label="Fable 5.1 · max: 52.6% $37.9"><circle class="chart-module-scss-module__3ia3wq__mark" cx="816" cy="69" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="816" y="54" text-anchor="middle">max</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.12s" tabindex="0" aria-label="Fable 5 · low: 12.3% $17.1"><circle class="chart-module-scss-module__3ia3wq__mark" cx="437" cy="377" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="437" y="362" text-anchor="middle">low</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.16s" tabindex="0" aria-label="Fable 5 · med: 21.4% $25"><circle class="chart-module-scss-module__3ia3wq__mark" cx="618" cy="307" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="618" y="292" text-anchor="middle">med</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.2s" tabindex="0" aria-label="Fable 5 · high: 25.0% $34.3"><circle class="chart-module-scss-module__3ia3wq__mark" cx="768" cy="280" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="768" y="265" text-anchor="middle">high</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.24s" tabindex="0" aria-label="Fable 5 · xhigh: 23.4% $36"><circle class="chart-module-scss-module__3ia3wq__mark" cx="792" cy="292" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="792" y="317" text-anchor="middle">xhigh</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.28s" tabindex="0" aria-label="Fable 5 · max: 24.7% $44.1"><circle class="chart-module-scss-module__3ia3wq__mark" cx="888" cy="282" r="7" fill="var(--chart-cloud)"></circle><text 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The public leaderboard (3 trials/task, Claude Code harness) reports Claude Opus 5 at 30.0% and Claude Fable 5 at 21.4%; our setup reproduces them at 29.0% and 24.7%, respectively, both within noise.</p></figure></div></div><div id="_R_meqnpfibrb_-panel-1" role="tabpanel" aria-labelledby="_R_meqnpfibrb_-tab-1" hidden="" class="ViewSwitcher-module-scss-module__kN30kq__panel" data-panel="true"><div class="Body-module-scss-module__z40yvW__media-column"><figure class="chart-module-scss-module__3ia3wq__frame"><div class="chart-module-scss-module__3ia3wq__panel"><figcaption class="chart-module-scss-module__3ia3wq__caption"><span class="chart-module-scss-module__3ia3wq__title">Terminal-Bench 4.0</span><span class="chart-module-scss-module__3ia3wq__subtitle">Accuracy vs Cost</span></figcaption><ul class="chart-module-scss-module__3ia3wq__legend chart-module-scss-module__3ia3wq__legendRow" aria-label="Series"><li class="chart-module-scss-module__3ia3wq__legendItem"><svg 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class="chart-module-scss-module__3ia3wq__footnote">Terminal-Bench 4.0 scores by cost (log scale), at each effort level. Claude Fable 5.1 and Claude Mythos 5.1 are the same underlying model; the gap between them reflects the tasks on which our earlier, less precise cyber safeguards intervened. With the improvements we’re making to these safeguards today, we expect the difference between the models to be much smaller.</p></figure></div></div><div id="_R_meqnpfibrb_-panel-2" role="tabpanel" aria-labelledby="_R_meqnpfibrb_-tab-2" hidden="" class="ViewSwitcher-module-scss-module__kN30kq__panel" data-panel="true"><div class="Body-module-scss-module__z40yvW__media-column"><figure class="chart-module-scss-module__3ia3wq__frame"><div class="chart-module-scss-module__3ia3wq__panel"><figcaption class="chart-module-scss-module__3ia3wq__caption"><span class="chart-module-scss-module__3ia3wq__title">Humanity&#x27;s Last Exam</span><span class="chart-module-scss-module__3ia3wq__subtitle">Accuracy vs Cost</span></figcaption><ul class="chart-module-scss-module__3ia3wq__legend chart-module-scss-module__3ia3wq__legendRow" aria-label="Series"><li class="chart-module-scss-module__3ia3wq__legendItem"><svg class="chart-module-scss-module__3ia3wq__swatch" viewBox="0 0 14 14" 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class="chart-module-scss-module__3ia3wq__mark" cx="407" cy="184" r="7" fill="url(#_R_bkmeqnpfibrb_-hatch-matcha-mark)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="407" y="169" text-anchor="middle">low</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.04s" tabindex="0" aria-label="Fable 5.1 (with tools) · med: 63.0% $0.67"><circle class="chart-module-scss-module__3ia3wq__mark" cx="474" cy="121" r="7" fill="url(#_R_bkmeqnpfibrb_-hatch-matcha-mark)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.08s" tabindex="0" aria-label="Fable 5.1 (with tools) · high: 64.8% $1.05"><circle class="chart-module-scss-module__3ia3wq__mark" cx="592" cy="82" r="7" fill="url(#_R_bkmeqnpfibrb_-hatch-matcha-mark)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.12s" tabindex="0" aria-label="Fable 5.1 (with tools) · xhigh: 65.1% $2.28"><circle 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fill="url(#_R_bkmeqnpfibrb_-hatch-cloud-mark)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="908" y="128" text-anchor="middle">max</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.24s" tabindex="0" aria-label="Fable 5.1 (no tools) · low: 53.2% $0.3"><circle class="chart-module-scss-module__3ia3wq__mark" cx="259" cy="332" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="259" y="357" text-anchor="middle">low</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.27999999999999997s" tabindex="0" aria-label="Fable 5.1 (no tools) · med: 55.9% $0.46"><circle class="chart-module-scss-module__3ia3wq__mark" cx="375" cy="272" r="7" fill="var(--chart-matcha)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.32s" tabindex="0" aria-label="Fable 5.1 (no tools) · high: 58.0% $0.75"><circle class="chart-module-scss-module__3ia3wq__mark" cx="504" cy="228" r="7" fill="var(--chart-matcha)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.36s" tabindex="0" aria-label="Fable 5.1 (no tools) · xhigh: 60.4% $1.53"><circle class="chart-module-scss-module__3ia3wq__mark" cx="692" cy="177" r="7" fill="var(--chart-matcha)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.4s" tabindex="0" aria-label="Fable 5.1 (no tools) · max: 60.9% $2.23"><circle class="chart-module-scss-module__3ia3wq__mark" cx="793" cy="165" r="7" fill="var(--chart-matcha)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="793" y="150" text-anchor="middle">max</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.36s" tabindex="0" aria-label="Fable 5 (no tools) · low: 50.6% $0.17"><circle class="chart-module-scss-module__3ia3wq__mark" cx="112" cy="387" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="112" y="412" text-anchor="middle">low</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.39999999999999997s" tabindex="0" aria-label="Fable 5 (no tools) · med: 55.9% $0.4"><circle class="chart-module-scss-module__3ia3wq__mark" cx="336" cy="273" r="7" fill="var(--chart-cloud)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.44s" tabindex="0" aria-label="Fable 5 (no tools) · high: 56.9% $0.62"><circle class="chart-module-scss-module__3ia3wq__mark" cx="451" cy="252" r="7" fill="var(--chart-cloud)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.48s" tabindex="0" aria-label="Fable 5 (no tools) · xhigh: 57.4% $0.91"><circle class="chart-module-scss-module__3ia3wq__mark" cx="554" cy="240" r="7" fill="var(--chart-cloud)"></circle></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.52s" tabindex="0" aria-label="Fable 5 (no tools) · max: 57.8% $1.7"><circle class="chart-module-scss-module__3ia3wq__mark" cx="720" cy="233" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="720" y="258" text-anchor="middle">max</text></g></svg></div></div><p class="chart-module-scss-module__3ia3wq__footnote">Humanity’s Last Exam scores by cost (log scale), at each effort level. 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class="chart-module-scss-module__3ia3wq__mark" cx="623" cy="239" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="623" y="264" text-anchor="middle">high</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.24s" tabindex="0" aria-label="Fable 5 · xhigh: 68.4% $11.73"><circle class="chart-module-scss-module__3ia3wq__mark" cx="713" cy="194" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="713" y="219" text-anchor="middle">xhigh</text></g><g class="chart-module-scss-module__3ia3wq__node" style="--chart-delay:0.28s" tabindex="0" aria-label="Fable 5 · max: 70.5% $17.32"><circle class="chart-module-scss-module__3ia3wq__mark" cx="834" cy="144" r="7" fill="var(--chart-cloud)"></circle><text class="chart-module-scss-module__3ia3wq__pointLabel" x="834" y="169" text-anchor="middle">max</text></g></svg></div></div><p class="chart-module-scss-module__3ia3wq__footnote">CursorBench 3.2.0 by cost (log scale), at each effort level.</p></figure></div></div></div></section></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Fable 5.1 avoids shortcuts that result in poorer-quality work, and it’s smart enough to fix the root causes of software issues. For example, in testing by the investment firm Millennium, Fable 5.1 found the cause of a rare crash in its internal systems that none of its engineers (or any other model) had been able to explain after several years of trying.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Here, you can see how Fable 5.1 compares across various benchmarks:</p><div class="Body-module-scss-module__z40yvW__media-column"><figure class="BenchmarkGrid-module-scss-module__zKqBAG__figure"><div class="BenchmarkGrid-module-scss-module__zKqBAG__wrap"><div class="BenchmarkGrid-module-scss-module__zKqBAG__layer"><table class="BenchmarkGrid-module-scss-module__zKqBAG__table"><thead><tr><td class="BenchmarkGrid-module-scss-module__zKqBAG__corner"></td><th scope="col" class="BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__pinned">Fable 5.1</th><th scope="col" class="">Fable 5</th><th scope="col" class="">Opus 5</th><th scope="col" class="">GPT-5.6 Sol</th></tr></thead><tbody><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic scientific research</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Terminal-Bench-Science 0.1 [1]</span></span></th><th colSpan="3"></th></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__first-data-row"><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic scientific research</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Terminal-Bench-Science 0.1 [1]</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">52.6%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">24.7%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">29.0%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">22.4%</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic coding</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Terminal-Bench 4.0</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic coding</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Terminal-Bench 4.0</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">55.8%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">60.9% (Mythos 5.1)</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">42.0%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">52.3%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">37.3%</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Knowledge work</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">GDPval-AA v2</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Knowledge work</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">GDPval-AA v2</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">1853</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">1723</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">1824</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">1711</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Computer use</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">OSWorld 2.0 [2]</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Computer use</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">OSWorld 2.0 [2]</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">77.9%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">partial</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">72.9%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">partial</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">75.4%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">partial</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">—</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">partial</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Computer use</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">OSWorld 2.0</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Computer use</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">OSWorld 2.0</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">41.7%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">strict</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">36.1%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">strict</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">39.6%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">strict</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">—</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">strict</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Multidisciplinary reasoning</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Humanity&#x27;s Last Exam</span></span></th><th colSpan="3"></th></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__split-row"><th scope="row" rowSpan="2" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Multidisciplinary reasoning</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">Humanity&#x27;s Last Exam</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">60.9%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">no tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">57.8%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">no tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">56.6%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">no tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">—</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">no tools</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__split-row"><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">65.0%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">with tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">63.8%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">with tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">63.6%</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">with tools</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">—</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__qualifier">with tools</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Business workflows</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">AutomationBench</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Business workflows</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">AutomationBench</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">31.4%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">17.1%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">26.9%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">19.6%</span></td></tr><tr class="BenchmarkGrid-module-scss-module__zKqBAG__label-row" aria-hidden="true"><th class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-pin"><span class="BenchmarkGrid-module-scss-module__zKqBAG__label-row-inner"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic coding</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">CursorBench 3.2.0</span></span></th><th colSpan="3"></th></tr><tr class=""><th scope="row" rowSpan="1" class="BenchmarkGrid-module-scss-module__zKqBAG__label-cell"><span class="BenchmarkGrid-module-scss-module__zKqBAG__category">Agentic coding</span><span class="BenchmarkGrid-module-scss-module__zKqBAG__benchmark">CursorBench 3.2.0</span></th><td class="BenchmarkGrid-module-scss-module__zKqBAG__pinned BenchmarkGrid-module-scss-module__zKqBAG__subject BenchmarkGrid-module-scss-module__zKqBAG__win-ours"><span class="BenchmarkGrid-module-scss-module__zKqBAG__value BenchmarkGrid-module-scss-module__zKqBAG__value-bold">73.4%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">70.5%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">70.0%</span></td><td class=""><span class="BenchmarkGrid-module-scss-module__zKqBAG__value">67.2%</span></td></tr></tbody></table></div><div class="BenchmarkGrid-module-scss-module__zKqBAG__scroll" aria-hidden="true"><div class="BenchmarkGrid-module-scss-module__zKqBAG__spacer"></div></div></div><figcaption class="BenchmarkGrid-module-scss-module__zKqBAG__caption"><strong></strong> <!-- -->Fable 5.1 was evaluated with its production safeguards enabled. On tasks where these safeguards intervened, Fable 5.1 and Fable 5 scored a zero on OSWorld 2.0, and Fable 5 scored a zero on AutomationBench. In all other interventions from our safeguards, cybersecurity tasks were completed by Claude Opus 4.8, and biology tasks were completed by Claude Opus 5. This likely reduces the performance of Fable 5.1 and Fable 5 on these benchmarks.</figcaption></figure></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Our early-access partners noticed these performance upgrades, and also picked up on more qualitative improvements in the model’s outputs. Here’s what they told us:</p><div class="Body-module-scss-module__z40yvW__media-column"><section class="TestimonialCarousel-module-scss-module__o0jJtW__carousel"><div class="TestimonialCarousel-module-scss-module__o0jJtW__pagination"><button type="button" class="TestimonialCarousel-module-scss-module__o0jJtW__pagination-button" disabled="">Previous</button><p class="TestimonialCarousel-module-scss-module__o0jJtW__pagination-count">1<!-- --> of <!-- -->22</p><button type="button" class="TestimonialCarousel-module-scss-module__o0jJtW__pagination-button">Next</button></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__stage"><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="active" aria-hidden="false"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“In internal benchmarks, Claude Fable 5.1 solves more of our coding problems than Fable 5 or Opus 5, and achieves state of the art on trading intuition. While prior models became hard to follow the longer they worked, Fable 5.1 remains readable over long, multi-step tasks.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Jane Street Capital</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Craig Falls, Head of Quantitative Research</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“We’re moving our Opus 5 traffic in Devin to Claude Fable 5.1 on launch day. It matched or edged out Fable 5 in our testing at a lower cost per task, and with the new cache read pricing a Fable-class model is finally economical for the workloads we’d kept on Opus, starting with code review.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Cognition</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Walden Yan, Co-founder and CPO</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“A particular piece of code had an extremely rare crash, about one in a million runs, that nobody on our team had explained in four to five years. Every model I tried, including Fable 5, missed it. Claude Fable 5.1 was the first to find it. It disassembled an external vendor library, matched it against the core dump, and traced the crash to a bug in that library. The time it would have taken to conduct that analysis is hard to justify.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Millennium</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Damien, Senior Portfolio Manager</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Claude Fable 5.1 built a complex prototype in about three days. It did initial research across all of our services code and documentation to produce a novel and extensible design. It then ran for hours unattended, with strong verification loops, to implement the entire prototype. I would wake up in the morning to the next phase finished, with a full visual walkthrough of what it built and clear evidence of its success.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">MongoDB</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Ron Sanzone, Staff Software Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“It’s friendly Fable. Fable-level intelligence, Opus-level price, Sonnet-speed. In our tests it was about twice as fast as Opus 5 and used half as many tokens, so for anyone used to using Opus as their daily driver it’s an obvious upgrade.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Every</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Dan Shipper, CEO</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“On our research suite, Claude Fable 5.1 set new best scores. On one task it came up with a novel solution along a completely different axis than we’d seen from other models or from human researchers in the past, which took its results well above the previous plateau. It&#x27;s better at creative problem solving and getting that flash of insight you need to solve a difficult problem.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">IMC</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Marquis Wong, Principal AI Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“As part of our ongoing evaluation of AI models, Claude Fable 5.1 delivered impressive results in our tests. Using Claude Code, it correctly identified the root cause of every broken build we tested, across all the effort levels. It also communicates more effectively than earlier Anthropic models, with updates that are more concise and easier to follow.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Red Hat</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Josh Boyer, Distinguished Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“We asked Claude Fable 5.1 to review a clinical research project for Rakuten Medical that three other frontier models had signed off on. It found a gap none of them had seen and insisted on testing it further. It then proposed a completely new hypothesis, turning a dataset we had written off into a new research direction in one afternoon. It’s the first time a frontier model like Claude has empowered us to explore new research in this way.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Rakuten</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Felix Giovanni Virgo, Principal AI Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Claude Fable 5.1 is very smart. On our 30-day simulated run-a-business eval, where the model gets full access to simulated Square tools, customers, employees, and vendors, it was far more efficient per token than Opus 5. We plan to use it to work through our most complex scenarios, the kind that used to take days of whiteboarding, so our engineering teams can keep moving fast.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Square (Block)</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Willem Avé, Global Head of Product</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“For anything research, greenfield or long-horizon, I would absolutely use Claude Fable 5.1 as the orchestrator. One unattended 38-hour run on a machine learning problem diagnosed a prior result as a label artifact, made the correction, kicked off six parallel experiments that ran overnight, and returned with a result and next steps. Given an open-ended prompt to find the highest-leverage problem nobody owned, it surfaced an unowned alert tied to a production outage, pulled the logs and prescribed the fix.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Ramp</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Dwight Temple, Sr. Machine Learning Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“The standout in Claude Fable 5.1 is the writing: more understandable, more meaningful, and it follows our writing guidance better. In blind tests against Fable 5, I preferred its writing and output. And in Canva Code it built a rhythm game with real music and on-beat gameplay matched to the level it generated, something no other model we tested delivered.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Canva</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Danny Wu, Head of AI</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“On our PowerPoint eval, Claude Fable 5.1 produced the best decks of any model we’ve tested, both in slide craft and in fully answering our research topic. That same completeness showed up in our Citations eval, where it had the best fact recall over financial documents. And on complex, multi-part questions, it’s the first model we’ve seen answer every part.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Hebbia</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Aabhas Sharma, CTO</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“We had a change that touched more than eight services across three codebases. Claude Fable 5.1 mapped the whole workflow end to end, in extremely fine detail, from the incoming service call down to the individual function and the database tables and rows, and it was accurate all the way down. We appreciated the opportunity to test the model and provide feedback, helping us prepare for a new frontier where we can increasingly rely on these tools to take on bolder initiatives.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Plaid</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Aditya Gupta, Staff Software Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Across our evaluation sets, our judges preferred Claude Fable 5.1’s answers roughly 2-to-1 over Fable 5 on everyday knowledge questions, high-intent research, and drafting and artifact work, all with improved response grounding over Fable. These results have made Fable 5.1 our go-to recommendation wherever Fable 5 was previously the choice.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Glean</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Nilesh Dalvi, Engineering Lead</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“On the hardest problems we work on, Claude Fable 5.1 separates strongly from any other model we’ve tried. On a grand challenge-tier problem we‘ve used as a testbed for 18 months, it actually produced material progress. Rather than being trapped in stamp collecting, it made clear white-space connections I have yet to see elsewhere. It also optimized a compute kernel that Fable 5 had tapped out on by about 35%.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">iGent</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Sean Ward, Co-founder and CEO</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“On our hardest browser-agent benchmark, Claude Fable 5.1 completed 82% of tasks in about 10 minutes each, against 74% for Opus 5 and 57% for Fable 5, while using fewer tokens than either. It feels stronger than Fable 5 in every dimension we test. It also did exactly the right amount of work: never crossed a critical stop point across hundreds of measured tasks.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Browserbase</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Miguel Gonzalez, Technical Lead</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“On our internal Finance benchmark, Claude Fable 5.1 matches Fable 5 on accuracy while using 20% fewer tokens. We also saw big gains in slide generation, with Fable 5.1 showing improvements in explaining complex data in simpler English and translating it into more banker-grade visuals.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Rogo</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Alex Wang, Applied AI</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Claude Fable 5.1 is more comfortable with long, unattended work than Fable 5. I’ve had workflows run for a long stretch without losing the plot: it keeps its own records, reprioritizes as things change, and picks up where it left off.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Shopify</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Ben Lafferty, Senior Staff Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Compared to Fable 5, Claude Fable 5.1 was a massive improvement on RedlineBench, our contract redlining benchmark, improving from 47.9 to 57.0. Most of the gain came on first-turn quality, where it doubled the previous score, with substantial gains on counterparty acceptance as well. Its edits were also more concise, with smaller changes on average to the documents.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Crosby</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Raymond Lin, Member of Technical Staff</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Claude Fable 5.1 is a leading model for our incident investigation evals, which use real production incidents to assess how effectively our agent, Bits Investigation, can produce root cause analyses. We evaluate our agent’s output against root causes identified by our engineers. In these evaluations, it has demonstrated stronger reasoning than Opus 5 and has successfully diagnosed the most complex production incidents we’ve tested.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Datadog</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Daniel Shan, Staff Engineer</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“Claude Fable 5.1 is the most capable model we’ve run on CursorBench 3.2, scoring 73.4% at max effort. We found it especially skilled at verifying its own work, allowing it to take on difficult coding tasks from start to finish.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">SpaceXAI</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Sualeh Asif, Director of ML</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__slide" data-slide="far-next" aria-hidden="true"><article class="TestimonialCarousel-module-scss-module__o0jJtW__card"><div class="TestimonialCarousel-module-scss-module__o0jJtW__body"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Quote</span><blockquote class="TestimonialCarousel-module-scss-module__o0jJtW__quote"><p class="body-2 serif">“We evaluate models and systems on real-world investor workflows. On FrontierFinance, our latest finance benchmark, Claude Fable 5.1 shows a clear gain over Fable 5, with a 55.9% rubric score compared to 49.2%. The gain comes from its ability to dig harder into grounded, authoritative sources: on one earnings question, it went straight to the call transcript and captured the exact figures management cited, where other models leaned on secondary coverage.”</p></blockquote></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__split"><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Company</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Samaya</span></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__cell"><span class="TestimonialCarousel-module-scss-module__o0jJtW__label">Author</span><span class="TestimonialCarousel-module-scss-module__o0jJtW__value">Yuhao Zhang, Research Lead</span></div></div></article></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__veil TestimonialCarousel-module-scss-module__o0jJtW__veil-left" aria-hidden="true"></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__veil TestimonialCarousel-module-scss-module__o0jJtW__veil-right" aria-hidden="true"></div></div><div class="TestimonialCarousel-module-scss-module__o0jJtW__strip-controls body-3"><div class="TestimonialCarousel-module-scss-module__o0jJtW__counter"><span>01</span> / <!-- -->22</div><div class="TestimonialCarousel-module-scss-module__o0jJtW__arrow-controls"><button type="button" class="TestimonialCarousel-module-scss-module__o0jJtW__arrow-button TestimonialCarousel-module-scss-module__o0jJtW__arrow-prev" disabled="" aria-label="Previous quote"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 8 5"><path d="M7.3016 0.231808C7.44932 0.0678162 7.70306 0.0546398 7.86724 0.20212C8.03137 0.349888 8.04461 0.603568 7.89692 0.767766L4.29684 4.76791L4.23434 4.82417C4.16662 4.87328 4.08425 4.89995 3.99918 4.89995C3.88588 4.89989 3.77733 4.85213 3.70152 4.76791L0.10144 0.767766L0.0537825 0.702139C-0.040206 0.541753 -0.0124254 0.331356 0.131128 0.20212C0.274775 0.0728844 0.486972 0.0674593 0.636608 0.1779L0.696765 0.231808L3.99918 3.90148L7.3016 0.231808Z" fill="#141413"></path></svg></button><button type="button" class="TestimonialCarousel-module-scss-module__o0jJtW__arrow-button TestimonialCarousel-module-scss-module__o0jJtW__arrow-next" aria-label="Next quote"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 8 5"><path d="M7.3016 0.231808C7.44932 0.0678162 7.70306 0.0546398 7.86724 0.20212C8.03137 0.349888 8.04461 0.603568 7.89692 0.767766L4.29684 4.76791L4.23434 4.82417C4.16662 4.87328 4.08425 4.89995 3.99918 4.89995C3.88588 4.89989 3.77733 4.85213 3.70152 4.76791L0.10144 0.767766L0.0537825 0.702139C-0.040206 0.541753 -0.0124254 0.331356 0.131128 0.20212C0.274775 0.0728844 0.486972 0.0674593 0.636608 0.1779L0.696765 0.231808L3.99918 3.90148L7.3016 0.231808Z" fill="#141413"></path></svg></button></div></div></section></div><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="scientific-research">Scientific research</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">We tested the scientific research capabilities of Claude Fable 5.1 and Claude Mythos 5.1 across a wide range of domains. What we found—which includes the early examples we share below—adds to the evidence that AI models will soon make important contributions to scientific discovery.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Molecular design<em>.</em> </strong>Many modern medicines work by binding to targets within the body to block, activate, or deliver something to them. High-affinity binders are necessary for drugs to work at lower doses; designing one is the first step in the development process for many common drug modalities. To see how well Claude Mythos 5.1 could do at this task, we gave the model access to open-source protein design and folding tools and sent its designs to two external organizations for experimental validation. Mythos 5.1 proved able to design very high-affinity binders. On three targets, <sup class="caption Body-module-scss-module__z40yvW__sup">[3]</sup> its binding affinities were 10 times higher than the best designs submitted to <a href="https://proteinbase.com/competitions" target="_blank" rel="noopener noreferrer">Adaptyv Bio’s protein design competitions</a>. Its hit rate (that is, the number of designs that were viable binders) was the strongest we’ve measured to date: it reached nearly 50% across 12 targets. (Hit rates of 10–15% are typical in protein design today.)</p><div class="Body-module-scss-module__z40yvW__media-column"><figure class="LaunchVideoSwitcher-module-scss-module__pNlP9a__video-switcher"><div class="LaunchVideoSwitcher-module-scss-module__pNlP9a__frame"><video class="LaunchVideoSwitcher-module-scss-module__pNlP9a__poster" src="https://cdn.sanity.io/files/4zrzovbb/website/dd1483b60a862b1acb3ed4359631ec99653a84c7.webm" poster="https://cdn.sanity.io/images/4zrzovbb/website/4b8acf7ac952566415849134b65025b89a197bf6-1920x1080.jpg" autoPlay="" loop="" muted="" playsInline=""></video><button type="button" class="LaunchVideoSwitcher-module-scss-module__pNlP9a__play" aria-label="Play: Claude-designed protein binders (orange) for each of 12 targets (grey). Every design in the video was confirmed to bind in the lab. Structures shown are ESMFold2 predictions."><svg viewBox="0 0 24 24" width="24" height="24" aria-hidden="true"><path d="M7 4 L20 12 L7 20 Z"></path></svg></button></div><figcaption class="LaunchVideoSwitcher-module-scss-module__pNlP9a__caption">Claude-designed protein binders (orange) for each of 12 targets (grey). Every design in the video was confirmed to bind in the lab. Structures shown are ESMFold2 predictions.</figcaption></figure></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Computational analysis and modeling</strong>. Claude Fable 5.1 trained a neural network to create a new, high-resolution elevation map of a third of the planet Venus. Its work was based on radar images taken by NASA’s Magellan mission more than 30 years ago and a <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012EO120002" target="_blank" rel="noopener noreferrer">map</a> that already existed for one-fifth of the planet. Claude’s new map now reveals details down to two to three kilometers, rather than 10 to 20, and shows heights up to 25% more accurately than before.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">We’re <a href="https://zenodo.org/records/22164484" target="_blank" rel="noopener noreferrer">releasing this map</a> under a Creative Commons license in advance of upcoming NASA VERITAS and ESA EnVision missions, in hopes that it might help them determine which geologic features to target for future observation.</p><div class="Body-module-scss-module__z40yvW__media-column Body-module-scss-module__z40yvW__full-page"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-gallery-wrapper"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-gallery-container"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-gallery"><figure class="MediaGalleryView-module-scss-module__6QZEyW__media-item"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-content"><img alt="Radar image (Magellan): bright cone, radian lava flows" loading="lazy" width="800" height="800" decoding="async" data-nimg="1" class="MediaGalleryView-module-scss-module__6QZEyW__media-image" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F4d8f06a743ecfc6ec87ccde0b1964e48175a141e-800x800.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F4d8f06a743ecfc6ec87ccde0b1964e48175a141e-800x800.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F4d8f06a743ecfc6ec87ccde0b1964e48175a141e-800x800.png&amp;w=1920&amp;q=75"/></div><figcaption class="caption">Magellan radar</figcaption></figure><figure class="MediaGalleryView-module-scss-module__6QZEyW__media-item"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-content"><img alt="Altimetry 10-20km footprint" loading="lazy" width="800" height="800" decoding="async" data-nimg="1" class="MediaGalleryView-module-scss-module__6QZEyW__media-image" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fe374c070fc1a84872dcb1343b5bb0ed540ca3770-800x800.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fe374c070fc1a84872dcb1343b5bb0ed540ca3770-800x800.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2Fe374c070fc1a84872dcb1343b5bb0ed540ca3770-800x800.png&amp;w=1920&amp;q=75"/></div><figcaption class="caption">Altimetry 10-20km footprint</figcaption></figure><figure class="MediaGalleryView-module-scss-module__6QZEyW__media-item"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-content"><img alt="New DEM (300m) a volcano 15km across" loading="lazy" width="800" height="800" decoding="async" data-nimg="1" class="MediaGalleryView-module-scss-module__6QZEyW__media-image" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F3dd626b47b88feb72082646cdea87944d49a3c7f-800x800.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F3dd626b47b88feb72082646cdea87944d49a3c7f-800x800.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F3dd626b47b88feb72082646cdea87944d49a3c7f-800x800.png&amp;w=1920&amp;q=75"/></div><figcaption class="caption">New DEM (300m) a volcano 15km across</figcaption></figure><figure class="MediaGalleryView-module-scss-module__6QZEyW__media-item"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-content"><div class="MediaGalleryView-module-scss-module__6QZEyW__media-video"><video autoPlay="" controls="" playsInline="" muted="" loop="" src="https://cdn.sanity.io/files/4zrzovbb/website/3a8b31b9b3525f607fc1da0c878b2ee1ea56e54c.mp4"></video></div></div><figcaption class="caption">A small shield volcano on Venus</figcaption></figure></div></div></div></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Computational biology<em>.</em></strong> In computational biology, it’s common to run task-specific machine learning models on GPUs. The speed of these models is therefore a bottleneck to research progress. Mythos 5.1 provided one solution to this problem: by writing custom GPU kernels and caching their intermediate results, it sped up seven open-source deep learning models by up to 2.5 times (with identical outputs).</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">The benefits of such speed-ups accumulate quickly. In any given experiment, biologists might run these models thousands of times (for example, testing every possible mutation near every human gene). On analyses like these, the optimized models cut estimated GPU costs by 30–60%. This kind of optimization would normally take a team of performance engineers weeks, and is often unaffordable for academic labs. Mythos 5.1 was able to do it in just days, using the publicly available source code alone. We plan to open-source these optimizations soon.</p><div class="Body-module-scss-module__z40yvW__media-column"><section class="ViewSwitcher-module-scss-module__kN30kq__root"><div class="ViewSwitcher-module-scss-module__kN30kq__tabs" role="tablist" aria-label="Views"><button type="button" role="tab" id="_R_1meqnpfibrb_-tab-0" aria-selected="true" aria-controls="_R_1meqnpfibrb_-panel-0" tabindex="0" class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__tabActive">Inference speedup</button><button type="button" role="tab" id="_R_1meqnpfibrb_-tab-1" aria-selected="false" aria-controls="_R_1meqnpfibrb_-panel-1" tabindex="-1" class="ViewSwitcher-module-scss-module__kN30kq__tab">Estimated cost savings on genome-wide analyses</button><div class="ViewSwitcher-module-scss-module__kN30kq__more" hidden=""><button type="button" class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__moreButton" aria-label="More views" aria-haspopup="menu" aria-expanded="false"><svg class="ViewSwitcher-module-scss-module__kN30kq__moreIcon" viewBox="0 0 12 12" aria-hidden="true"><line x1="1" y1="6" x2="11" y2="6"></line><line class="ViewSwitcher-module-scss-module__kN30kq__moreIconVertical" x1="6" y1="1" x2="6" y2="11"></line></svg></button><div class="ViewSwitcher-module-scss-module__kN30kq__menu" role="menu" hidden=""></div></div><div class="ViewSwitcher-module-scss-module__kN30kq__measure" aria-hidden="true"><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Inference speedup</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab">Estimated cost savings on genome-wide analyses</span><span class="ViewSwitcher-module-scss-module__kN30kq__tab ViewSwitcher-module-scss-module__kN30kq__moreButton"><svg class="ViewSwitcher-module-scss-module__kN30kq__moreIcon" viewBox="0 0 12 12" aria-hidden="true"><line x1="1" y1="6" x2="11" y2="6"></line><line 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y2="464"></line><text class="chart-module-scss-module__3ia3wq__tick" y="474" dy="1em" text-anchor="middle">0</text></g><g transform="translate(428, 0)"><line class="chart-module-scss-module__3ia3wq__grid" y1="30" y2="464"></line><text class="chart-module-scss-module__3ia3wq__tick" y="474" dy="1em" text-anchor="middle">1</text></g><g transform="translate(688, 0)"><line class="chart-module-scss-module__3ia3wq__grid" y1="30" y2="464"></line><text class="chart-module-scss-module__3ia3wq__tick" y="474" dy="1em" text-anchor="middle">2</text></g><g transform="translate(948, 0)"><line class="chart-module-scss-module__3ia3wq__grid" y1="30" y2="464"></line><text class="chart-module-scss-module__3ia3wq__tick" y="474" dy="1em" text-anchor="middle">3</text></g><text class="chart-module-scss-module__3ia3wq__axisLabel" x="557.95" y="504" dy="1em" text-anchor="middle"><tspan x="557.95">Speedup on an NVIDIA H100 (×)</tspan></text></g><g><line class="chart-module-scss-module__3ia3wq__reference" x1="428" x2="428" y1="30" y2="464"></line><text class="chart-module-scss-module__3ia3wq__referenceLabel" x="428" y="22" text-anchor="middle">Original implementation</text></g><g><line class="chart-module-scss-module__3ia3wq__axisLine" x1="167.90000000000003" x2="167.90000000000003" y1="30" y2="464"></line><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="157.90000000000003" y="49.5" dy="0.35em" text-anchor="end"><tspan class="chart-module-scss-module__3ia3wq__emphasis">ChromBPNet</tspan> (6M)</text><text class="chart-module-scss-module__3ia3wq__categoryNote" x="157.90000000000003" y="72.5" dy="0.35em" text-anchor="end"><tspan x="157.90000000000003">2.1-kb DNA sequence</tspan></text></g><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="157.90000000000003" y="111.5" dy="0.35em" text-anchor="end"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Flashzoi</tspan> (200M)</text><text class="chart-module-scss-module__3ia3wq__categoryNote" x="157.90000000000003" y="134.5" dy="0.35em" text-anchor="end"><tspan x="157.90000000000003">524-kb DNA sequence</tspan></text></g><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="157.90000000000003" y="173.5" dy="0.35em" text-anchor="end"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Enformer</tspan> (250M)</text><text class="chart-module-scss-module__3ia3wq__categoryNote" x="157.90000000000003" y="196.5" dy="0.35em" text-anchor="end"><tspan x="157.90000000000003">196-kb DNA sequence</tspan></text></g><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="157.90000000000003" y="235.5" dy="0.35em" text-anchor="end"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Profluent-E1</tspan> (600M)</text><text class="chart-module-scss-module__3ia3wq__categoryNote" x="157.90000000000003" y="258.5" dy="0.35em" text-anchor="end"><tspan x="157.90000000000003">1,024-amino-acid protein</tspan></text></g><g><text 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text-anchor="start">1.8×</text></g></g><g transform="translate(0, 154)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="168" y="18" width="364" height="26" fill="var(--chart-heather)" tabindex="0" aria-label="Optimized · Enformer (250M): 1.4×"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="540" y="31" dy="0.35em" text-anchor="start">1.4×</text></g></g><g transform="translate(0, 216)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="168" y="18" width="416" height="26" fill="var(--chart-heather)" tabindex="0" aria-label="Optimized · Profluent-E1 (600M): 1.6×"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="592" y="31" dy="0.35em" text-anchor="start">1.6×</text></g></g><g transform="translate(0, 278)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark 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class="chart-module-scss-module__3ia3wq__valueLabel" x="540" y="31" dy="0.35em" text-anchor="start">1.4×</text></g></g></g></g></svg></div></div><p class="chart-module-scss-module__3ia3wq__footnote">Inference speedup for seven open-source protein and genomics models on an NVIDIA H100</p></figure></div></div><div id="_R_1meqnpfibrb_-panel-1" role="tabpanel" aria-labelledby="_R_1meqnpfibrb_-tab-1" hidden="" class="ViewSwitcher-module-scss-module__kN30kq__panel" data-panel="true"><div class="Body-module-scss-module__z40yvW__media-column"><figure class="chart-module-scss-module__3ia3wq__frame"><div class="chart-module-scss-module__3ia3wq__panel"><figcaption class="chart-module-scss-module__3ia3wq__caption"><span class="chart-module-scss-module__3ia3wq__title">Estimated cost savings on genome-wide analyses</span></figcaption><ul class="chart-module-scss-module__3ia3wq__legend chart-module-scss-module__3ia3wq__legendRow" aria-label="Series"><li 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Evo 2 40B saves more on a whole job (2.3x) than per forward (1.4x) because some of its optimizations only pay off across many sequences.</p></figure></div></div></div></section></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">As our models’ scientific capabilities improve, our investment in scientific progress is also growing. Last week, we previewed the <a href="https://www.anthropic.com/news/model-hardware-standard-research-preview" target="_blank" rel="noopener noreferrer">Model Hardware Standard</a>, which allows Claude to directly and safely operate laboratory equipment. We’ve also recently <a href="https://www.anthropic.com/news/expanding-support-for-scientists" target="_blank" rel="noopener noreferrer">expanded our support for scientists</a> through our <a href="https://www.anthropic.com/news/ai-for-science-program" target="_blank" rel="noopener noreferrer">AI for Science program</a>, which provides free credits to researchers working on high-impact scientific projects, and we are offering steeply discounted usage through our new <a href="https://claude.com/programs/team-plan-for-scientists" target="_blank" rel="noopener noreferrer">Claude Team plan for scientists</a>.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="safety-security-and-alignment">Safety, security, and alignment</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">AI models’ agentic capabilities have become much more powerful over the past two years. But as we’ve <a href="https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals" target="_blank" rel="noopener noreferrer">documented</a>, greater autonomy comes with new risks. Work on safety, security, and alignment needs to advance at the same pace as AI capabilities. Yesterday, we <a href="https://www.anthropic.com/news/improving-alignment-security-efforts" target="_blank" rel="noopener noreferrer">published a report</a> describing how we are improving our own alignment and security efforts</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Prior to releasing Claude Fable 5.1 and Claude Mythos 5.1, we (and, in some cases, external researchers) subjected the models to extensive testing for risks across many areas. We describe these efforts in full in our <a href="https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card" target="_blank" rel="noopener noreferrer">System Card</a>; below is a brief summary.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Chemical and biological risks</strong>. We tested the extent to which Claude Mythos 5.1 could help create chemical or biological weapons. This involved expert red-teaming, automated evaluations, and a tabletop exercise that paired PhD-level biologists with AI experts, testing whether the models could match human specialists’ performance. Mythos 5.1’s capabilities are greater than those of Mythos 5. However, our evaluations indicate that it still falls short of the next risk tier defined in our <a href="https://www.anthropic.com/responsible-scaling-policy" target="_blank" rel="noopener noreferrer">Responsible Scaling Policy</a>. We are therefore deploying Mythos 5.1 with the <a href="https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards" target="_blank" rel="noopener noreferrer">same safeguards</a> that we applied to Mythos 5, which restrict access to research biology capabilities.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Cyber risks</strong>. We ran a suite of evaluations to assess the cyber capabilities of Claude Mythos 5.1 (with cybersecurity safeguards off). Overall, the model demonstrates the strongest cyber capabilities of any model we’ve released, though it still falls within the lower category of risk in our <a href="https://www.anthropic.com/news/compliance-framework-SB53" target="_blank" rel="noopener noreferrer">Frontier Compliance Framework</a>. We also performed extensive stress-testing of our cybersecurity safeguards for Fable 5.1: in addition to our own dynamic evaluation of their robustness, we commissioned external testing from two organizations, along with automated testing by <a href="https://www.grayswan.ai/" target="_blank" rel="noopener noreferrer">Gray Swan</a>. As with Fable 5 and Opus 5, we have not found evidence of a <a href="https://www.anthropic.com/news/fable-safeguards-jailbreak-framework" target="_blank" rel="noopener noreferrer">critical-severity jailbreak</a> for these safeguards.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Agentic safety</strong>. We ran evaluations of how Claude Mythos 5.1 responds to malicious requests and prompt injections (adversarial instructions hidden within content processed by AI models). It refused malicious agentic coding and computer use requests at a comparable rate to Mythos 5, Sonnet 5, and Opus 5, and it is our most robust model to date on an external <a href="https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card" target="_blank" rel="noopener noreferrer">prompt injection benchmark</a>.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Alignment<em>.</em></strong> We tested the model’s behavior through static and interactive behavioral evaluations, analyses of its internal thinking using <a href="https://www.anthropic.com/research/natural-language-autoencoders" target="_blank" rel="noopener noreferrer">natural language autoencoders</a>, misalignment-related capability evaluations, a review of our training data, and analyses of our internal pilot use. We also received reports from external testing.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Our automated behavioral audit found that Claude Mythos 5.1 is better aligned across most metrics than its predecessor, Mythos 5. The model is significantly less likely than Mythos 5 to try to access resources outside of its test environment when assigned an otherwise impossible task. It is also less likely than Mythos 5 to use motivated reasoning to justify its actions (for instance, by reasoning that the situation is a simulation or evaluation), and it is less likely to ignore explicit constraints in pursuit of users’ goals. From our review of its training data, Mythos 5.1 both attempts reward hacking (or cheating), and succeeds at it, at a lower overall rate than Mythos 5.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Though generally our alignment evaluations showed improvements, our testing found the model can still sometimes bypass approvals and auto-mode classifiers (as we discuss in more detail in our <a href="https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card" target="_blank" rel="noopener noreferrer">System Card</a>). There are also limitations to the coverage provided by our alignment assessment. Currently, our automated behavioral audit provides less visibility into very long-context work and multi-agent settings. We also have less coverage of impossible tasks (which can elicit more abnormal and misaligned behavior) than we’d like, although we’ve recently made improvements in this domain and are working hard to continue doing so.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">We have also improved our safeguards so that they allow our models to be more useful without compromising on safety. We describe these changes below.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Automated safeguards for enterprises<em>.</em></strong> <a href="https://www.anthropic.com/news/enterprise-frontier-safeguards" target="_blank" rel="noopener noreferrer">Enterprise Frontier Safeguards</a> (EFS) allows us to detect and respond to misuse of our models while still providing our enterprise customers the privacy of a zero data retention agreement. With EFS, customers store their data on their own cloud infrastructure, rather than on Anthropic’s systems; any human review is, by default, done by the customer themselves, rather than Anthropic. We developed EFS in close collaboration with more than 100 customers across industries like financial services, healthcare, manufacturing, telecom, law, retail, and the public sector, and with our cloud partners at Amazon Web Services, Google Cloud, and Microsoft Azure.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">EFS will be supported on Claude Code, Claude Enterprise, the Claude Platform, Amazon Bedrock, Claude Platform on AWS, Google’s Agent Platform, and Microsoft Foundry. It’s rolling out in phases, starting this fall. As noted above, customers who are eligible for EFS can use Fable 5.1 (and Fable 5) with zero data retention until EFS is ready. You can read more about EFS <a href="https://www.anthropic.com/news/enterprise-frontier-safeguards" target="_blank" rel="noopener noreferrer">here</a>; to request access, please complete <a href="https://claude.com/form/enterprise-frontier-safeguards">this form</a>.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>More precise safeguards for biology and cybersecurity<em>. </em></strong>In the past few months, we’ve made progress in making our safeguards for Fable 5.1 more precise: ensuring that they’re less likely to flag benign content (like queries about medical issues or cyberdefenders using the model to make their systems safer), but still ensuring they provide robust protection against genuine threats.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">As we <a href="https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards" target="_blank" rel="noopener noreferrer">recently shared</a>, our latest biology safeguards for Fable 5.1 and Fable 5 fire 85% less often for benign requests related to elementary biology and medical questions (relative to those that launched with Fable 5). However, queries related to research and development in the life sciences will still be directed to our Opus models. We’re making the model’s life sciences capabilities available to professionals through an access program for Claude Mythos 5.1 that we’ve developed in partnership with the US government, which we discuss below.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">With Fable 5.1, we’re updating our cybersecurity safeguards to be more precise. We’re also now allowing Fable 5.1 to be used for identifying software vulnerabilities—that is, to conduct the kind of defensive work that improves software security. As a result of these changes, Claude Code users can expect an average of around 60% fewer interventions per session from our cyber safeguards, relative to the previous safeguards on Fable 5. Our safeguards do, however, still redirect several kinds of dual-use cybersecurity tasks (tasks that might have helpful <em>or</em> harmful applications) to our Opus models. This includes penetration testing, exploit generation, and binary-based vulnerability scanning.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><strong>Anti-distillation mechanisms<em>.</em></strong> Distillation is a method used to extract the capabilities of advanced models. It is often employed on an industrial scale, using thousands of fake accounts. Distillation is a safety risk, since the distilled capabilities can subsequently be released without adequate safeguards. Fable 5.1 comes with strengthened mechanisms to make distillation attacks harder. For example, it is no longer possible for new API accounts (those created from today onwards) to manually edit Claude’s prior context in a multi-turn conversation while preserving the transcript of Claude’s prior thinking. This closes off a common, publicly documented distillation technique, which allowed distillers to illicitly extract Claude’s thinking. We’re rolling out the change gradually, to minimize disruption: existing accounts are not currently affected by this change, though it will apply to all users with future model releases. A small number of customers’ custom integrations will then be affected. Our <a href="https://support.claude.com/en/articles/16761192" target="_blank" rel="noopener noreferrer">Help Center article</a> explains more about this change and the adjustments that developers can make.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="mythos">Trusted access for Claude Mythos 5.1</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Claude Mythos 5.1 is identical to Fable 5.1, but it offers more permissive safeguards for vetted individuals and organizations whose work is affected by the cybersecurity and life sciences restrictions outlined above. It will be available through two trusted access programs:</p><ul class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text"><li><strong>Cyber Verification Program:</strong> The CVP currently provides access to certain Opus- and Sonnet-class models with reduced cyber safeguards for defensive security work. In the near future, this program will also include access to Claude Mythos-class models. <a href="https://portal.anthropic.com/programs/cvp" target="_blank" rel="noopener noreferrer">Apply to join the CVP here</a>.</li><li><strong>Life Sciences Verification Program:</strong> The LSVP is designed so that life sciences professionals can use Claude Mythos 5.1 with safeguards designed for professional research and development activities (while all other safeguards remain in place). In partnership with the US government, we have enrolled our first participants, and we plan to expand access to this program to the broader life sciences community.</li></ul><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">In addition to these trusted access programs, <a href="https://claude.com/product/claude-security" target="_blank" rel="noopener noreferrer">Claude Security</a>, our product that scans codebases for vulnerabilities and suggests patches for human review, is now also powered by Claude Mythos 5.1.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="compliance-with-the-eu-ai-act">Compliance with the EU AI Act</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">In July 2026, Anthropic (along with <a href="https://digital-strategy.ec.europa.eu/en/news/strong-backing-code-practice-transparency-ai-generated-content" target="_blank" rel="noopener noreferrer">190 other signatories</a>, including several other major AI model providers) signed the EU AI Act’s Code of Practice on Transparency of AI-Generated Content.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">This required us to add a watermark—a numerical way of determining the likelihood that Claude was involved in writing a piece of text—to the outputs of models released after August 2, 2026. As we <a href="https://www.anthropic.com/news/claude-text-watermark" target="_blank" rel="noopener noreferrer">recently explained</a>, this watermark is invisible to anyone who does not have the detection API. It has no practical impact on the quality or content of Claude’s outputs and contains no information about the user, their organization, or their conversations with Claude.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">The Act also required us to provide a way for users to tell whether a text likely contains the watermark. We are thus rolling out a detection API in private preview. It is currently available to eligible organizations (such as regulators, law enforcement, media, fact-checkers, independent researchers, educational organizations, and EU civil society groups) as required under EU law. It is also available for enterprises that are similarly obligated to verify watermarking for their own compliance with the Act. We plan to expand access to the detection API over time. You can register interest in access <a href="https://forms.gle/9tGA33hPJJwtHsMk9" target="_blank" rel="noopener noreferrer">here</a>.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 serif post-heading" id="cost-and-availability">Cost and availability</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Claude Fable 5.1 is available today on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. Developers can get started with <code class="InlineCodeBlock-module-scss-module__nsPAba__code">claude-fable-5-1</code> on the Claude API.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">As mentioned above, we have reduced the price of Fable 5.1’s cache reads (where the model reuses context it has already processed) wherever usage is billed by token, such as on our API. Cache reads now cost 75% less, or $0.25 per million tokens.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">This change leads to a substantial reduction in the overall cost of running the model. For typical workloads, costs are reduced by around 25% relative to Fable 5. For complex coding and highly agentic tasks, the savings could be up to around 45%. The graph below illustrates why this change makes such a big difference:</p><div class="Body-module-scss-module__z40yvW__media-column"><figure class="chart-module-scss-module__3ia3wq__frame"><div class="chart-module-scss-module__3ia3wq__panel"><figcaption class="chart-module-scss-module__3ia3wq__caption"><span class="chart-module-scss-module__3ia3wq__title">Indexed cost of Fable usage</span></figcaption><ul class="chart-module-scss-module__3ia3wq__legend chart-module-scss-module__3ia3wq__legendRow" aria-label="Series"><li class="chart-module-scss-module__3ia3wq__legendItem"><svg class="chart-module-scss-module__3ia3wq__swatch" viewBox="0 0 14 14" aria-hidden="true"><defs><pattern id="_R_3oeqnpfibrb_-legend-0-hatch-paper" width="6" height="6" patternUnits="userSpaceOnUse"><rect width="6" height="6" fill="var(--chart-paper)"></rect><path d="M-1 1 l2 -2 M0 6 l6 -6 M5 7 l2 -2" stroke="var(--chart-ink)" stroke-width="1"></path></pattern><pattern 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y="14" text-anchor="middle"><tspan x="243.5">Typical workload</tspan></text><g><g><g transform="translate(0, 519)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">0</text></g><g transform="translate(0, 409)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">25</text></g><g transform="translate(0, 299)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">50</text></g><g transform="translate(0, 190)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">75</text></g><g transform="translate(0, 80)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">100</text></g><line class="chart-module-scss-module__3ia3wq__axisLine" x1="43" x2="43" y1="36" y2="519"></line><text class="chart-module-scss-module__3ia3wq__axisLabel" transform="translate(2, 277.5) rotate(-90)" dy="1em" text-anchor="middle">Indexed cost (Fable 5 = 100)</text></g><g><line class="chart-module-scss-module__3ia3wq__axisLine" x1="43" x2="444" y1="519" y2="519"></line><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="143.25" y="523" dy="1em" text-anchor="middle"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Fable 5</tspan></text></g><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="343.75" y="523" dy="1em" text-anchor="middle"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Fable 5.1</tspan></text></g></g><g transform="translate(43, 0)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="343" width="103.2174" height="176" fill="url(#_R_3oeqnpfibrb_-hatch-cloud)" tabindex="0" aria-label="Cache reads · Fable 5: 40.0"></rect><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="80" width="103.2174" height="263" fill="var(--chart-cloud)" tabindex="0" aria-label="All other tokens · Fable 5: 60.0"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="100.25" y="72" text-anchor="middle"><tspan x="100.25">100</tspan></text></g></g><g transform="translate(243.5, 0)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="471" width="103.2174" height="48" fill="url(#_R_3oeqnpfibrb_-hatch-matcha)" tabindex="0" aria-label="Cache reads · Fable 5.1: 11.0"></rect><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="190" width="103.2174" height="281" fill="var(--chart-matcha)" tabindex="0" aria-label="All other tokens · Fable 5.1: 64.0"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="100.25" y="182" text-anchor="middle"><tspan x="100.25">75 (~25% less)</tspan></text></g></g></g></g><g transform="translate(504, 0)"><text class="chart-module-scss-module__3ia3wq__panelTitle" x="243.5" y="14" text-anchor="middle"><tspan x="243.5">Highly agentic workload</tspan></text><g><g><g transform="translate(0, 519)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">0</text></g><g transform="translate(0, 409)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">25</text></g><g transform="translate(0, 299)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">50</text></g><g transform="translate(0, 190)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">75</text></g><g transform="translate(0, 80)"><line class="chart-module-scss-module__3ia3wq__grid" x1="43" x2="444"></line><text class="chart-module-scss-module__3ia3wq__tick" x="39" dy="0.35em" text-anchor="end">100</text></g><line class="chart-module-scss-module__3ia3wq__axisLine" x1="43" x2="43" y1="36" y2="519"></line><text class="chart-module-scss-module__3ia3wq__axisLabel" transform="translate(2, 277.5) rotate(-90)" dy="1em" text-anchor="middle">Indexed cost (Fable 5 = 100)</text></g><g><line class="chart-module-scss-module__3ia3wq__axisLine" x1="43" x2="444" y1="519" y2="519"></line><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="143.25" y="523" dy="1em" text-anchor="middle"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Fable 5</tspan></text></g><g><text class="chart-module-scss-module__3ia3wq__categoryLabel" x="343.75" y="523" dy="1em" text-anchor="middle"><tspan class="chart-module-scss-module__3ia3wq__emphasis">Fable 5.1</tspan></text></g></g><g transform="translate(43, 0)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="234" width="103.2174" height="285" fill="url(#_R_3oeqnpfibrb_-hatch-cloud)" tabindex="0" aria-label="Cache reads · Fable 5: 65.0"></rect><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="80" width="103.2174" height="154" fill="var(--chart-cloud)" tabindex="0" aria-label="All other tokens · Fable 5: 35.0"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="100.25" y="72" text-anchor="middle"><tspan x="100.25">100</tspan></text></g></g><g transform="translate(243.5, 0)"><g style="--chart-delay:0s"><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="444" width="103.2174" height="75" fill="url(#_R_3oeqnpfibrb_-hatch-matcha)" tabindex="0" aria-label="Cache reads · Fable 5.1: 17.0"></rect><rect class="chart-module-scss-module__3ia3wq__mark chart-module-scss-module__3ia3wq__bar" x="48.6413" y="278" width="103.2174" height="166" fill="var(--chart-matcha)" tabindex="0" aria-label="All other tokens · Fable 5.1: 38.0"></rect><text class="chart-module-scss-module__3ia3wq__valueLabel" x="100.25" y="270" text-anchor="middle"><tspan x="100.25">55 (~45% less)</tspan></text></g></g></g></g></svg></div></div><p class="chart-module-scss-module__3ia3wq__footnote">Indexed cost of running the same workloads on Fable 5 and Fable 5.1, at usage-based pricing measured at default effort over four weeks of actual usage in August 2026. Typical workload covers Fable usage across Claude Enterprise, Claude Code, and the API. Highly agentic workload covers context-heavy, tool-heavy work, where cache reads make up most of the cost.</p></figure></div><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Fable 5.1’s pricing is otherwise the same as Fable 5’s: $10 per million input tokens and $50 per million output tokens. In parallel, we’re continuing our work to bring many of the improvements of Fable 5.1 to the rest of our model family.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">As discussed above, Claude Mythos 5.1 is available to vetted cyberdefenders and life scientists. Currently, it is only available to a set of US organizations, though we’re coordinating with the US government to expand access to a broader set of domestic and international partners as quickly as possible. To register interest in access to Claude Mythos 5.1 for cyberdefense through the CVP, <a href="https://portal.anthropic.com/programs/cvp" target="_blank" rel="noopener noreferrer">head here</a>.</p></div><section id="get-started" class="LaunchGetStarted-module-scss-module__qvEXQG__get-started"><h2 class="headline-5 serif LaunchGetStarted-module-scss-module__qvEXQG__heading">Get started</h2><div class="LaunchGetStarted-module-scss-module__qvEXQG__actions"><a href="https://claude.ai/" class="Button-module-scss-module__f9ZZrG__button Button-module-scss-module__f9ZZrG__medium Button-module-scss-module__f9ZZrG__primary" target="_blank" rel="noopener" referrerPolicy="no-referrer-when-downgrade"><span class="body-3">Try Claude</span></a><a href="https://platform.claude.com/" class="Button-module-scss-module__f9ZZrG__button Button-module-scss-module__f9ZZrG__medium Button-module-scss-module__f9ZZrG__secondary" target="_blank" rel="noopener"><span class="body-3">Start building</span></a></div></section><section id="footnotes" class="LaunchFootnotes-module-scss-module___FS4_a__footnotes"><h2 class="headline-5 serif LaunchFootnotes-module-scss-module___FS4_a__heading">Footnotes</h2><div class="LaunchFootnotes-module-scss-module___FS4_a__columns"><p class="LaunchFootnotes-module-scss-module___FS4_a__note"><sup class="LaunchFootnotes-module-scss-module___FS4_a__label">1</sup> <strong>Terminal-Bench-Science 0.1:</strong> The standard error is ±3.5–4.5 pts per model. The public leaderboard (3 trials/task, Claude Code harness) reports Claude Opus 5 at 30.0% and Claude Fable 5 at 21.4%; our setup reproduces them at 29.0% and 24.7%, respectively, both within noise.</p><p class="LaunchFootnotes-module-scss-module___FS4_a__note"><sup class="LaunchFootnotes-module-scss-module___FS4_a__label">2</sup> <strong>OSWorld 2.0:</strong> Scores are on the benchmark authors’ August 2026 task release; Fable 5 and Opus 5 were re-run under the same conditions. Because the task files differ from earlier releases, these numbers aren&#x27;t directly comparable to previously published OSWorld 2.0 results, which is why no competitor score is shown</p><p class="LaunchFootnotes-module-scss-module___FS4_a__note"><sup class="LaunchFootnotes-module-scss-module___FS4_a__label">3</sup> <!-- -->These three targets are (EGFR, Nipah G, 15-PGDH) and come from <a href="https://proteinbase.com/competitions" target="_blank" rel="noopener">Adaptyv Bio’s protein design competitions</a>. The Nipah G comparison is against <em>de novo</em> designs targeting the receptor-binding site on the G head (best: ~8–12 nM, <a href="https://proteinbase.com/proteins/shy-otter-jade" target="_blank" rel="noopener">N1032</a>). A <em>de novo</em> entry from <a href="https://blog.escalante.bio/winning-the-de-novo-portion-of-the-adaptyv-nipah-binder-competition/" target="_blank" rel="noopener">Nick Boyd/Escalante Bio</a> that targets a different region (the stalk) reached ~1.4 nM (<a href="https://proteinbase.com/proteins/shy-eagle-fern" target="_blank" rel="noopener">design_7</a>), comparable to our best binder.</p></div></section><section class="LaunchEndMatter-module-scss-module__Y_7aRW__end-matter"><div id="further-reading" class="LaunchEndMatter-module-scss-module__Y_7aRW__column"><h2 class="headline-5 serif LaunchEndMatter-module-scss-module__Y_7aRW__heading">Further reading</h2><div class="LaunchEndMatter-module-scss-module__Y_7aRW__items"><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">1<!-- -->.</strong> <!-- -->The Claude Fable 5.1 and Claude Mythos 5.1 system card. <a href="https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card" target="_blank" rel="noopener">View card</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">2<!-- -->.</strong> <!-- -->More detail on our Enterprise Frontier Safeguards. <a href="https://www.anthropic.com/news/enterprise-frontier-safeguards" target="_blank" rel="noopener">Learn more</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">3<!-- -->.</strong> <!-- -->Request access to our Enterprise Frontier Safeguards. <a href="https://www.anthropic.com/news/enterprise-frontier-safeguards" target="_blank" rel="noopener">Open form</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">4<!-- -->.</strong> <!-- -->An overview of our improvements to our biology safeguards. <a href="https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards" target="_blank" rel="noopener">Learn more</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">5<!-- -->.</strong> <!-- -->Support for scientists. <a href="https://www.anthropic.com/news/expanding-support-for-scientists" target="_blank" rel="noopener">Read more</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">6<!-- -->.</strong> <!-- -->Earlier work by Claude in protein design. <a href="https://www.anthropic.com/research/Claude-accelerates-protein-design" target="_blank" rel="noopener">Read more</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">7<!-- -->.</strong> <!-- -->Earlier work by Claude in mathematics. <a href="https://www.anthropic.com/research/riemann-zeta" target="_blank" rel="noopener">Read more</a></p><p class="LaunchEndMatter-module-scss-module__Y_7aRW__entry"><strong class="LaunchEndMatter-module-scss-module__Y_7aRW__label">8<!-- -->.</strong> <!-- -->More about the Model Hardware Standard. <a href="https://www.anthropic.com/news/model-hardware-standard-research-preview" target="_blank" rel="noopener">Learn more</a></p></div></div></section></article></div></main><footer id="footer" class="SiteFooter-module-scss-module__JdOqwq__root" role="contentinfo" aria-label="Site footer"><div class="page-wrapper SiteFooter-module-scss-module__JdOqwq__footer"><div class="SiteFooter-module-scss-module__JdOqwq__logoWrapper"><a href="/" aria-label="Return to homepage"><svg class="Icon-module-scss-module__lqbdHG__icon" width="46" height="32" viewBox="0 0 46 32"><path d="M32.73 0h-6.945L38.45 32h6.945L32.73 0ZM12.665 0 0 32h7.082l2.59-6.72h13.25l2.59 6.72h7.082L19.929 0h-7.264Zm-.702 19.337 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Until EFS is available, eligible customers will be able to use Fable 5.1 with zero data retention.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"d51e04287398\",\"_type\":\"block\",\"children\":[{\"_key\":\"f056af5cfed8\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Safeguards\"},{\"_key\":\"881afa14a536\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\". \"},{\"_key\":\"dad0f5322735\",\"_type\":\"span\",\"marks\":[],\"text\":\"We’ve improved our safeguards to reduce false positives (where the system flags benign content). In cybersecurity, our newest safeguards block 60% fewer false positives than before. In part, this is because Fable 5.1 can now be used to discover software vulnerabilities—though not to develop exploits for them. In biology, we’ve established an access program, developed in partnership with the US government, to enable access to Claude Mythos 5.1’s advanced biology capabilities. We expect to open enrollment for scientists soon.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"9404b8c00c31\",\"_type\":\"manateeNavMarker\",\"anchor\":{\"_type\":\"slug\",\"current\":\"frontier\"},\"heroLabel\":\"A new performance frontier\",\"markDefs\":null,\"navLabel\":\"A new performance frontier\"},{\"_key\":\"bf0e1ccc23e4\",\"_type\":\"block\",\"children\":[{\"_key\":\"ce8cc2e4f0c6\",\"_type\":\"span\",\"marks\":[],\"text\":\"A new performance frontier\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"74eab9fb626f\",\"_type\":\"block\",\"children\":[{\"_key\":\"62bc5065173f\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Fable 5.1 sets a new standard for coding, knowledge work, and long-running problem-solving tasks. The charts below show that Fable 5.1 is capable of much higher performance than its predecessor, Fable 5. And when set to Low or Medium effort, Fable 5.1 achieves results similar to or better than Fable 5’s at a much lower cost. (Note that Fable 5.1 defaults to High effort in Claude Code, and to Medium in Claude Cowork and on Claude.ai.)\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"c1d758d1950b\",\"_type\":\"viewSwitcher\",\"markDefs\":null,\"views\":[{\"_key\":\"lc0view\",\"_type\":\"view\",\"body\":[{\"_key\":\"lc0chart\",\"_type\":\"chart\",\"chartType\":\"line\",\"data\":\"series,x,y,label,labelPlacement\\nfable51,11.1,26.3,low,\\nfable51,14.9,35.7,med,\\nfable51,20.3,40.0,high,\\nfable51,31.8,49.5,xhigh,\\nfable51,37.9,52.6,max,\\nfable5,17.1,12.3,low,\\nfable5,25.0,21.4,med,\\nfable5,34.3,25.0,high,\\nfable5,36.0,23.4,xhigh,below\\nfable5,44.1,24.7,max,below\",\"footnote\":\"Terminal-Bench-Science 0.1: The standard error is ±3.5–4.5 pts per model. The public leaderboard (3 trials/task, Claude Code harness) reports Claude Opus 5 at 30.0% and Claude Fable 5 at 21.4%; our setup reproduces them at 29.0% and 24.7%, respectively, both within noise.\",\"markDefs\":null,\"series\":[{\"_key\":\"lc0fable51\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"solid\",\"id\":\"fable51\",\"name\":\"**Fable 5.1**\",\"outline\":false},{\"_key\":\"lc0fable5\",\"_type\":\"series\",\"color\":\"cloud\",\"fill\":\"solid\",\"id\":\"fable5\",\"name\":\"**Fable 5**\",\"outline\":false}],\"subtitle\":\"Accuracy vs Cost\",\"title\":\"Terminal-Bench-Science 0.1\",\"xAxis\":{\"format\":\"currency\",\"label\":\"Mean cost per task (USD, log scale)\",\"max\":50,\"min\":8,\"scale\":\"log\",\"ticks\":\"10,15,20,30,40,50\"},\"yAxis\":{\"format\":\"percent\",\"label\":\"Score (%)\",\"max\":60,\"min\":0}}],\"label\":\"Agentic scientific research\"},{\"_key\":\"lc1view\",\"_type\":\"view\",\"body\":[{\"_key\":\"lc1chart\",\"_type\":\"chart\",\"animate\":true,\"chartType\":\"line\",\"data\":\"series,x,y,label,labelPlacement\\nmythos51,5.4,41.7,low,\\nmythos51,7.4,46.7,med,\\nmythos51,10.1,57.1,high,\\nmythos51,15.0,59.7,xhigh,\\nmythos51,18.0,60.9,max,\\nfable51,5.7,40.2,low,below\\nfable51,7.8,43.4,med,below\\nfable51,10.5,49.4,high,below\\nfable51,15.8,51.3,xhigh,below\\nfable51,19.5,55.8,max,below\\nmythos5,12.3,21.5,low,below\\nmythos5,15.4,32.6,med,below\\nmythos5,17.9,38.9,high,below\\nmythos5,22.7,43.4,xhigh,below\\nmythos5,26.7,45.8,max,below\",\"footnote\":\"Terminal-Bench 4.0 scores by cost (log scale), at each effort level. Claude Fable 5.1 and Claude Mythos 5.1 are the same underlying model; the gap between them reflects the tasks on which our earlier, less precise cyber safeguards intervened. With the improvements we’re making to these safeguards today, we expect the difference between the models to be much smaller.\",\"markDefs\":null,\"series\":[{\"_key\":\"lc1mythos51\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"dots\",\"id\":\"mythos51\",\"name\":\"**Mythos 5.1**\",\"outline\":false},{\"_key\":\"lc1fable51\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"solid\",\"id\":\"fable51\",\"name\":\"**Fable 5.1**\",\"outline\":false},{\"_key\":\"lc1mythos5\",\"_type\":\"series\",\"color\":\"cloud\",\"fill\":\"dots\",\"id\":\"mythos5\",\"name\":\"**Mythos 5**\",\"outline\":false}],\"subtitle\":\"Accuracy vs Cost\",\"title\":\"Terminal-Bench 4.0\",\"xAxis\":{\"format\":\"currency\",\"label\":\"Mean cost per task (USD, log scale)\",\"max\":32,\"min\":4,\"scale\":\"log\",\"ticks\":\"5,8,10,15,20,30\"},\"yAxis\":{\"format\":\"percent\",\"label\":\"Score (%)\",\"max\":70,\"min\":10,\"ticks\":\"10,20,30,40,50,60,70\"}}],\"label\":\"Agentic terminal coding\"},{\"_key\":\"lc2view\",\"_type\":\"view\",\"body\":[{\"_key\":\"lc2chart\",\"_type\":\"chart\",\"chartType\":\"line\",\"data\":\"series,x,y,label,labelPlacement\\nf51tools,0.5237,60.0,low,\\nf51tools,0.6719,62.96,med,hidden\\nf51tools,1.0498,64.76,high,hidden\\nf51tools,2.2783,65.08,xhigh,hidden\\nf51tools,3.1957,65.0,max,\\nf5tools,0.6092,59.64,low,below\\nf5tools,1.0108,61.4,med,hidden\\nf5tools,1.4184,63.16,high,hidden\\nf5tools,1.9219,63.52,xhigh,hidden\\nf5tools,3.4373,63.8,max,below\\nf51notools,0.3002,53.16,low,below\\nf51notools,0.4632,55.92,med,hidden\\nf51notools,0.7517,57.96,high,hidden\\nf51notools,1.5282,60.36,xhigh,hidden\\nf51notools,2.2327,60.92,max,\\nf5notools,0.1721,50.6,low,below\\nf5notools,0.4009,55.88,med,hidden\\nf5notools,0.6181,56.88,high,hidden\\nf5notools,0.9079,57.44,xhigh,hidden\\nf5notools,1.6986,57.76,max,below\",\"footnote\":\"Humanity’s Last Exam scores by cost (log scale), at each effort level. CursorBench 3.2.0 scores by cost (log scale), at each effort level.\",\"markDefs\":null,\"series\":[{\"_key\":\"lc2f51tools\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"hatch\",\"id\":\"f51tools\",\"line\":\"slate\",\"name\":\"**Fable 5.1** (with tools)\",\"outline\":false},{\"_key\":\"lc2f51notools\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"solid\",\"id\":\"f51notools\",\"name\":\"**Fable 5.1** (no tools)\",\"outline\":false},{\"_key\":\"lc2f5tools\",\"_type\":\"series\",\"color\":\"cloud\",\"fill\":\"hatch\",\"id\":\"f5tools\",\"line\":\"slate\",\"name\":\"**Fable 5** (with tools)\",\"outline\":false},{\"_key\":\"lc2f5notools\",\"_type\":\"series\",\"color\":\"cloud\",\"fill\":\"solid\",\"id\":\"f5notools\",\"name\":\"**Fable 5** (no tools)\",\"outline\":false}],\"subtitle\":\"Accuracy vs Cost\",\"title\":\"Humanity's Last Exam\",\"xAxis\":{\"format\":\"currency\",\"label\":\"Mean cost per task (USD, log scale)\",\"max\":4,\"min\":0.15,\"scale\":\"log\",\"ticks\":\"0.2,0.5,1,2,4\"},\"yAxis\":{\"format\":\"percent\",\"label\":\"Pass rate (%)\",\"max\":68,\"min\":48,\"ticks\":\"50,55,60,65\"}},{\"_key\":\"65d200fefd53\",\"_type\":\"block\",\"children\":[{\"_key\":\"94b7e9fe4b8a\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"}],\"label\":\"Multidisciplinary reasoning\"},{\"_key\":\"lc3view\",\"_type\":\"view\",\"body\":[{\"_key\":\"lc3chart\",\"_type\":\"chart\",\"chartType\":\"line\",\"data\":\"series,x,y,label,labelPlacement\\nfable51,2.90,66.2,low,below\\nfable51,3.53,68.0,med,\\nfable51,4.80,69.4,high,below\\nfable51,6.96,72.8,xhigh,\\nfable51,9.64,73.4,max,\\nfable5,4.46,62.1,low,below\\nfable5,6.8,65.2,med,below\\nfable5,8.77,66.5,high,below\\nfable5,11.73,68.4,xhigh,below\\nfable5,17.32,70.5,max,below\",\"footnote\":\"CursorBench 3.2.0 by cost (log scale), at each effort level.\",\"markDefs\":null,\"series\":[{\"_key\":\"lc3fable51\",\"_type\":\"series\",\"color\":\"matcha\",\"fill\":\"solid\",\"id\":\"fable51\",\"name\":\"**Fable 5.1**\",\"outline\":false},{\"_key\":\"lc3fable5\",\"_type\":\"series\",\"color\":\"cloud\",\"fill\":\"solid\",\"id\":\"fable5\",\"name\":\"**Fable 5**\",\"outline\":false}],\"subtitle\":\"Accuracy vs Cost\",\"title\":\"CursorBench 3.2.0\",\"xAxis\":{\"format\":\"currency\",\"label\":\"Cost per task (USD, log scale)\",\"max\":25,\"min\":1.5,\"scale\":\"log\",\"ticks\":\"2,3,5,10,20\"},\"yAxis\":{\"format\":\"percent\",\"label\":\"Score (%)\",\"max\":76,\"min\":58,\"ticks\":\"60,65,70,75\"}}],\"label\":\"Agentic coding\"}]},{\"_key\":\"fd11dd86eb0a\",\"_type\":\"block\",\"children\":[{\"_key\":\"4471007234a8\",\"_type\":\"span\",\"marks\":[],\"text\":\"Fable 5.1 avoids shortcuts that result in poorer-quality work, and it’s smart enough to fix the root causes of software issues. For example, in testing by the investment firm Millennium, Fable 5.1 found the cause of a rare crash in its internal systems that none of its engineers (or any other model) had been able to explain after several years of trying.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"b8a88e4c8e64\",\"_type\":\"block\",\"children\":[{\"_key\":\"5e5d82f2caa7\",\"_type\":\"span\",\"marks\":[],\"text\":\"Here, you can see how Fable 5.1 compares across various benchmarks:\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"1ff33c052127\",\"_type\":\"benchmarkGrid\",\"caption\":\"Fable 5.1 was evaluated with its production safeguards enabled. On tasks where these safeguards intervened, Fable 5.1 and Fable 5 scored a zero on OSWorld 2.0, and Fable 5 scored a zero on AutomationBench. In all other interventions from our safeguards, cybersecurity tasks were completed by Claude Opus 4.8, and biology tasks were completed by Claude Opus 5. This likely reduces the performance of Fable 5.1 and Fable 5 on these benchmarks.\",\"competitorModels\":\"GPT-5.6 Sol\",\"data\":\"category,benchmark,qualifier,Fable 5.1,Fable 5,Opus 5,GPT-5.6 Sol\\nAgentic scientific research,Terminal-Bench-Science 0.1 [1],,52.6%,24.7%,29.0%,22.4%\\nAgentic coding,Terminal-Bench 4.0,,55.8% | 60.9% (Mythos 5.1),42.0%,52.3%,37.3%\\nKnowledge work,GDPval-AA v2,,1853,1723,1824,1711\\nComputer use,OSWorld 2.0 [2],partial,77.9%,72.9%,75.4%,—\\nComputer use,OSWorld 2.0,strict,41.7%,36.1%,39.6%,—\\nMultidisciplinary reasoning,Humanity's Last Exam,no tools,60.9%,57.8%,56.6%,—\\nMultidisciplinary reasoning,Humanity's Last Exam,with tools,65.0%,63.8%,63.6%,—\\nBusiness workflows,AutomationBench,,31.4%,17.1%,26.9%,19.6%\\nAgentic coding,CursorBench 3.2.0,,73.4%,70.5%,70.0%,67.2%\",\"highlightModel\":\"Fable 5.1\",\"markDefs\":null},{\"_key\":\"fb1e223f573c\",\"_type\":\"block\",\"children\":[{\"_key\":\"d53209ed7c6a\",\"_type\":\"span\",\"marks\":[],\"text\":\"Our early-access partners noticed these performance upgrades, and also picked up on more qualitative improvements in the model’s outputs. Here’s what they told us:\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"79ccb1bed281\",\"_type\":\"testimonialCarousel\",\"authorLabel\":\"Author\",\"companyLabel\":\"Company\",\"markDefs\":null,\"quotes\":[{\"_key\":\"8888beaa8eca\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Craig Falls\",\"authorRole\":\"Head of Quantitative Research\",\"company\":\"Jane Street Capital\",\"quote\":[{\"_key\":\"c978df98e324\",\"_type\":\"block\",\"children\":[{\"_key\":\"94ea31fba0f0\",\"_type\":\"span\",\"marks\":[],\"text\":\"“In internal benchmarks, Claude Fable 5.1 solves more of our coding problems than Fable 5 or Opus 5, and achieves state of the art on trading intuition. While prior models became hard to follow the longer they worked, Fable 5.1 remains readable over long, multi-step tasks.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"8ffce0f3456c\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Walden Yan\",\"authorRole\":\"Co-founder and CPO\",\"company\":\"Cognition\",\"quote\":[{\"_key\":\"0f5b7676d2ea\",\"_type\":\"block\",\"children\":[{\"_key\":\"ed918001cf64\",\"_type\":\"span\",\"marks\":[],\"text\":\"“We’re moving our Opus 5 traffic in Devin to Claude Fable 5.1 on launch day. It matched or edged out Fable 5 in our testing at a lower cost per task, and with the new cache read pricing a Fable-class model is finally economical for the workloads we’d kept on Opus, starting with code review.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"87be5743c44b\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Damien\",\"authorRole\":\"Senior Portfolio Manager\",\"company\":\"Millennium\",\"quote\":[{\"_key\":\"e6071d0f3592\",\"_type\":\"block\",\"children\":[{\"_key\":\"9e36c0174d37\",\"_type\":\"span\",\"marks\":[],\"text\":\"“A particular piece of code had an extremely rare crash, about one in a million runs, that nobody on our team had explained in four to five years. Every model I tried, including Fable 5, missed it. Claude Fable 5.1 was the first to find it. It disassembled an external vendor library, matched it against the core dump, and traced the crash to a bug in that library. The time it would have taken to conduct that analysis is hard to justify.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"12bec4524ca4\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Ron Sanzone\",\"authorRole\":\"Staff Software Engineer\",\"company\":\"MongoDB\",\"quote\":[{\"_key\":\"6ac45699e140\",\"_type\":\"block\",\"children\":[{\"_key\":\"6355271826ed\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Claude Fable 5.1 built a complex prototype in about three days. It did initial research across all of our services code and documentation to produce a novel and extensible design. It then ran for hours unattended, with strong verification loops, to implement the entire prototype. I would wake up in the morning to the next phase finished, with a full visual walkthrough of what it built and clear evidence of its success.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"0b7b347c1f8a\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Dan Shipper\",\"authorRole\":\"CEO\",\"company\":\"Every\",\"quote\":[{\"_key\":\"937cb0080369\",\"_type\":\"block\",\"children\":[{\"_key\":\"8e906320b207\",\"_type\":\"span\",\"marks\":[],\"text\":\"“It’s friendly Fable. Fable-level intelligence, Opus-level price, Sonnet-speed. In our tests it was about twice as fast as Opus 5 and used half as many tokens, so for anyone used to using Opus as their daily driver it’s an obvious upgrade.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"ffe33246fdb8\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Marquis Wong\",\"authorRole\":\"Principal AI Engineer\",\"company\":\"IMC\",\"quote\":[{\"_key\":\"7f6c4253c88c\",\"_type\":\"block\",\"children\":[{\"_key\":\"0fc0dafe8561\",\"_type\":\"span\",\"marks\":[],\"text\":\"“On our research suite, Claude Fable 5.1 set new best scores. On one task it came up with a novel solution along a completely different axis than we’d seen from other models or from human researchers in the past, which took its results well above the previous plateau. It's better at creative problem solving and getting that flash of insight you need to solve a difficult problem.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"c4e64bbcd354\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Josh Boyer\",\"authorRole\":\"Distinguished Engineer\",\"company\":\"Red Hat\",\"quote\":[{\"_key\":\"6b35fe4a146b\",\"_type\":\"block\",\"children\":[{\"_key\":\"c115ef70de06\",\"_type\":\"span\",\"marks\":[],\"text\":\"“As part of our ongoing evaluation of AI models, Claude Fable 5.1 delivered impressive results in our tests. Using Claude Code, it correctly identified the root cause of every broken build we tested, across all the effort levels. It also communicates more effectively than earlier Anthropic models, with updates that are more concise and easier to follow.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"4009dc6c8a4d\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Felix Giovanni Virgo\",\"authorRole\":\"Principal AI Engineer\",\"company\":\"Rakuten\",\"quote\":[{\"_key\":\"47cb6cfb8de5\",\"_type\":\"block\",\"children\":[{\"_key\":\"f3b4e6223454\",\"_type\":\"span\",\"marks\":[],\"text\":\"“We asked Claude Fable 5.1 to review a clinical research project for Rakuten Medical that three other frontier models had signed off on. It found a gap none of them had seen and insisted on testing it further. It then proposed a completely new hypothesis, turning a dataset we had written off into a new research direction in one afternoon. It’s the first time a frontier model like Claude has empowered us to explore new research in this way.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"7e8936b2cb26\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Willem Avé\",\"authorRole\":\"Global Head of Product\",\"company\":\"Square (Block)\",\"quote\":[{\"_key\":\"685b3adbd015\",\"_type\":\"block\",\"children\":[{\"_key\":\"90da1b1479df\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Claude Fable 5.1 is very smart. On our 30-day simulated run-a-business eval, where the model gets full access to simulated Square tools, customers, employees, and vendors, it was far more efficient per token than Opus 5. We plan to use it to work through our most complex scenarios, the kind that used to take days of whiteboarding, so our engineering teams can keep moving fast.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"a23a3f974a50\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Dwight Temple\",\"authorRole\":\"Sr. Machine Learning Engineer\",\"company\":\"Ramp\",\"quote\":[{\"_key\":\"c8bf207a1cbc\",\"_type\":\"block\",\"children\":[{\"_key\":\"0f64f47bc866\",\"_type\":\"span\",\"marks\":[],\"text\":\"“For anything research, greenfield or long-horizon, I would absolutely use Claude Fable 5.1 as the orchestrator. One unattended 38-hour run on a machine learning problem diagnosed a prior result as a label artifact, made the correction, kicked off six parallel experiments that ran overnight, and returned with a result and next steps. Given an open-ended prompt to find the highest-leverage problem nobody owned, it surfaced an unowned alert tied to a production outage, pulled the logs and prescribed the fix.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"b523c4737bfe\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Danny Wu\",\"authorRole\":\"Head of AI\",\"company\":\"Canva\",\"quote\":[{\"_key\":\"cd18eec416d8\",\"_type\":\"block\",\"children\":[{\"_key\":\"d24e36c585f8\",\"_type\":\"span\",\"marks\":[],\"text\":\"“The standout in Claude Fable 5.1 is the writing: more understandable, more meaningful, and it follows our writing guidance better. In blind tests against Fable 5, I preferred its writing and output. And in Canva Code it built a rhythm game with real music and on-beat gameplay matched to the level it generated, something no other model we tested delivered.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"172cf95cc825\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Aabhas Sharma\",\"authorRole\":\"CTO\",\"company\":\"Hebbia\",\"quote\":[{\"_key\":\"62465b43a999\",\"_type\":\"block\",\"children\":[{\"_key\":\"cd06af6a1036\",\"_type\":\"span\",\"marks\":[],\"text\":\"“On our PowerPoint eval, Claude Fable 5.1 produced the best decks of any model we’ve tested, both in slide craft and in fully answering our research topic. That same completeness showed up in our Citations eval, where it had the best fact recall over financial documents. And on complex, multi-part questions, it’s the first model we’ve seen answer every part.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"ef2ef0cca578\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Aditya Gupta\",\"authorRole\":\"Staff Software Engineer\",\"company\":\"Plaid\",\"quote\":[{\"_key\":\"11b5b6d9853b\",\"_type\":\"block\",\"children\":[{\"_key\":\"2580d8eb50e4\",\"_type\":\"span\",\"marks\":[],\"text\":\"“We had a change that touched more than eight services across three codebases. Claude Fable 5.1 mapped the whole workflow end to end, in extremely fine detail, from the incoming service call down to the individual function and the database tables and rows, and it was accurate all the way down. We appreciated the opportunity to test the model and provide feedback, helping us prepare for a new frontier where we can increasingly rely on these tools to take on bolder initiatives.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"cccc0d4f0313\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Nilesh Dalvi\",\"authorRole\":\"Engineering Lead\",\"company\":\"Glean\",\"quote\":[{\"_key\":\"0a3d11bec785\",\"_type\":\"block\",\"children\":[{\"_key\":\"a14e776e43b2\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Across our evaluation sets, our judges preferred Claude Fable 5.1’s answers roughly 2-to-1 over Fable 5 on everyday knowledge questions, high-intent research, and drafting and artifact work, all with improved response grounding over Fable. These results have made Fable 5.1 our go-to recommendation wherever Fable 5 was previously the choice.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"bcb41d0bcb46\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Sean Ward\",\"authorRole\":\"Co-founder and CEO\",\"company\":\"iGent\",\"quote\":[{\"_key\":\"aac21b8d8aaa\",\"_type\":\"block\",\"children\":[{\"_key\":\"d22d6f8040d0\",\"_type\":\"span\",\"marks\":[],\"text\":\"“On the hardest problems we work on, Claude Fable 5.1 separates strongly from any other model we’ve tried. On a grand challenge-tier problem we‘ve used as a testbed for 18 months, it actually produced material progress. Rather than being trapped in stamp collecting, it made clear white-space connections I have yet to see elsewhere. 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We also saw big gains in slide generation, with Fable 5.1 showing improvements in explaining complex data in simpler English and translating it into more banker-grade visuals.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"a756ef1e905a\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Ben Lafferty\",\"authorRole\":\"Senior Staff Engineer\",\"company\":\"Shopify\",\"quote\":[{\"_key\":\"03f88ca3838c\",\"_type\":\"block\",\"children\":[{\"_key\":\"b76c14370a4f\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Claude Fable 5.1 is more comfortable with long, unattended work than Fable 5. I’ve had workflows run for a long stretch without losing the plot: it keeps its own records, reprioritizes as things change, and picks up where it left off.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"bd3b561c50f4\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Raymond Lin\",\"authorRole\":\"Member of Technical Staff\",\"company\":\"Crosby\",\"quote\":[{\"_key\":\"32cba3336ba3\",\"_type\":\"block\",\"children\":[{\"_key\":\"31b0d5ac5760\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Compared to Fable 5, Claude Fable 5.1 was a massive improvement on RedlineBench, our contract redlining benchmark, improving from 47.9 to 57.0. Most of the gain came on first-turn quality, where it doubled the previous score, with substantial gains on counterparty acceptance as well. Its edits were also more concise, with smaller changes on average to the documents.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"63837a9e6f29\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Daniel Shan\",\"authorRole\":\"Staff Engineer\",\"company\":\"Datadog\",\"quote\":[{\"_key\":\"df94f96fcd89\",\"_type\":\"block\",\"children\":[{\"_key\":\"4b085fe3da7c\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Claude Fable 5.1 is a leading model for our incident investigation evals, which use real production incidents to assess how effectively our agent, Bits Investigation, can produce root cause analyses. We evaluate our agent’s output against root causes identified by our engineers. In these evaluations, it has demonstrated stronger reasoning than Opus 5 and has successfully diagnosed the most complex production incidents we’ve tested.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"4069839032c1\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Sualeh Asif\",\"authorRole\":\"Director of ML\",\"company\":\"SpaceXAI\",\"quote\":[{\"_key\":\"a7b600ddd0b2\",\"_type\":\"block\",\"children\":[{\"_key\":\"47f28fbeb156\",\"_type\":\"span\",\"marks\":[],\"text\":\"“Claude Fable 5.1 is the most capable model we’ve run on CursorBench 3.2, scoring 73.4% at max effort. We found it especially skilled at verifying its own work, allowing it to take on difficult coding tasks from start to finish.”\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"fd8112e83057\",\"_type\":\"testimonial\",\"audioUrl\":null,\"authorName\":\"Yuhao Zhang\",\"authorRole\":\"Research Lead\",\"company\":\"Samaya\",\"quote\":[{\"_key\":\"c3c1e6ae661b\",\"_type\":\"block\",\"children\":[{\"_key\":\"9e9a33b6362d\",\"_type\":\"span\",\"marks\":[],\"text\":\"“We evaluate models and systems on real-world investor workflows. On FrontierFinance, our latest finance benchmark, Claude Fable 5.1 shows a clear gain over Fable 5, with a 55.9% rubric score compared to 49.2%. The gain comes from its ability to dig harder into grounded, authoritative sources: on one earnings question, it went straight to the call transcript and captured the exact figures management cited, where other models leaned on secondary coverage.”\"}],\"markDefs\":[],\"style\":\"normal\"}]}],\"testimonialLabel\":\"Quote\"},{\"_key\":\"6bb402106119\",\"_type\":\"manateeNavMarker\",\"anchor\":{\"_type\":\"slug\",\"current\":\"scientific-research\"},\"heroLabel\":\"Scientific research\",\"markDefs\":null,\"navLabel\":\"Scientific research\"},{\"_key\":\"cc1ad6772e8d\",\"_type\":\"block\",\"children\":[{\"_key\":\"9e41ef8cb62b\",\"_type\":\"span\",\"marks\":[],\"text\":\"Scientific research\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"c0befd610e54\",\"_type\":\"block\",\"children\":[{\"_key\":\"4d14b0773f7f\",\"_type\":\"span\",\"marks\":[],\"text\":\"We tested the scientific research capabilities of Claude Fable 5.1 and Claude Mythos 5.1 across a wide range of domains. What we found—which includes the early examples we share below—adds to the evidence that AI models will soon make important contributions to scientific discovery.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"48047c4c5f69\",\"_type\":\"block\",\"children\":[{\"_key\":\"ed8535452da3\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Molecular design\"},{\"_key\":\"2128da5d638f\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\".\"},{\"_key\":\"ef86123535f7\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\" \"},{\"_key\":\"d8752839504a\",\"_type\":\"span\",\"marks\":[],\"text\":\"Many modern medicines work by binding to targets within the body to block, activate, or deliver something to them. High-affinity binders are necessary for drugs to work at lower doses; designing one is the first step in the development process for many common drug modalities. To see how well Claude Mythos 5.1 could do at this task, we gave the model access to open-source protein design and folding tools and sent its designs to two external organizations for experimental validation. Mythos 5.1 proved able to design very high-affinity binders. On three targets, \"},{\"_key\":\"368e5f5cd6f1\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"[3]\"},{\"_key\":\"560b44789a09\",\"_type\":\"span\",\"marks\":[],\"text\":\" its binding affinities were 10 times higher than the best designs submitted to \"},{\"_key\":\"65a6c16021a9\",\"_type\":\"span\",\"marks\":[\"29529e7a9561\"],\"text\":\"Adaptyv Bio’s protein design competitions\"},{\"_key\":\"7cff33ed6401\",\"_type\":\"span\",\"marks\":[],\"text\":\". Its hit rate (that is, the number of designs that were viable binders) was the strongest we’ve measured to date: it reached nearly 50% across 12 targets. (Hit rates of 10–15% are typical in protein design today.)\"}],\"markDefs\":[{\"_key\":\"29529e7a9561\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://proteinbase.com/competitions\"}],\"style\":\"normal\"},{\"_key\":\"683b117514ca\",\"_type\":\"manateeVideoSwitcher\",\"markDefs\":null,\"videos\":[{\"_key\":\"17be6cb219a8\",\"_type\":\"manateeVideo\",\"animatedPoster\":{\"_type\":\"file\",\"asset\":{\"_ref\":\"file-dd1483b60a862b1acb3ed4359631ec99653a84c7-webm\",\"_type\":\"reference\"}},\"animatedPosterUrl\":\"https://cdn.sanity.io/files/4zrzovbb/website/dd1483b60a862b1acb3ed4359631ec99653a84c7.webm\",\"caption\":\"Claude-designed protein binders (orange) for each of 12 targets (grey). Every design in the video was confirmed to bind in the lab. Structures shown are ESMFold2 predictions.\",\"poster\":{\"_type\":\"image\",\"alt\":\"Claude-designed protein binders (orange) for each of 12 targets (grey). Every design in the video was confirmed to bind in the lab. 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Its work was based on radar images taken by NASA’s Magellan mission more than 30 years ago and a \"},{\"_key\":\"35f76ef9f91c\",\"_type\":\"span\",\"marks\":[\"5a4e2378278a\"],\"text\":\"map\"},{\"_key\":\"53acd88237b5\",\"_type\":\"span\",\"marks\":[],\"text\":\" that already existed for one-fifth of the planet. 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The speed of these models is therefore a bottleneck to research progress. Mythos 5.1 provided one solution to this problem: by writing custom GPU kernels and caching their intermediate results, it sped up seven open-source deep learning models by up to 2.5 times (with identical outputs).\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"a0243c6bab4b\",\"_type\":\"block\",\"children\":[{\"_key\":\"9bde93237243\",\"_type\":\"span\",\"marks\":[],\"text\":\"The benefits of such speed-ups accumulate quickly. In any given experiment, biologists might run these models thousands of times (for example, testing every possible mutation near every human gene). On analyses like these, the optimized models cut estimated GPU costs by 30–60%. This kind of optimization would normally take a team of performance engineers weeks, and is often unaffordable for academic labs. Mythos 5.1 was able to do it in just days, using the publicly available source code alone. 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Last week, we previewed the \"},{\"_key\":\"a4fcbc5ae63a\",\"_type\":\"span\",\"marks\":[\"94abef0740c7\"],\"text\":\"Model Hardware Standard\"},{\"_key\":\"cc3a9319ce4f\",\"_type\":\"span\",\"marks\":[],\"text\":\", which allows Claude to directly and safely operate laboratory equipment. We’ve also recently \"},{\"_key\":\"84ae07025b94\",\"_type\":\"span\",\"marks\":[\"e9b6a2ed28bb\"],\"text\":\"expanded our support for scientists\"},{\"_key\":\"5f9c7fc1c5e3\",\"_type\":\"span\",\"marks\":[],\"text\":\" through our \"},{\"_key\":\"6e18d279237b\",\"_type\":\"span\",\"marks\":[\"0a2d6c91d16f\"],\"text\":\"AI for Science program\"},{\"_key\":\"fd37ea5629db\",\"_type\":\"span\",\"marks\":[],\"text\":\", which provides free credits to researchers working on high-impact scientific projects, and we are offering steeply discounted usage through our new \"},{\"_key\":\"1d6776db26a8\",\"_type\":\"span\",\"marks\":[\"72ad176f58db\"],\"text\":\"Claude Team plan for scientists\"},{\"_key\":\"8a27818f312c\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"94abef0740c7\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/model-hardware-standard-research-preview\"},{\"_key\":\"e9b6a2ed28bb\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/expanding-support-for-scientists\"},{\"_key\":\"0a2d6c91d16f\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/ai-for-science-program\"},{\"_key\":\"72ad176f58db\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://claude.com/programs/team-plan-for-scientists\"}],\"style\":\"normal\"},{\"_key\":\"8a9f66e04c89\",\"_type\":\"manateeNavMarker\",\"anchor\":{\"_type\":\"slug\",\"current\":\"safety-security-and-alignment\"},\"heroLabel\":\"Safety, security, and alignment\",\"markDefs\":null,\"navLabel\":\"Safety\"},{\"_key\":\"620a1f803644\",\"_type\":\"block\",\"children\":[{\"_key\":\"4fabdb9172df\",\"_type\":\"span\",\"marks\":[],\"text\":\"Safety, security, and alignment\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"fa099c3d6dff\",\"_type\":\"block\",\"children\":[{\"_key\":\"a90ce7f2c4a4\",\"_type\":\"span\",\"marks\":[],\"text\":\"AI models’ agentic capabilities have become much more powerful over the past two years. But as we’ve \"},{\"_key\":\"81788a55af6c\",\"_type\":\"span\",\"marks\":[\"d14b3bb61026\"],\"text\":\"documented\"},{\"_key\":\"4fd2eb7db6ba\",\"_type\":\"span\",\"marks\":[],\"text\":\", greater autonomy comes with new risks. Work on safety, security, and alignment needs to advance at the same pace as AI capabilities. Yesterday, we \"},{\"_key\":\"311026c8b736\",\"_type\":\"span\",\"marks\":[\"0000d3b4ad89\"],\"text\":\"published a report\"},{\"_key\":\"eceda85d7bf9\",\"_type\":\"span\",\"marks\":[],\"text\":\" describing how we are improving our own alignment and security efforts\"}],\"markDefs\":[{\"_key\":\"d14b3bb61026\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals\"},{\"_key\":\"0000d3b4ad89\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/improving-alignment-security-efforts\"}],\"style\":\"normal\"},{\"_key\":\"1cbbdf79808c\",\"_type\":\"block\",\"children\":[{\"_key\":\"66f3ea192373\",\"_type\":\"span\",\"marks\":[],\"text\":\"Prior to releasing Claude Fable 5.1 and Claude Mythos 5.1, we (and, in some cases, external researchers) subjected the models to extensive testing for risks across many areas. We describe these efforts in full in our \"},{\"_key\":\"e2cc58dc4fef\",\"_type\":\"span\",\"marks\":[\"35882d48ceb9\"],\"text\":\"System Card\"},{\"_key\":\"2fbbe1a8e187\",\"_type\":\"span\",\"marks\":[],\"text\":\"; below is a brief summary.\"}],\"markDefs\":[{\"_key\":\"35882d48ceb9\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card\"}],\"style\":\"normal\"},{\"_key\":\"006438491597\",\"_type\":\"block\",\"children\":[{\"_key\":\"994e1f4371e2\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Chemical and biological risks\"},{\"_key\":\"bbc65644961f\",\"_type\":\"span\",\"marks\":[],\"text\":\". We tested the extent to which Claude Mythos 5.1 could help create chemical or biological weapons. This involved expert red-teaming, automated evaluations, and a tabletop exercise that paired PhD-level biologists with AI experts, testing whether the models could match human specialists’ performance. Mythos 5.1’s capabilities are greater than those of Mythos 5. However, our evaluations indicate that it still falls short of the next risk tier defined in our \"},{\"_key\":\"88a36687843e\",\"_type\":\"span\",\"marks\":[\"7bc8da56ffbb\"],\"text\":\"Responsible Scaling Policy\"},{\"_key\":\"ebe76934f0ba\",\"_type\":\"span\",\"marks\":[],\"text\":\". We are therefore deploying Mythos 5.1 with the \"},{\"_key\":\"91b5365de993\",\"_type\":\"span\",\"marks\":[\"1f54f7029291\"],\"text\":\"same safeguards\"},{\"_key\":\"b8f3b0df8297\",\"_type\":\"span\",\"marks\":[],\"text\":\" that we applied to Mythos 5, which restrict access to research biology capabilities.\"}],\"markDefs\":[{\"_key\":\"7bc8da56ffbb\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/responsible-scaling-policy\"},{\"_key\":\"1f54f7029291\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards\"}],\"style\":\"normal\"},{\"_key\":\"e88e7829b3e5\",\"_type\":\"block\",\"children\":[{\"_key\":\"124afcc8c72b\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Cyber risks\"},{\"_key\":\"2051e16adb2d\",\"_type\":\"span\",\"marks\":[],\"text\":\". We ran a suite of evaluations to assess the cyber capabilities of Claude Mythos 5.1 (with cybersecurity safeguards off). Overall, the model demonstrates the strongest cyber capabilities of any model we’ve released, though it still falls within the lower category of risk in our \"},{\"_key\":\"fc019df9dd20\",\"_type\":\"span\",\"marks\":[\"ec328def08bd\"],\"text\":\"Frontier Compliance Framework\"},{\"_key\":\"05152d4e7d7e\",\"_type\":\"span\",\"marks\":[],\"text\":\". We also performed extensive stress-testing of our cybersecurity safeguards for Fable 5.1: in addition to our own dynamic evaluation of their robustness, we commissioned external testing from two organizations, along with automated testing by \"},{\"_key\":\"7c7f1d28c71f\",\"_type\":\"span\",\"marks\":[\"e1f0f1df0b5b\"],\"text\":\"Gray Swan\"},{\"_key\":\"1dc6dc9193e4\",\"_type\":\"span\",\"marks\":[],\"text\":\". As with Fable 5 and Opus 5, we have not found evidence of a \"},{\"_key\":\"10b0c77bc26c\",\"_type\":\"span\",\"marks\":[\"5edad6667166\"],\"text\":\"critical-severity jailbreak\"},{\"_key\":\"25bf14c5ee21\",\"_type\":\"span\",\"marks\":[],\"text\":\" for these safeguards.\"}],\"markDefs\":[{\"_key\":\"ec328def08bd\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/compliance-framework-SB53\"},{\"_key\":\"e1f0f1df0b5b\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.grayswan.ai/\"},{\"_key\":\"5edad6667166\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/fable-safeguards-jailbreak-framework\"}],\"style\":\"normal\"},{\"_key\":\"6c3be7828bc6\",\"_type\":\"block\",\"children\":[{\"_key\":\"2027a2de9199\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Agentic safety\"},{\"_key\":\"d8e792c902f3\",\"_type\":\"span\",\"marks\":[],\"text\":\". We ran evaluations of how Claude Mythos 5.1 responds to malicious requests and prompt injections (adversarial instructions hidden within content processed by AI models). It refused malicious agentic coding and computer use requests at a comparable rate to Mythos 5, Sonnet 5, and Opus 5, and it is our most robust model to date on an external \"},{\"_key\":\"9d91bbe22010\",\"_type\":\"span\",\"marks\":[\"955a5060abfb\"],\"text\":\"prompt injection benchmark\"},{\"_key\":\"6436172f6d49\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"955a5060abfb\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card\"}],\"style\":\"normal\"},{\"_key\":\"9ec7556a78ff\",\"_type\":\"block\",\"children\":[{\"_key\":\"b7a047385404\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Alignment\"},{\"_key\":\"6aefd2d911d9\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\".\"},{\"_key\":\"f135ff2efe16\",\"_type\":\"span\",\"marks\":[],\"text\":\" We tested the model’s behavior through static and interactive behavioral evaluations, analyses of its internal thinking using \"},{\"_key\":\"b73a98e2bc2e\",\"_type\":\"span\",\"marks\":[\"23648af98f70\"],\"text\":\"natural language autoencoders\"},{\"_key\":\"4134b0c0b6b7\",\"_type\":\"span\",\"marks\":[],\"text\":\", misalignment-related capability evaluations, a review of our training data, and analyses of our internal pilot use. We also received reports from external testing.\"}],\"markDefs\":[{\"_key\":\"23648af98f70\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/research/natural-language-autoencoders\"}],\"style\":\"normal\"},{\"_key\":\"c178025fbd2c\",\"_type\":\"block\",\"children\":[{\"_key\":\"31626937a8d0\",\"_type\":\"span\",\"marks\":[],\"text\":\"Our automated behavioral audit found that Claude Mythos 5.1 is better aligned across most metrics than its predecessor, Mythos 5. The model is significantly less likely than Mythos 5 to try to access resources outside of its test environment when assigned an otherwise impossible task. It is also less likely than Mythos 5 to use motivated reasoning to justify its actions (for instance, by reasoning that the situation is a simulation or evaluation), and it is less likely to ignore explicit constraints in pursuit of users’ goals. From our review of its training data, Mythos 5.1 both attempts reward hacking (or cheating), and succeeds at it, at a lower overall rate than Mythos 5.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"b29b6c9a23e6\",\"_type\":\"block\",\"children\":[{\"_key\":\"17cb9437d2e0\",\"_type\":\"span\",\"marks\":[],\"text\":\"Though generally our alignment evaluations showed improvements, our testing found the model can still sometimes bypass approvals and auto-mode classifiers (as we discuss in more detail in our \"},{\"_key\":\"c700868b78a6\",\"_type\":\"span\",\"marks\":[\"50c4dcefb25c\"],\"text\":\"System Card\"},{\"_key\":\"e3d9a3f718c5\",\"_type\":\"span\",\"marks\":[],\"text\":\"). There are also limitations to the coverage provided by our alignment assessment. Currently, our automated behavioral audit provides less visibility into very long-context work and multi-agent settings. We also have less coverage of impossible tasks (which can elicit more abnormal and misaligned behavior) than we’d like, although we’ve recently made improvements in this domain and are working hard to continue doing so.\"}],\"markDefs\":[{\"_key\":\"50c4dcefb25c\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card\"}],\"style\":\"normal\"},{\"_key\":\"1b1a5a0b746f\",\"_type\":\"block\",\"children\":[{\"_key\":\"d3110504e422\",\"_type\":\"span\",\"marks\":[],\"text\":\"We have also improved our safeguards so that they allow our models to be more useful without compromising on safety. We describe these changes below.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"146449b4ac71\",\"_type\":\"block\",\"children\":[{\"_key\":\"83e6d270b63a\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Automated safeguards for enterprises\"},{\"_key\":\"61ad5ff1a99a\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\".\"},{\"_key\":\"07173e1060d4\",\"_type\":\"span\",\"marks\":[],\"text\":\" \"},{\"_key\":\"8bbe3743727f\",\"_type\":\"span\",\"marks\":[\"bcbb7f5cc2a0\"],\"text\":\"Enterprise Frontier Safeguards\"},{\"_key\":\"bd3cf7454f0d\",\"_type\":\"span\",\"marks\":[],\"text\":\" (EFS) allows us to detect and respond to misuse of our models while still providing our enterprise customers the privacy of a zero data retention agreement. With EFS, customers store their data on their own cloud infrastructure, rather than on Anthropic’s systems; any human review is, by default, done by the customer themselves, rather than Anthropic. We developed EFS in close collaboration with more than 100 customers across industries like financial services, healthcare, manufacturing, telecom, law, retail, and the public sector, and with our cloud partners at Amazon Web Services, Google Cloud, and Microsoft Azure.\"}],\"markDefs\":[{\"_key\":\"bcbb7f5cc2a0\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/enterprise-frontier-safeguards\"}],\"style\":\"normal\"},{\"_key\":\"da83739b277d\",\"_type\":\"block\",\"children\":[{\"_key\":\"49aac5159d3c\",\"_type\":\"span\",\"marks\":[],\"text\":\"EFS will be supported on Claude Code, Claude Enterprise, the Claude Platform, Amazon Bedrock, Claude Platform on AWS, Google’s Agent Platform, and Microsoft Foundry. It’s rolling out in phases, starting this fall. As noted above, customers who are eligible for EFS can use Fable 5.1 (and Fable 5) with zero data retention until EFS is ready. You can read more about EFS \"},{\"_key\":\"774d49d6eb9c\",\"_type\":\"span\",\"marks\":[\"aeb27a122715\"],\"text\":\"here\"},{\"_key\":\"838db74fcf42\",\"_type\":\"span\",\"marks\":[],\"text\":\"; to request access, please complete \"},{\"_key\":\"4ba2c7345883\",\"_type\":\"span\",\"marks\":[\"fc35bdf6f45b\"],\"text\":\"this form\"},{\"_key\":\"8d938a27a0e5\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"fc35bdf6f45b\",\"_type\":\"link\",\"href\":\"https://claude.com/form/enterprise-frontier-safeguards\"},{\"_key\":\"aeb27a122715\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/enterprise-frontier-safeguards\"}],\"style\":\"normal\"},{\"_key\":\"85eed2b97f06\",\"_type\":\"block\",\"children\":[{\"_key\":\"8997eec9fd6f\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"More precise safeguards for biology and cybersecurity\"},{\"_key\":\"247805bce282\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\". \"},{\"_key\":\"113bb2748d87\",\"_type\":\"span\",\"marks\":[],\"text\":\"In the past few months, we’ve made progress in making our safeguards for Fable 5.1 more precise: ensuring that they’re less likely to flag benign content (like queries about medical issues or cyberdefenders using the model to make their systems safer), but still ensuring they provide robust protection against genuine threats.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"cb405c13e34f\",\"_type\":\"block\",\"children\":[{\"_key\":\"85f6da219f46\",\"_type\":\"span\",\"marks\":[],\"text\":\"As we \"},{\"_key\":\"c0b497f52d0a\",\"_type\":\"span\",\"marks\":[\"1bd52966ab7d\"],\"text\":\"recently shared\"},{\"_key\":\"0a35c92bc73e\",\"_type\":\"span\",\"marks\":[],\"text\":\", our latest biology safeguards for Fable 5.1 and Fable 5 fire 85% less often for benign requests related to elementary biology and medical questions (relative to those that launched with Fable 5). However, queries related to research and development in the life sciences will still be directed to our Opus models. We’re making the model’s life sciences capabilities available to professionals through an access program for Claude Mythos 5.1 that we’ve developed in partnership with the US government, which we discuss below.\"}],\"markDefs\":[{\"_key\":\"1bd52966ab7d\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards\"}],\"style\":\"normal\"},{\"_key\":\"2bce345c3d7e\",\"_type\":\"block\",\"children\":[{\"_key\":\"fd5e8050e43a\",\"_type\":\"span\",\"marks\":[],\"text\":\"With Fable 5.1, we’re updating our cybersecurity safeguards to be more precise. We’re also now allowing Fable 5.1 to be used for identifying software vulnerabilities—that is, to conduct the kind of defensive work that improves software security. As a result of these changes, Claude Code users can expect an average of around 60% fewer interventions per session from our cyber safeguards, relative to the previous safeguards on Fable 5. Our safeguards do, however, still redirect several kinds of dual-use cybersecurity tasks (tasks that might have helpful \"},{\"_key\":\"139decdc9121\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"or\"},{\"_key\":\"44793b608ff5\",\"_type\":\"span\",\"marks\":[],\"text\":\" harmful applications) to our Opus models. This includes penetration testing, exploit generation, and binary-based vulnerability scanning.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"4d4f37f0e2f8\",\"_type\":\"block\",\"children\":[{\"_key\":\"980cc10e75b6\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Anti-distillation mechanisms\"},{\"_key\":\"e5e3a9541803\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\".\"},{\"_key\":\"73ecac6ac823\",\"_type\":\"span\",\"marks\":[],\"text\":\" Distillation is a method used to extract the capabilities of advanced models. It is often employed on an industrial scale, using thousands of fake accounts. Distillation is a safety risk, since the distilled capabilities can subsequently be released without adequate safeguards. Fable 5.1 comes with strengthened mechanisms to make distillation attacks harder. For example, it is no longer possible for new API accounts (those created from today onwards) to manually edit Claude’s prior context in a multi-turn conversation while preserving the transcript of Claude’s prior thinking. This closes off a common, publicly documented distillation technique, which allowed distillers to illicitly extract Claude’s thinking. We’re rolling out the change gradually, to minimize disruption: existing accounts are not currently affected by this change, though it will apply to all users with future model releases. A small number of customers’ custom integrations will then be affected. Our \"},{\"_key\":\"42c922916aab\",\"_type\":\"span\",\"marks\":[\"e0f0a67581ac\"],\"text\":\"Help Center article\"},{\"_key\":\"aff969eaa8a9\",\"_type\":\"span\",\"marks\":[],\"text\":\" explains more about this change and the adjustments that developers can make.\"}],\"markDefs\":[{\"_key\":\"e0f0a67581ac\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://support.claude.com/en/articles/16761192\"}],\"style\":\"normal\"},{\"_key\":\"e31e6a5834ff\",\"_type\":\"manateeNavMarker\",\"anchor\":{\"_type\":\"slug\",\"current\":\"mythos\"},\"heroLabel\":\"Claude Mythos 5.1\",\"markDefs\":null,\"navLabel\":\"Claude Mythos 5.1\"},{\"_key\":\"771d5b372731\",\"_type\":\"block\",\"children\":[{\"_key\":\"4c75f6a26ad3\",\"_type\":\"span\",\"marks\":[],\"text\":\"Trusted access for Claude Mythos 5.1\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"1cd86f37a7a5\",\"_type\":\"block\",\"children\":[{\"_key\":\"09a81adf3a5e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Mythos 5.1 is identical to Fable 5.1, but it offers more permissive safeguards for vetted individuals and organizations whose work is affected by the cybersecurity and life sciences restrictions outlined above. It will be available through two trusted access programs:\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"56743b1a0801\",\"_type\":\"block\",\"children\":[{\"_key\":\"845ed512c040\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Cyber Verification Program:\"},{\"_key\":\"49ddf118103f\",\"_type\":\"span\",\"marks\":[],\"text\":\" The CVP currently provides access to certain Opus- and Sonnet-class models with reduced cyber safeguards for defensive security work. In the near future, this program will also include access to Claude Mythos-class models. \"},{\"_key\":\"7d33a4e4a6d0\",\"_type\":\"span\",\"marks\":[\"703138737e77\"],\"text\":\"Apply to join the CVP here\"},{\"_key\":\"c0837c979fc7\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"703138737e77\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://portal.anthropic.com/programs/cvp\"}],\"style\":\"normal\"},{\"_key\":\"d943c7995958\",\"_type\":\"block\",\"children\":[{\"_key\":\"e531e25dccdf\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Life Sciences Verification Program:\"},{\"_key\":\"8d311d6893f7\",\"_type\":\"span\",\"marks\":[],\"text\":\" The LSVP is designed so that life sciences professionals can use Claude Mythos 5.1 with safeguards designed for professional research and development activities (while all other safeguards remain in place). In partnership with the US government, we have enrolled our first participants, and we plan to expand access to this program to the broader life sciences community.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"e0c69e6a7307\",\"_type\":\"block\",\"children\":[{\"_key\":\"375ec457a4a5\",\"_type\":\"span\",\"marks\":[],\"text\":\"In addition to these trusted access programs, \"},{\"_key\":\"2d7e45ee9850\",\"_type\":\"span\",\"marks\":[\"10b4d72f716e\"],\"text\":\"Claude Security\"},{\"_key\":\"6b52731a1768\",\"_type\":\"span\",\"marks\":[],\"text\":\", our product that scans codebases for vulnerabilities and suggests patches for human review, is now also powered by Claude Mythos 5.1.\"}],\"markDefs\":[{\"_key\":\"10b4d72f716e\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://claude.com/product/claude-security\"}],\"style\":\"normal\"},{\"_key\":\"0c93e1afcbf9\",\"_type\":\"block\",\"children\":[{\"_key\":\"4d46c08ee34f\",\"_type\":\"span\",\"marks\":[],\"text\":\"Compliance with the EU AI Act\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"ac1a0e6da03a\",\"_type\":\"block\",\"children\":[{\"_key\":\"3b29a35ab6ab\",\"_type\":\"span\",\"marks\":[],\"text\":\"In July 2026, Anthropic (along with \"},{\"_key\":\"0090f91ad75e\",\"_type\":\"span\",\"marks\":[\"675a489eb720\"],\"text\":\"190 other signatories\"},{\"_key\":\"b204b1263aea\",\"_type\":\"span\",\"marks\":[],\"text\":\", including several other major AI model providers) signed the EU AI Act’s Code of Practice on Transparency of AI-Generated Content.\"}],\"markDefs\":[{\"_key\":\"675a489eb720\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://digital-strategy.ec.europa.eu/en/news/strong-backing-code-practice-transparency-ai-generated-content\"}],\"style\":\"normal\"},{\"_key\":\"67f81c6c073a\",\"_type\":\"block\",\"children\":[{\"_key\":\"ec1196b54276\",\"_type\":\"span\",\"marks\":[],\"text\":\"This required us to add a watermark—a numerical way of determining the likelihood that Claude was involved in writing a piece of text—to the outputs of models released after August 2, 2026. As we \"},{\"_key\":\"29b4afcc9d21\",\"_type\":\"span\",\"marks\":[\"1cd774849139\"],\"text\":\"recently explained\"},{\"_key\":\"3b7fa4f0a4d5\",\"_type\":\"span\",\"marks\":[],\"text\":\", this watermark is invisible to anyone who does not have the detection API. It has no practical impact on the quality or content of Claude’s outputs and contains no information about the user, their organization, or their conversations with Claude.\"}],\"markDefs\":[{\"_key\":\"1cd774849139\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/news/claude-text-watermark\"}],\"style\":\"normal\"},{\"_key\":\"5a88af64a7db\",\"_type\":\"block\",\"children\":[{\"_key\":\"64716ad0184a\",\"_type\":\"span\",\"marks\":[],\"text\":\"The Act also required us to provide a way for users to tell whether a text likely contains the watermark. We are thus rolling out a detection API in private preview. It is currently available to eligible organizations (such as regulators, law enforcement, media, fact-checkers, independent researchers, educational organizations, and EU civil society groups) as required under EU law. It is also available for enterprises that are similarly obligated to verify watermarking for their own compliance with the Act. We plan to expand access to the detection API over time. You can register interest in access \"},{\"_key\":\"e10924b3c2aa\",\"_type\":\"span\",\"marks\":[\"51b0734d90c6\"],\"text\":\"here\"},{\"_key\":\"bf25bf4e2386\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"51b0734d90c6\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://forms.gle/9tGA33hPJJwtHsMk9\"}],\"style\":\"normal\"},{\"_key\":\"74bf3d32ee33\",\"_type\":\"manateeNavMarker\",\"anchor\":{\"_type\":\"slug\",\"current\":\"cost-and-availability\"},\"markDefs\":null,\"navLabel\":\"Cost and availability\"},{\"_key\":\"811591ef110f\",\"_type\":\"block\",\"children\":[{\"_key\":\"51f13fefb586\",\"_type\":\"span\",\"marks\":[],\"text\":\"Cost and availability\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"d4f08d2503d0\",\"_type\":\"block\",\"children\":[{\"_key\":\"c03035da2586\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Fable 5.1 is available today on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. Developers can get started with \"},{\"_key\":\"aa3faa8848bf\",\"_type\":\"span\",\"marks\":[\"code\"],\"text\":\"claude-fable-5-1\"},{\"_key\":\"cde746ee8b44\",\"_type\":\"span\",\"marks\":[],\"text\":\" on the Claude API.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"87610af94038\",\"_type\":\"block\",\"children\":[{\"_key\":\"d7ff61667039\",\"_type\":\"span\",\"marks\":[],\"text\":\"As mentioned above, we have reduced the price of Fable 5.1’s cache reads (where the model reuses context it has already processed) wherever usage is billed by token, such as on our API. Cache reads now cost 75% less, or $0.25 per million tokens.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"464d0da599e8\",\"_type\":\"block\",\"children\":[{\"_key\":\"23dec96faeee\",\"_type\":\"span\",\"marks\":[],\"text\":\"This change leads to a substantial reduction in the overall cost of running the model. For typical workloads, costs are reduced by around 25% relative to Fable 5. For complex coding and highly agentic tasks, the savings could be up to around 45%. The graph below illustrates why this change makes such a big difference:\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"workloadchart\",\"_type\":\"chart\",\"animate\":false,\"chartType\":\"bar\",\"data\":\"panel,panelTitle,group,series,value,stack,ground,label\\ntypical,Typical workload,**Fable 5**,reads,40,yes,cloud,100\\ntypical,,**Fable 5**,other,60,yes,,\\ntypical,,**Fable 5.1**,reads,11,yes,matcha,75 (~25% less)\\ntypical,,**Fable 5.1**,other,64,yes,,\\nagentic,Highly agentic workload,**Fable 5**,reads,65,yes,cloud,100\\nagentic,,**Fable 5**,other,35,yes,,\\nagentic,,**Fable 5.1**,reads,17,yes,matcha,55 (~45% less)\\nagentic,,**Fable 5.1**,other,38,yes,,\",\"footnote\":\"Indexed cost of running the same workloads on Fable 5 and Fable 5.1, at usage-based pricing measured at default effort over four weeks of actual usage in August 2026. 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