<!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/2pnrpp88lh_gg.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/2jn9l_ludk91m.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/19a2mp9v_87ce.css" data-precedence="next"/><link rel="stylesheet" href="/_next/static/chunks/1rheyw5ipd0tt.css" data-precedence="next"/><link rel="preload" 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20C7.16699 19.5858 7.50278 19.25 7.91699 19.25H32.084ZM32.084 10.5C32.4979 10.5004 32.834 10.836 32.834 11.25C32.8339 11.6639 32.4979 11.9996 32.084 12H7.91699C7.50282 12 7.16706 11.6642 7.16699 11.25C7.16699 10.8358 7.50278 10.5 7.91699 10.5H32.084Z" fill="currentColor"></path></svg></button></div></div></header><main id="main-content" class="bg-ivory-light"><article><section class="LandingPageSection-module-scss-module__ZSMdoa__root bg-ivory-light" data-theme="ivory"><div class="page-wrapper HeroModel-module-scss-module__kMpfDa__wrapper"><h1 class="HeroModel-module-scss-module__kMpfDa__hidden-header">Claude Sonnet 4.6</h1><div class="HeroModel-module-scss-module__kMpfDa__wordmark-desktop"><img alt="Claude Sonnet 4.6" loading="lazy" width="1015" height="125" decoding="async" data-nimg="1" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/b3b985350fe7c9090fd3e0c992994dee39a78c72-1015x125.svg"/></div><div class="HeroModel-module-scss-module__kMpfDa__wordmark-mobile"><img alt="Claude Sonnet 4.6" loading="lazy" width="174" height="144" decoding="async" data-nimg="1" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/cfa57f07c6ac1832f35b1364c2868cc47ae55d05-174x144.svg"/></div><p class="body-large-2 serif tight HeroModel-module-scss-module__kMpfDa__tagline">Hybrid reasoning model with fast, capable intelligence for real-time agents and high-volume work, featuring a 1M context window</p><div class="ButtonGroup-module-scss-module__DBorwq__wrapper HeroModel-module-scss-module__kMpfDa__button-group" data-theme="ivory"><a href="https://claude.ai/" class="Button-module-scss-module__f9ZZrG__button Button-module-scss-module__f9ZZrG__large Button-module-scss-module__f9ZZrG__primary ButtonGroup-module-scss-module__DBorwq__button" 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__large Button-module-scss-module__f9ZZrG__secondary ButtonGroup-module-scss-module__DBorwq__button" target="_blank" rel="noopener"><span class="body-3">Get API access</span></a></div></div></section><section class="LandingPageSection-module-scss-module__ZSMdoa__root bg-ivory-light LandingPageSection-module-scss-module__ZSMdoa__flushBottom LandingPageSection-module-scss-module__ZSMdoa__flushTop" data-theme="ivory"><div class="page-wrapper"><article><div class=""><div class="Body-module-scss-module__z40yvW__body" data-theme="ivory"><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="announcements">Announcements</h2><div class="Body-module-scss-module__z40yvW__reading-column"><ul class="LatestUpdates-module-scss-module__sg7Vka__wrapper"><li><div class="LatestUpdates-module-scss-module__sg7Vka__update"><div class="body-3 LatestUpdates-module-scss-module__sg7Vka__headline"><span class="caption LatestUpdates-module-scss-module__sg7Vka__label">NEW</span><p class="body-3 bold">Claude Sonnet 5</p><p class="body-3 LatestUpdates-module-scss-module__sg7Vka__date">Jun 30, 2026</p></div><p class="body-3 serif LatestUpdates-module-scss-module__sg7Vka__summary">Our most agentic Sonnet yet. It brings performance that recently required larger, more expensive models to coding, tool use, and everyday work, at Sonnet’s speed and price.</p><a class="LatestUpdates-module-scss-module__sg7Vka__link" href="/news/claude-sonnet-5"><p class="body-3">Read more</p><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 21 21"><path d="M4.14585 9.87492L14.4584 9.87492L9.60419 5.04158L10.5 4.14575L16.8542 10.4999L10.5 16.8541L9.60419 15.9583L14.4584 11.1249L4.14585 11.1249L4.14585 9.87492Z" fill="currentColor"></path></svg></a></div></li><li><div class="LatestUpdates-module-scss-module__sg7Vka__update" data-nosnippet=""><div class="body-3 LatestUpdates-module-scss-module__sg7Vka__headline"><p class="body-3 bold">Claude Sonnet 4.6</p><p class="body-3 LatestUpdates-module-scss-module__sg7Vka__date">Feb 17, 2026</p></div><p class="body-3 serif LatestUpdates-module-scss-module__sg7Vka__summary">Sonnet 4.6 delivers frontier performance across coding, agents, and professional work at scale. It can compress multi-day coding projects into hours and deliver production-ready solutions.</p><a class="LatestUpdates-module-scss-module__sg7Vka__link" href="/news/claude-sonnet-4-6"><p class="body-3">Read more</p><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 21 21"><path d="M4.14585 9.87492L14.4584 9.87492L9.60419 5.04158L10.5 4.14575L16.8542 10.4999L10.5 16.8541L9.60419 15.9583L14.4584 11.1249L4.14585 11.1249L4.14585 9.87492Z" fill="currentColor"></path></svg></a></div></li><li><div class="LatestUpdates-module-scss-module__sg7Vka__update" data-nosnippet=""><div class="body-3 LatestUpdates-module-scss-module__sg7Vka__headline"><p class="body-3 bold">Claude Sonnet 4.5 </p><p class="body-3 LatestUpdates-module-scss-module__sg7Vka__date">Sep 29, 2025</p></div><p class="body-3 serif LatestUpdates-module-scss-module__sg7Vka__summary">Sonnet 4.5 is the best model in the world for agents, coding, and computer use.  It’s also our most accurate and detailed model for long-running tasks, with enhanced domain knowledge in coding, finance, and cybersecurity.</p><a class="LatestUpdates-module-scss-module__sg7Vka__link" href="/news/claude-sonnet-4-5"><p class="body-3">Read more</p><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 21 21"><path d="M4.14585 9.87492L14.4584 9.87492L9.60419 5.04158L10.5 4.14575L16.8542 10.4999L10.5 16.8541L9.60419 15.9583L14.4584 11.1249L4.14585 11.1249L4.14585 9.87492Z" fill="currentColor"></path></svg></a></div></li><li><div class="LatestUpdates-module-scss-module__sg7Vka__update" data-nosnippet=""><div class="body-3 LatestUpdates-module-scss-module__sg7Vka__headline"><p class="body-3 bold">Claude Sonnet 4</p><p class="body-3 LatestUpdates-module-scss-module__sg7Vka__date">May 22, 2025</p></div><p class="body-3 serif LatestUpdates-module-scss-module__sg7Vka__summary">Sonnet 4 improves on Sonnet 3.7 across a variety of areas, especially coding. It offers frontier performance that’s practical for most AI use cases, including user-facing AI assistants and high-volume tasks.</p><a class="LatestUpdates-module-scss-module__sg7Vka__link" href="/news/claude-4"><p class="body-3">Read more</p><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 21 21"><path d="M4.14585 9.87492L14.4584 9.87492L9.60419 5.04158L10.5 4.14575L16.8542 10.4999L10.5 16.8541L9.60419 15.9583L14.4584 11.1249L4.14585 11.1249L4.14585 9.87492Z" fill="currentColor"></path></svg></a></div></li></ul></div><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="availability-and-pricing">Availability and pricing</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Anyone can chat with Claude using Sonnet 5 on Claude.ai, available on web, iOS, and Android.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">For developers interested in building agents, Sonnet 5 is available on the Claude Platform natively, and on Amazon Web Services, Google Cloud, and Microsoft Foundry.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 is available today at $2 per million input tokens and $10 per million output tokens, with up to 90% cost savings with <a href="https://platform.claude.com/docs/en/build-with-claude/prompt-caching">prompt caching</a> and 50% cost savings with <a href="https://platform.claude.com/docs/en/build-with-claude/batch-processing#pricing">batch processing</a>. To learn more, check out our <a href="https://claude.com/pricing#api">pricing page</a>. To get started, simply use <code class="InlineCodeBlock-module-scss-module__nsPAba__code">claude-sonnet-5</code> via the <a href="https://platform.claude.com/docs/en/about-claude/models/overview">Claude API</a>.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">For workloads that need to run in the US, US-only inference is available at 1.1x pricing for input and output tokens. <a href="https://platform.claude.com/docs/en/build-with-claude/data-residency">Learn more</a>.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="use-cases">Use cases</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 is a powerful, versatile model built for daily use, scaled production, and complex tasks across coding, agents, and professional workflows.</p><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">API users have fine-grained control over the model’s thinking effort. Popular use cases include:</p><h3 class="Body-module-scss-module__z40yvW__reading-column headline-5 post-section" id="advanced-coding">Advanced coding</h3><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 delivers exceptional coding performance across the entire software development lifecycle. From initial planning and implementation to debugging, maintenance, and large-scale refactors, it excels at reasoning through complex, multi-file codebases, producing precise implementations, and iterating with minimal back-and-forth.</p><h3 class="Body-module-scss-module__z40yvW__reading-column headline-5 post-section" id="long-running-agents">Long-running agents</h3><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 offers superior instruction following, tool selection, and error correction for autonomous AI workflows. It reliably handles complex, multi-step tasks that require sustained coherence and adaptive decision-making, making it an ideal backbone for customer-facing agents, internal automation, and production-grade AI systems that need to operate independently at scale.</p><h3 class="Body-module-scss-module__z40yvW__reading-column headline-5 post-section" id="browser-and-computer-use">Browser and computer use</h3><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 excels in computer use capabilities, reliably handling any browser-based task from competitive analysis to procurement workflows to customer onboarding. Building on the foundation that made Sonnet the first frontier AI model to use computers, Sonnet 5 navigates digital environments with greater accuracy and reliability, enabling enterprises to automate workflows that previously required human intervention.</p><h3 class="Body-module-scss-module__z40yvW__reading-column headline-5 post-section" id="enterprise-workflows">Enterprise workflows</h3><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 handles high-volume, high-stakes professional workflows across finance, research, content, and business operations. It can analyze complex financial data, synthesize insights from internal and external sources, generate and edit documents and spreadsheets, and produce compelling written content with nuance and precision.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="benchmarks">Benchmarks</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">Sonnet 5 delivers strong results across the benchmarks that matter most for real-world deployment, from reasoning and tool use to software coding, knowledge work, and more.</p><div class="Body-module-scss-module__z40yvW__media-column"><figure class="ImageWithCaption-module-scss-module__Duq99q__e-imageWithCaption"><img alt="Claude Sonnet 5 benchmark table" loading="lazy" width="2600" height="1234" decoding="async" data-nimg="1" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F9941d610909f28a504e16dd5af823df172ec6035-2600x1234.png&amp;w=3840&amp;q=75 1x" src="/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F9941d610909f28a504e16dd5af823df172ec6035-2600x1234.png&amp;w=3840&amp;q=75"/></figure></div><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="trust-safety">Trust &amp; Safety</h2><p class="Body-module-scss-module__z40yvW__reading-column body-2 serif post-text">We’ve conducted extensive testing and evaluation of Sonnet 5, working with external experts to ensure it meets our standards for safety, security, and reliability. The accompanying <a href="https://www.anthropic.com/claude-sonnet-5-system-card">system card</a> covers safety results in depth.</p><h2 class="Body-module-scss-module__z40yvW__reading-column headline-4 post-heading" id="hear-from-our-customers">Hear from our customers</h2><div class="Body-module-scss-module__z40yvW__media-column"><div class="Carousel-module-scss-module__hqckjG__carousel-wrapper"><div class="Carousel-module-scss-module__hqckjG__carousel-container"><div class="Carousel-module-scss-module__hqckjG__carousel-track"><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/18f900625532e1baaa3302bdf9539f73592bdf60-164x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 gives our agents a strong execution layer for multi-step software engineering work. It handles sustained coding, tool use, and debugging well across messy technical contexts, and has been especially useful for workflows where follow-through and technical grounding matter.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Zimu Li</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Member of Technical Staff</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/f343481e6a953bc7b5390e6d9f61cf387c2ceb11-103x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">We handed Claude Sonnet 5 a two-part job—update Salesforce account tiers, send a launch announcement to enterprise contacts—and it finished end to end. That used to stall halfway. For day-to-day automation, it’s a no-brainer</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Daniel Shepard</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Senior Engineer</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/40a2a6a28afd8ac8fbf0e764b6bbf4ebf06a1977-133x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 gets more done with less. Same output quality, fewer steps to get there. It refuses unsafe requests cleanly and consistently, too. At Lovable, we’re putting powerful tools in the hands of millions of builders. A model that knows when to say no is just as important as one that knows how to build.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Fabian Hedin</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Co-founder</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/73b380711885c6beb5270575119dbf31d7f71236-107x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">We ran Claude Sonnet 5 against dozens of our most challenging real pull requests, and it carried each one through to a tested, verified result on its own — freeing our engineers to focus on the judgment, the decision, and the final sign-off.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Yusuke Kaji</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">GM, AI for Business</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/0cd3307b18e36ea9146a2fa1cbcb2966c4ccfd36-107x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">I asked Claude Sonnet 5 to investigate a bug. Unprompted, it wrote a reproducing test, implemented the fix, then stashed it to confirm the bug came back without the change. All in a single pass.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Neel Chotai</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Rust Engineer and Software Engineer</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/f084c88e65466636019709c40cc477aadce2f718-151x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">With Claude Sonnet 5, agents stay on plan, follow our conventions, and ship clean multi-step changes, all at an efficient cost.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Sualeh Asif</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Co-founder</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/b6ba30deda95060f1a9ff19cf03b184d36529a2a-92x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 is at its best on brownfield code—race conditions, hidden tests, the parts nobody wants to touch. It traces a failure to its actual root cause and ships a durable fix instead of patching the symptom.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Dominic Elm</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Founding Engineer</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/b130e0ceb83f1514a925c7dc93f9768ed60232e3-85x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 sits on the Pareto frontier for Eve’s plaintiff-law tasks. We see the clearest gains in legal research and analysis, at a price-to-performance ratio that made the choice to migrate easy.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Mauricio Wulfovich</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Staff ML Engineer</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/d97a1e1d49be76a2b9cc3df04843793e9a803214-180x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">ClickHouse agents explore live data and produce insights on the fly, so time-to-insight matters when testing new models. Claude Sonnet 5 reasons in tighter steps and gets our users to answers noticeably faster. That speed is a difference our customers feel.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Ryadh Dahimene</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Director PM AI/ML</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/7504d4d2c41a3baa1ac5cb0d4e7bd19abc57e07d-105x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">At Pace, our computer-use agents run insurance workflows—submission intake, FNOL, loss runs—on the systems our operations teams already use. Claude Sonnet 5 consistently takes the right action and does it quickly, which is what real insurance work demands.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Eric He</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Member of Technical Staff</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/bf162513ba017e72d4e07b0cd7683b86c4c5bc88-60x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">For enterprise teams managing high-volume, complex workloads, Claude Sonnet 5 represents a genuine step forward — strong performance where it counts, with the speed and cost profile that makes scaling practical. On several complex tasks it exceeds the current frontier, while delivering fast responses at low costs. For enterprises running at scale, that&#x27;s a real operational win.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Ben Kus</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">Chief Technology Officer</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt=" logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/f0719d10f52d94453dfd4c51b4fb26395f277b25-111x64.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 handled the full range of coding tasks we tested it on, while resolving more issues. It&#x27;s a meaningful improvement to both quality and efficiency.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Manav Khurana</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">CPMO</span></cite></footer></blockquote></div><div class="Carousel-module-scss-module__hqckjG__carousel-item QuoteCarousel-module-scss-module__XVJWRG__with-logo bg-ivory-medium"><div class="QuoteCarousel-module-scss-module__XVJWRG__logo-container"><img alt="Kiro logo" loading="lazy" width="120" height="48" decoding="async" data-nimg="1" class="QuoteCarousel-module-scss-module__XVJWRG__company-logo" style="color:transparent" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/cfaee5416044c0e687cf04a0fd1f3f3f26025dfc-125x32.svg"/></div><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-mark-container"><svg class="Icon-module-scss-module__lqbdHG__icon" width="16" height="16" viewBox="0 0 37 31"><path d="M16.7999 4.81642C14.2986 5.45109 12.1333 6.45909 10.3039 7.84042C8.51192 9.18442 7.13059 10.8644 6.15992 12.8804C5.18926 14.8964 4.70392 17.1738 4.70392 19.7124H3.47192C3.47192 18.1444 3.95726 16.8938 4.92792 15.9604C5.93592 14.9898 7.27992 14.5044 8.95992 14.5044C10.3786 14.5044 11.6853 14.8591 12.8799 15.5684C14.0746 16.2778 15.0266 17.2298 15.7359 18.4244C16.4453 19.6191 16.7999 20.9258 16.7999 22.3444C16.7999 23.7631 16.4453 25.0698 15.7359 26.2644C15.0266 27.4591 14.0746 28.4111 12.8799 29.1204C11.6853 29.8298 10.3786 30.1844 8.95992 30.1844C7.24259 30.1844 5.69326 29.7364 4.31192 28.8404C2.96792 27.9444 1.90392 26.6938 1.11992 25.0884C0.373255 23.4831 -7.79331e-05 21.6911 -7.79331e-05 19.7124C-7.79331e-05 16.3524 0.690589 13.3097 2.07192 10.5844C3.49059 7.82175 5.45059 5.54442 7.95192 3.75242C10.4906 1.92309 13.4399 0.672418 16.7999 0.000417709V4.81642ZM36.9599 4.81642C34.4586 5.45109 32.2933 6.45909 30.4639 7.84042C28.6719 9.18442 27.2906 10.8644 26.3199 12.8804C25.3493 14.8964 24.8639 17.1738 24.8639 19.7124H23.6319C23.6319 18.1444 24.1173 16.8938 25.0879 15.9604C26.0959 14.9898 27.4399 14.5044 29.1199 14.5044C30.5386 14.5044 31.8453 14.8591 33.0399 15.5684C34.2346 16.2778 35.1866 17.2298 35.8959 18.4244C36.6053 19.6191 36.9599 20.9258 36.9599 22.3444C36.9599 23.7631 36.6053 25.0698 35.8959 26.2644C35.1866 27.4591 34.2346 28.4111 33.0399 29.1204C31.8453 29.8298 30.5386 30.1844 29.1199 30.1844C27.4026 30.1844 25.8533 29.7364 24.4719 28.8404C23.1279 27.9444 22.0639 26.6938 21.2799 25.0884C20.5333 23.4831 20.1599 21.6911 20.1599 19.7124C20.1599 16.3524 20.8506 13.3097 22.2319 10.5844C23.6506 7.82175 25.6106 5.54442 28.1119 3.75242C30.6506 1.92309 33.5999 0.672418 36.9599 0.000417709V4.81642Z" fill="currentColor"></path></svg></div><blockquote class="QuoteCarousel-module-scss-module__XVJWRG__quote-content"><div class="QuoteCarousel-module-scss-module__XVJWRG__quote-text body-2 serif">Claude Sonnet 5 is a strong agentic-coding model, delivering top-tier accuracy comparable to Opus-class models and a clear step-function improvement over Sonnet 4.6. It conducts thorough explorations and sustains focus noticeably longer on complex tasks.</div><footer class="QuoteCarousel-module-scss-module__XVJWRG__quote-footer"><cite class="QuoteCarousel-module-scss-module__XVJWRG__speaker-info body-3"><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-name">Deepak Singh</span><br/><span class="QuoteCarousel-module-scss-module__XVJWRG__speaker-title">VP</span><span class="QuoteCarousel-module-scss-module__XVJWRG__separator">, </span><span class="QuoteCarousel-module-scss-module__XVJWRG__company-name">Kiro</span></cite></footer></blockquote></div></div></div><div class="Carousel-module-scss-module__hqckjG__carousel-controls body-3"><div class="Carousel-module-scss-module__hqckjG__counter"><span>01</span> /<!-- --> <!-- -->13</div><div class="Carousel-module-scss-module__hqckjG__arrow-controls"><button class="Carousel-module-scss-module__hqckjG__arrow-button Carousel-module-scss-module__hqckjG__arrow-prev" disabled="" 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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></div></div></div></div></article></div></section><section class="LandingPageSection-module-scss-module__ZSMdoa__root bg-ivory-light" data-theme="ivory"><div class="PromptCards-module-scss-module__dTJ7xa__root page-wrapper" data-theme="light"><div class="SectionIntro-module-scss-module__i9TRza__root SectionIntro-module-scss-module__i9TRza__center"><h2 class="headline-4 SectionIntro-module-scss-module__i9TRza__title">See Claude in action</h2></div><div class="PromptCards-module-scss-module__dTJ7xa__cards-container"><div class="PromptCards-module-scss-module__dTJ7xa__cards-panel PromptCards-module-scss-module__dTJ7xa__active" role="tabpanel" 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Sonnet 5 delivers top-tier intelligence with optimal efficiency for high-volume use cases. We recommend Sonnet 5 for most AI applications where you need a balance of advanced capabilities and cost-efficient performance at scale. Common use cases include customer-facing agents, production coding workflows, content generation at scale, and real-time research tasks."}},{"@type":"Question","name":"How much does it cost to use Claude Sonnet 5?","acceptedAnswer":{"@type":"Answer","text":"Pricing depends on how you want to use Sonnet 5. To learn more, check out our pricing page."}}]}</script><div class="page-wrapper"><div class="DrawerList-module-scss-module__Xud4PW__container"><div class="DrawerList-module-scss-module__Xud4PW__header"><h2 class="headline-4">Frequently asked questions</h2></div><div class="DrawerList-module-scss-module__Xud4PW__items"><div class="DrawerList-module-scss-module__Xud4PW__drawerContainer"><input id="drawer-27d0bc4e-95f0-4d0d-800b-fc5df7de3932-1" class="DrawerList-module-scss-module__Xud4PW__drawerInput" type="checkbox" name="open-drawers"/><label for="drawer-27d0bc4e-95f0-4d0d-800b-fc5df7de3932-1" class="DrawerList-module-scss-module__Xud4PW__drawerLabel"><h3 class="headline-6 DrawerList-module-scss-module__Xud4PW__drawerTitle">When should I use Claude Sonnet 5?</h3><span class="ToggleIcon-module-scss-module__wUPPYa__toggleIcon DrawerList-module-scss-module__Xud4PW__drawerIcon" style="width:24px;height:24px"><span class="ToggleIcon-module-scss-module__wUPPYa__iconBar" style="width:13.5px;height:1.248px"></span><span class="ToggleIcon-module-scss-module__wUPPYa__iconBarVertical iconBarVertical" style="width:1.248px;height:13.5px"></span></span></label><div class="DrawerList-module-scss-module__Xud4PW__drawerContent"><div class="DrawerList-module-scss-module__Xud4PW__drawerPost"><p class="body-2 serif">We offer different models across the spectrum of speed, price, and performance. Sonnet 5 delivers top-tier intelligence with optimal efficiency for high-volume use cases. We recommend Sonnet 5 for most AI applications where you need a balance of advanced capabilities and cost-efficient performance at scale. Common use cases include customer-facing agents, production coding workflows, content generation at scale, and real-time research tasks.</p></div></div></div><div class="DrawerList-module-scss-module__Xud4PW__drawerContainer"><input id="drawer-27d0bc4e-95f0-4d0d-800b-fc5df7de3932-2" class="DrawerList-module-scss-module__Xud4PW__drawerInput" type="checkbox" name="open-drawers"/><label for="drawer-27d0bc4e-95f0-4d0d-800b-fc5df7de3932-2" class="DrawerList-module-scss-module__Xud4PW__drawerLabel"><h3 class="headline-6 DrawerList-module-scss-module__Xud4PW__drawerTitle">How much does it cost to use Claude Sonnet 5?</h3><span class="ToggleIcon-module-scss-module__wUPPYa__toggleIcon DrawerList-module-scss-module__Xud4PW__drawerIcon" style="width:24px;height:24px"><span class="ToggleIcon-module-scss-module__wUPPYa__iconBar" style="width:13.5px;height:1.248px"></span><span class="ToggleIcon-module-scss-module__wUPPYa__iconBarVertical iconBarVertical" style="width:1.248px;height:13.5px"></span></span></label><div class="DrawerList-module-scss-module__Xud4PW__drawerContent"><div class="DrawerList-module-scss-module__Xud4PW__drawerPost"><p class="body-2 serif">Pricing depends on how you want to use Sonnet 5. 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Sonnet 5 (orange line) is a strict improvement over Sonnet 4.6 (gray line) and covers a much wider range of cost-performance options than Opus 4.8 (yellow line). It provides substantially improved cost efficiency at medium effort; its higher-effort performance can match Opus 4.8 on some tasks. 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The charts show Sonnet 5 priced at $3 per million input tokens and $15 per million output tokens (existing standard pricing). Sonnet 5's introductory pricing of $2/MTok input and $10/MTok output has since been made permanent, so its actual cost is lower than shown here. Opus 4.8 is priced at $5/MTok input and $25/MTok output. xhigh = extra high effort level.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"columnWidth\":\"inline-width\"},\"mediaType\":\"image\",\"video\":{\"_type\":\"video\",\"autoplay\":false,\"columnWidth\":\"full-width\",\"loop\":false,\"muted\":true,\"showControls\":true}},\"tabTitle\":\"Agentic computer use\"}]},{\"_key\":\"e584a0313c60\",\"_type\":\"block\",\"children\":[{\"_key\":\"4d9fbf7ee25a\",\"_type\":\"span\",\"marks\":[],\"text\":\"Feedback from our early access partners has been consistent: Sonnet 5 is much more agentic than its predecessors. 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It handles sustained coding, tool use, and debugging well across messy technical contexts, and has been especially useful for workflows where follow-through and technical grounding matter.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Zimu Li\",\"speakerTitle\":\"Member of Technical Staff\"},{\"_key\":\"655fb30d1869\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-f343481e6a953bc7b5390e6d9f61cf387c2ceb11-103x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"a4545aaa1b35\",\"_type\":\"block\",\"children\":[{\"_key\":\"5324ba04de18\",\"_type\":\"span\",\"marks\":[],\"text\":\"We handed Claude Sonnet 5 a two-part job—update Salesforce account tiers, send a launch announcement to enterprise contacts—and it finished end to end. That used to stall halfway. For day-to-day automation, it’s a no-brainer.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Daniel Shepard\",\"speakerTitle\":\"Senior Engineer\"},{\"_key\":\"8bb1f60824e3\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-40a2a6a28afd8ac8fbf0e764b6bbf4ebf06a1977-133x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"31f8a5700495\",\"_type\":\"block\",\"children\":[{\"_key\":\"a06902686ee3\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 5 gets more done with less. Same output quality, fewer steps to get there. It refuses unsafe requests cleanly and consistently, too. At Lovable, we’re putting powerful tools in the hands of millions of builders. A model that knows when to say no is just as important as one that knows how to build.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Fabian Hedin\",\"speakerTitle\":\"Co-founder\"},{\"_key\":\"fdfd9a20c4c4\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-73b380711885c6beb5270575119dbf31d7f71236-107x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"fee1f39787dc\",\"_type\":\"block\",\"children\":[{\"_key\":\"62d9897e1152\",\"_type\":\"span\",\"marks\":[],\"text\":\"We ran Claude Sonnet 5 against dozens of our most challenging real pull requests, and it carried each one through to a tested, verified result on its own — freeing our engineers to focus on the judgment, the decision, and the final sign-off.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Yusuke Kaji\",\"speakerTitle\":\"GM, AI for Business\"},{\"_key\":\"d794f6ae4cc2\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-0cd3307b18e36ea9146a2fa1cbcb2966c4ccfd36-107x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"afb96e2a885f\",\"_type\":\"block\",\"children\":[{\"_key\":\"062110087366\",\"_type\":\"span\",\"marks\":[],\"text\":\"I asked Claude Sonnet 5 to investigate a bug. Unprompted, it wrote a reproducing test, implemented the fix, then stashed it to confirm the bug came back without the change. All in a single pass.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Neel Chotai\",\"speakerTitle\":\"Rust Engineer and Software Engineer\"},{\"_key\":\"d516dd1eebbf\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-f084c88e65466636019709c40cc477aadce2f718-151x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"192de92bca82\",\"_type\":\"block\",\"children\":[{\"_key\":\"995d0888af04\",\"_type\":\"span\",\"marks\":[],\"text\":\"With Claude Sonnet 5, agents stay on plan, follow our conventions, and ship clean multi-step changes, all at an efficient cost.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Sualeh Asif\",\"speakerTitle\":\"Co-founder\"},{\"_key\":\"4f4fbf4bdd0f\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-b6ba30deda95060f1a9ff19cf03b184d36529a2a-92x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"9e15a9434e1b\",\"_type\":\"block\",\"children\":[{\"_key\":\"af1de9549d37\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 5 is at its best on brownfield code—race conditions, hidden tests, the parts nobody wants to touch. It traces a failure to its actual root cause and ships a durable fix instead of patching the symptom.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Dominic Elm\",\"speakerTitle\":\"Founding Engineer\"},{\"_key\":\"7873486ec267\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-b130e0ceb83f1514a925c7dc93f9768ed60232e3-85x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"2f178efa2348\",\"_type\":\"block\",\"children\":[{\"_key\":\"ff018b8b6c7b\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 5 sits on the Pareto frontier for Eve’s plaintiff-law tasks. We see the clearest gains in legal research and analysis, at a price-to-performance ratio that made the choice to migrate easy.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Mauricio Wulfovich\",\"speakerTitle\":\"Staff ML Engineer\"},{\"_key\":\"93a37e9e9169\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-d97a1e1d49be76a2b9cc3df04843793e9a803214-180x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"2a35327ae46e\",\"_type\":\"block\",\"children\":[{\"_key\":\"dd8d874d65e8\",\"_type\":\"span\",\"marks\":[],\"text\":\"ClickHouse agents explore live data and produce insights on the fly, so time-to-insight matters when testing new models. Claude Sonnet 5 reasons in tighter steps and gets our users to answers noticeably faster. That speed is a difference our customers feel.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Ryadh Dahimene\",\"speakerTitle\":\"Director PM AI/ML\"},{\"_key\":\"9cd6a334bef6\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-7504d4d2c41a3baa1ac5cb0d4e7bd19abc57e07d-105x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"0a71388e2f90\",\"_type\":\"block\",\"children\":[{\"_key\":\"355c0e6bfc79\",\"_type\":\"span\",\"marks\":[],\"text\":\"At Pace, our computer-use agents run insurance workflows—submission intake, FNOL, loss runs—on the systems our operations teams already use. Claude Sonnet 5 consistently takes the right action and does it quickly, which is what real insurance work demands.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Eric He\",\"speakerTitle\":\"Member of Technical Staff\"},{\"_key\":\"f323e6f81ec3\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-bf162513ba017e72d4e07b0cd7683b86c4c5bc88-60x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"d202f1832769\",\"_type\":\"block\",\"children\":[{\"_key\":\"0c76a1a9f43e\",\"_type\":\"span\",\"text\":\"For enterprise teams managing high-volume, complex workloads, Claude Sonnet 5 represents a genuine step forward — strong performance where it counts, with the speed and cost profile that makes scaling practical. On several complex tasks it exceeds the current frontier, while delivering fast responses at low costs. For enterprises running at scale, that's a real operational win.\"}],\"style\":\"normal\"}],\"speakerName\":\"Ben Kus\",\"speakerTitle\":\"Chief Technology Officer\"},{\"_key\":\"453c319c9615\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-f0719d10f52d94453dfd4c51b4fb26395f277b25-111x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"b3e40335478a\",\"_type\":\"block\",\"children\":[{\"_key\":\"95e07211726d\",\"_type\":\"span\",\"text\":\"Claude Sonnet 5 handled the full range of coding tasks we tested it on, while resolving more issues. It's a meaningful improvement to both quality and efficiency.\"}],\"style\":\"normal\"}],\"speakerName\":\"Manav Khurana\",\"speakerTitle\":\"CPMO\"},{\"_key\":\"15c1133e2873\",\"_type\":\"quote\",\"companyName\":\"Kiro\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-cfaee5416044c0e687cf04a0fd1f3f3f26025dfc-125x32-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"0e955ac9fc82\",\"_type\":\"block\",\"children\":[{\"_key\":\"625929365f58\",\"_type\":\"span\",\"text\":\"Claude Sonnet 5 is a strong agentic-coding model, delivering top-tier accuracy comparable to Opus-class models and a clear step-function improvement over Sonnet 4.6. It conducts thorough explorations and sustains focus noticeably longer on complex tasks.\"}],\"style\":\"normal\"}],\"speakerName\":\"Deepak Singh\",\"speakerTitle\":\"VP\"}]},{\"_key\":\"6f7654cc4e85\",\"_type\":\"block\",\"children\":[{\"_key\":\"e744b9718367\",\"_type\":\"span\",\"marks\":[],\"text\":\"Safety evaluations\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"febb414c31e5\",\"_type\":\"block\",\"children\":[{\"_key\":\"f360aeb188cc\",\"_type\":\"span\",\"marks\":[],\"text\":\"Our pre-deployment safety evaluations found that Sonnet 5 was overall an improvement on Sonnet 4.6. On agentic safety, the model is better at refusing malicious requests and resisting hijack attempts in prompt injection attacks. The model shows lower rates of hallucination and sycophancy than Sonnet 4.6. On our automated behavioral audit, which tests a wide range of misaligned behaviors such as cooperation with misuse and deception, Sonnet 5 scored lower (that is, safer) overall. However, it did show somewhat higher rates of misaligned behavior on this assessment compared to the more capable Opus 4.8 and Claude Mythos Preview.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"7d9e08ba6a1d\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-d018d76aa03c0ef18abc8a68de8f6fcd51c0a574-3840x2160-png\",\"_type\":\"reference\"},\"caption\":[{\"_key\":\"a93712fc6458\",\"_type\":\"block\",\"children\":[{\"_key\":\"75d455b352f5\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Rates of misaligned behavior on our automated behavioral audit, which tests for a very wide range of undesirable behaviors across many situations and contexts (see Section 6.4 of the \"},{\"_key\":\"14a53fda8be0\",\"_type\":\"span\",\"marks\":[\"2dec08f6ab81\",\"em\"],\"text\":\"Sonnet 5 System Card\"},{\"_key\":\"87ae8a89bb35\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\" for a complete list and results for each specific behavior). Sonnet 5 shows an overall lower rate of misaligned behavior than Sonnet 4.6, though a higher rate than Mythos Preview and Opus 4.8.\"}],\"markDefs\":[{\"_key\":\"2dec08f6ab81\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-sonnet-5-system-card\"}],\"style\":\"normal\"}],\"columnWidth\":\"inline-width\",\"description\":\"Rates of misaligned behavior across Claude models\"},{\"_key\":\"1d6ff9b4e541\",\"_type\":\"block\",\"children\":[{\"_key\":\"497726d6842a\",\"_type\":\"span\",\"marks\":[],\"text\":\"We did not deliberately train Sonnet 5 on cybersecurity tasks. It can perform some routine, non-harmful cyber tasks, but on evaluations testing potentially dangerous cyber skills, such as developing software exploits, it shows substantially poorer performance than models such as Opus 4.8 and Mythos 5. Scores from one evaluation, which tested models’ ability to develop exploits for vulnerabilities in the Firefox browser, are shown in the chart below. Sonnet 5 was never able to develop a full working exploit, but it does show a slightly higher rate of \"},{\"_key\":\"065226e66861\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"partial\"},{\"_key\":\"4f218b0eb007\",\"_type\":\"span\",\"marks\":[],\"text\":\" success than Sonnet 4.6. This latter change is likely due to improvements in general intelligence rather than specific training.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"9c3cae87a7d4\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-ee9944c865937053bae293f057fffa478ee0f46b-3840x2160-png\",\"_type\":\"reference\"},\"caption\":[{\"_key\":\"11b3ccefa3ff\",\"_type\":\"block\",\"children\":[{\"_key\":\"847a44eb71ed\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Scores measuring models’ success at developing exploits for software vulnerabilities in Firefox 147 (this evaluation was developed \"},{\"_key\":\"d02f049799bd\",\"_type\":\"span\",\"marks\":[\"ce15cadb9a93\",\"em\"],\"text\":\"in collaboration with Mozilla\"},{\"_key\":\"630f9536844f\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"; all vulnerabilities have been patched in Firefox 148). For each model, the left-hand bar shows how often the model (without safeguards) developed a working exploit; the right-hand bar shows how often the model had partial success. Neither of the Sonnet models could successfully develop a working exploit (both scored 0.0%); Sonnet 5 showed a slightly higher partial success rate than Sonnet 4.6. Both Sonnet models have substantially poorer cyber capabilities than Opus 4.8 and Mythos 5. For full details, see Section 3.2.4 of the \"},{\"_key\":\"6af9d1bd5ad7\",\"_type\":\"span\",\"marks\":[\"0a2ba0806a32\",\"em\"],\"text\":\"Sonnet 5 System Card\"},{\"_key\":\"5aed6c6fb42d\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"ce15cadb9a93\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/mozilla-firefox-security\"},{\"_key\":\"0a2ba0806a32\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-sonnet-5-system-card\"}],\"style\":\"normal\"}],\"columnWidth\":\"inline-width\",\"description\":\"Scores measuring Claude models’ success at developing exploits for software vulnerabilities in Firefox 147\"},{\"_key\":\"407f2282c43a\",\"_type\":\"block\",\"children\":[{\"_key\":\"50a7a628c246\",\"_type\":\"span\",\"marks\":[],\"text\":\"Since Sonnet 5 is somewhat stronger than its predecessor on these tasks, we’ve launched it with cyber safeguards enabled by default. These \"},{\"_key\":\"6d2426310a2a\",\"_type\":\"span\",\"marks\":[\"4f1397dabed9\"],\"text\":\"safeguards\"},{\"_key\":\"3a3826ba40e8\",\"_type\":\"span\",\"marks\":[],\"text\":\"—which detect and block dangerous cyber usage in real time—are the same as those present in Claude Opus 4.7 and 4.8 (because we judged that the overall level of cybersecurity risk from Sonnet 5 was low, the safeguards are less strict than those launched with Fable 5, which block a much wider range of cybersecurity tasks).\"},{\"_key\":\"fafea525f484\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"1\"}],\"markDefs\":[{\"_key\":\"4f1397dabed9\",\"_type\":\"link\",\"href\":\"https://support.claude.com/en/articles/14604842-real-time-cyber-safeguards-on-claude\"}],\"style\":\"normal\"},{\"_key\":\"deaaf122b33b\",\"_type\":\"block\",\"children\":[{\"_key\":\"514a0b9f2a42\",\"_type\":\"span\",\"marks\":[],\"text\":\"Our full assessment of Sonnet 5 across many safety and capability evaluations is reported in the \"},{\"_key\":\"572f80d56972\",\"_type\":\"span\",\"marks\":[\"ba200da4f7c7\"],\"text\":\"Claude Sonnet 5 System Card\"},{\"_key\":\"d0c2211ffdff\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"ba200da4f7c7\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-sonnet-5-system-card\"}],\"style\":\"normal\"},{\"_key\":\"17a75f71324f\",\"_type\":\"block\",\"children\":[{\"_key\":\"76813e3a1d26\",\"_type\":\"span\",\"marks\":[],\"text\":\"Availability and pricing\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"be3256af2b93\",\"_type\":\"block\",\"children\":[{\"_key\":\"e8bbbebd4534\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 5 is available everywhere today at $2 per million input tokens and $10 per million output tokens\"},{\"_key\":\"cdb2f1537da1\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"2\"},{\"_key\":\"de31d19ea19b\",\"_type\":\"span\",\"marks\":[],\"text\":\". We’ve increased rate limits across Chat, Cowork, Claude Code, and the Claude Platform\"},{\"_key\":\"19e35990c863\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"3\"},{\"_key\":\"7e249bc9ec47\",\"_type\":\"span\",\"marks\":[],\"text\":\" to accommodate the higher token usage of higher effort levels; users can select whichever level makes sense for their particular project.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"f8c87438107a\",\"_type\":\"block\",\"children\":[{\"_key\":\"0e3f45193414\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"52e819dd8ae7\",\"_type\":\"block\",\"children\":[{\"_key\":\"8591648a4051\",\"_type\":\"span\",\"marks\":[],\"text\":\"Changelog\"}],\"markDefs\":[],\"style\":\"h4\"},{\"_key\":\"9f2078335c51\",\"_type\":\"block\",\"children\":[{\"_key\":\"b33c1631b0db\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Edit August 10, 2026: \"},{\"_key\":\"d24daccb7e19\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 5's introductory pricing of $2 per million input tokens and $10 per million output tokens is now permanent. The standard pricing of $3 input / $15 output previously set to take effect September 1 no longer applies. The pricing references in this post have been updated accordingly.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"44d9820bea8f\",\"_type\":\"block\",\"children\":[{\"_key\":\"e85dd6f2bf4d\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"97aaf7419857\",\"_type\":\"block\",\"children\":[{\"_key\":\"dfe27849b567\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Edit June 30, 2026:\"},{\"_key\":\"0c95bb38b915\",\"_type\":\"span\",\"marks\":[],\"text\":\" In the original version of this post, we included a cost-performance chart for the BrowseComp evaluation that was based on data from a simpler methodology that did not reflect the \"},{\"_key\":\"210a04aa0e12\",\"_type\":\"span\",\"marks\":[\"076cf7c9ee15\"],\"text\":\"standard methodology\"},{\"_key\":\"f4b3ea7b1fad\",\"_type\":\"span\",\"marks\":[],\"text\":\" we use for agentic search evaluations. This had the result of underestimating Sonnet 5's performance on the evaluation.\"}],\"markDefs\":[{\"_key\":\"076cf7c9ee15\",\"_type\":\"link\",\"href\":\"https://platform.claude.com/cookbook/evals-agentic-search-reproduce-agentic-search-benchmarks\"}],\"style\":\"normal\"},{\"_key\":\"c9fd8fb42da2\",\"_type\":\"block\",\"children\":[{\"_key\":\"6d9157204ef2\",\"_type\":\"span\",\"marks\":[],\"text\":\"We have now updated the chart so that it matches the methodology that we used and discussed in the \"},{\"_key\":\"abebbd4ecac9\",\"_type\":\"span\",\"marks\":[\"1ca43852e20c\"],\"text\":\"Sonnet 5 system card\"},{\"_key\":\"f7257e5f4b74\",\"_type\":\"span\",\"marks\":[],\"text\":\" (which used a 10M token budget with compaction and programmatic tool calling). We have also updated the surrounding text.\"}],\"markDefs\":[{\"_key\":\"1ca43852e20c\",\"_type\":\"link\",\"href\":\"https://www-cdn.anthropic.com/9e6a1044980d8c4ed85669faf9c2a8342e2e9f1e/Claude%20Sonnet%205%20System%20Card.pdf\"}],\"style\":\"normal\"}],\"cardPhoto\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-2039cc549c023bc855671308211d20d3382828a9-2880x1620-jpg\",\"_type\":\"reference\"}},\"directories\":[{\"_key\":\"news\",\"_type\":\"tag\",\"label\":\"News\",\"value\":\"news\"}],\"footnotesBody\":[{\"_key\":\"6864f6e05469\",\"_type\":\"block\",\"children\":[{\"_key\":\"3ea38013484d\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"1 \"},{\"_key\":\"6ca5de39f0f1\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 5 is part of our \"},{\"_key\":\"940fff72a938\",\"_type\":\"span\",\"marks\":[\"6cc119187f57\"],\"text\":\"Cyber Verification Program\"},{\"_key\":\"6b8f1946c7e8\",\"_type\":\"span\",\"marks\":[],\"text\":\", which is available today on the native Claude Platform, the Claude Platform on AWS, and Claude in Microsoft Foundry (hosted on Azure and Anthropic), and coming soon on Claude in Google Vertex. Organizations that are already enrolled in the Cyber Verification Program automatically have the same access on Sonnet 5, with no need to reapply. Overall, we recommend Claude Opus 4.8 for cybersecurity work that requires reduced guardrails.\"}],\"markDefs\":[{\"_key\":\"6cc119187f57\",\"_type\":\"link\",\"blank\":false,\"href\":\"https://support.claude.com/en/articles/14604842-real-time-cyber-safeguards-on-claude\"}],\"style\":\"normal\"},{\"_key\":\"041913eafb3b\",\"_type\":\"block\",\"children\":[{\"_key\":\"e587f0acd33e\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"2 \"},{\"_key\":\"8bf364d27aea\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 5 is an upgrade to Sonnet 4.6, but it uses an updated tokenizer that changes how the model processes text to improve performance (this is similar to the tokenizer change we introduced with Claude Opus 4.7). The tradeoff is that the same input can map to more tokens: roughly 1.0–1.35× depending on the content type.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"0d85b8d0f60a\",\"_type\":\"block\",\"children\":[{\"_key\":\"715cfeb26930\",\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"3 \"},{\"_key\":\"b067ceaa15cb\",\"_type\":\"span\",\"marks\":[],\"text\":\"On April 26, 2026, we raised Sonnet and Haiku rate limits at every usage tier and simplified to three tiers (Start, Build, and Scale) on the native Claude Platform. You can view your tier and current limits in the \"},{\"_key\":\"cc48f5147df8\",\"_type\":\"span\",\"marks\":[\"5ebd764099f0\"],\"text\":\"Claude Console\"},{\"_key\":\"683dd252ef77\",\"_type\":\"span\",\"marks\":[],\"text\":\" or read the \"},{\"_key\":\"d22aeed5f56d\",\"_type\":\"span\",\"marks\":[\"fac97efb7a95\"],\"text\":\"documentation\"},{\"_key\":\"416cadc362f6\",\"_type\":\"span\",\"marks\":[],\"text\":\" to learn more.\"}],\"markDefs\":[{\"_key\":\"5ebd764099f0\",\"_type\":\"link\",\"href\":\"https://platform.claude.com/settings/limits\"},{\"_key\":\"fac97efb7a95\",\"_type\":\"link\",\"href\":\"https://platform.claude.com/docs/en/api/rate-limits\"}],\"style\":\"normal\"},{\"_key\":\"9e0945688837\",\"_type\":\"block\",\"children\":[{\"_key\":\"4a0eb34afad0\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Humanity’s Last Exam:\"},{\"_key\":\"144f8b9389ac\",\"_type\":\"span\",\"marks\":[],\"text\":\" We updated the grader model for Humanity’s Last Exam and have updated the Sonnet 4.6 score to 34.6% (no tools) and 46.8% (with tools). This is the reason the score differs from that reported in the \"},{\"_key\":\"7440d6a9ab00\",\"_type\":\"span\",\"marks\":[\"c4518bef3883\"],\"text\":\"Sonnet 4.6 launch blog\"},{\"_key\":\"f19393ee4c18\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"c4518bef3883\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/claude-sonnet-4-6\"}],\"style\":\"normal\"},{\"_key\":\"f2f68e9e4688\",\"_type\":\"block\",\"children\":[{\"_key\":\"0cc9baebc5c5\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"OSWorld-Verified:\"},{\"_key\":\"833f0845087e\",\"_type\":\"span\",\"marks\":[],\"text\":\" We made changes to how we run the OSWorld-Verified evaluation to more accurately reflect the model’s performance in the real world, and have updated the Sonnet 4.6 score to 78.5%. This is the reason the score differs from that reported in the \"},{\"_key\":\"2171afde3e38\",\"_type\":\"span\",\"marks\":[\"8273b37ea9ca\"],\"text\":\"Sonnet 4.6 launch blog\"},{\"_key\":\"4ee99347b94e\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"8273b37ea9ca\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/claude-sonnet-4-6\"}],\"style\":\"normal\"}],\"footnotesTitle\":\"Footnotes\",\"hero\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-2039cc549c023bc855671308211d20d3382828a9-2880x1620-jpg\",\"_type\":\"reference\"}},\"illustration\":{\"backgroundColor\":\"default\"},\"meta\":{\"robotsIndexable\":true,\"seoDescription\":\"Our most agentic Sonnet yet, with top-tier intelligence for coding and everyday professional work.\",\"seoTitle\":\"Introducing Claude Sonnet 5\",\"socialImage\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-2039cc549c023bc855671308211d20d3382828a9-2880x1620-jpg\",\"_type\":\"reference\"},\"description\":\"A mix of flowers and leaves forming the number 5\"}},\"publishedOn\":\"2026-06-30T18:00:00.000Z\",\"showSidebar\":false,\"slug\":{\"_type\":\"slug\",\"current\":\"claude-sonnet-5\"},\"subjects\":[{\"_key\":\"product\",\"_type\":\"tag\",\"label\":\"Product\",\"value\":\"product\"}],\"summary\":\"Sonnet 5 delivers frontier performance across coding, agents, and professional work at scale.\",\"title\":\"Introducing Claude Sonnet 5\"},\"summary\":\"Our most agentic Sonnet yet. It brings performance that recently required larger, more expensive models to coding, tool use, and everyday work, at Sonnet’s speed and price.\",\"title\":\"Claude Sonnet 5\"},{\"_key\":\"86b9f7165dc1\",\"date\":\"2026-02-17\",\"link\":{\"_createdAt\":\"2026-02-16T18:42:54Z\",\"_id\":\"241d5428-e51a-4f47-b74a-70f3677fb7e8\",\"_rev\":\"XTNRa73Wp2rdE7p1GF5RYR\",\"_system\":{\"base\":{\"id\":\"241d5428-e51a-4f47-b74a-70f3677fb7e8\",\"rev\":\"2sVq3tXWy8O0iDM0tgabtu\"}},\"_type\":\"post\",\"_updatedAt\":\"2026-07-21T23:52:43Z\",\"body\":[{\"_key\":\"eac7dd16c45f\",\"_type\":\"block\",\"children\":[{\"_key\":\"b647fbad940c\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Claude Sonnet 4.6 is our most capable Sonnet model yet\"},{\"_key\":\"72845792ef4b\",\"_type\":\"span\",\"marks\":[],\"text\":\". It’s a full upgrade of the model’s skills across coding, computer use, long-context reasoning, agent planning, knowledge work, and design. Sonnet 4.6 also features a 1M token context window in beta.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"0124f1ea3824\",\"_type\":\"block\",\"children\":[{\"_key\":\"cf01913044e5\",\"_type\":\"span\",\"marks\":[],\"text\":\"For those on our \"},{\"_key\":\"89570961f877\",\"_type\":\"span\",\"marks\":[\"d372d8b3dfd0\"],\"text\":\"Free and Pro plans\"},{\"_key\":\"907a377090d8\",\"_type\":\"span\",\"marks\":[],\"text\":\", Claude Sonnet 4.6 is now the default model in \"},{\"_key\":\"5e992c1ee976\",\"_type\":\"span\",\"marks\":[\"7d7f8308c729\"],\"text\":\"claude.ai\"},{\"_key\":\"7b9fe90a1fe8\",\"_type\":\"span\",\"marks\":[],\"text\":\" and \"},{\"_key\":\"a3979fe51f1c\",\"_type\":\"span\",\"marks\":[\"9ed29d831f36\"],\"text\":\"Claude Cowork\"},{\"_key\":\"ff9feed092de\",\"_type\":\"span\",\"marks\":[],\"text\":\". \"},{\"_key\":\"f3582e283ebd\",\"_type\":\"span\",\"marks\":[\"c7c1d7e3d7e4\"],\"text\":\"Pricing\"},{\"_key\":\"f7fb8783e3ce\",\"_type\":\"span\",\"marks\":[],\"text\":\" remains the same as Sonnet 4.5, starting at $3/$15 per million tokens.\"}],\"markDefs\":[{\"_key\":\"d372d8b3dfd0\",\"_type\":\"link\",\"href\":\"https://claude.com/pricing\"},{\"_key\":\"7d7f8308c729\",\"_type\":\"link\",\"href\":\"https://claude.ai\"},{\"_key\":\"9ed29d831f36\",\"_type\":\"link\",\"href\":\"https://claude.com/product/cowork\"},{\"_key\":\"c7c1d7e3d7e4\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://claude.com/pricing#api\"}],\"style\":\"normal\"},{\"_key\":\"ff7f774f0d85\",\"_type\":\"block\",\"children\":[{\"_key\":\"50bde0409e1c\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 4.6 brings much-improved coding skills to more of our users. Improvements in consistency, instruction following, and more have made developers with early access prefer Sonnet 4.6 to its predecessor by a wide margin. They often even prefer it to our smartest model from November 2025, Claude Opus 4.5.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"3a3143383387\",\"_type\":\"block\",\"children\":[{\"_key\":\"81beb4f6bf69\",\"_type\":\"span\",\"marks\":[],\"text\":\"Performance that would have previously required reaching for an Opus-class model—including on real-world, economically valuable \"},{\"_key\":\"e9a39d3042a2\",\"_type\":\"span\",\"marks\":[\"39ec9ab8636b\"],\"text\":\"office tasks\"},{\"_key\":\"a5a19d7f5767\",\"_type\":\"span\",\"marks\":[],\"text\":\"—is now available with Sonnet 4.6. The model also shows a major improvement in computer use skills compared to prior Sonnet models.\"}],\"markDefs\":[{\"_key\":\"39ec9ab8636b\",\"_type\":\"link\",\"href\":\"https://artificialanalysis.ai/evaluations/gdpval-aa\"}],\"style\":\"normal\"},{\"_key\":\"b94c06b6fa19\",\"_type\":\"block\",\"children\":[{\"_key\":\"8751d308e284\",\"_type\":\"span\",\"marks\":[],\"text\":\"As with every new Claude model, we’ve run \"},{\"_key\":\"e2ce666d17c6\",\"_type\":\"span\",\"marks\":[\"b7a1bc8f891d\"],\"text\":\"extensive safety evaluations\"},{\"_key\":\"a5b8f2a30187\",\"_type\":\"span\",\"marks\":[],\"text\":\" of Sonnet 4.6, which overall showed it to be as safe as, or safer than, our other recent Claude models. Our safety researchers concluded that Sonnet 4.6 has “a broadly warm, honest, prosocial, and at times funny character, very strong safety behaviors, and no signs of major concerns around high-stakes forms of misalignment.”\"}],\"markDefs\":[{\"_key\":\"b7a1bc8f891d\",\"_type\":\"link\",\"href\":\"https://anthropic.com/claude-sonnet-4-6-system-card\"}],\"style\":\"normal\"},{\"_key\":\"8c8c342ff43f\",\"_type\":\"block\",\"children\":[{\"_key\":\"5b38e785fe3d\",\"_type\":\"span\",\"marks\":[],\"text\":\"Computer use\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"433e259b839a\",\"_type\":\"block\",\"children\":[{\"_key\":\"f4d9189855f8\",\"_type\":\"span\",\"marks\":[],\"text\":\"Almost every organization has software it can’t easily automate: specialized systems and tools built before modern interfaces like APIs existed. To have AI use such software, users would previously have had to build bespoke connectors. But a model that can use a computer the way a person does changes that equation.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"84b7aff4935f\",\"_type\":\"block\",\"children\":[{\"_key\":\"dcd5a50bd829\",\"_type\":\"span\",\"marks\":[],\"text\":\"In October 2024, we were the \"},{\"_key\":\"8cd20547e0a6\",\"_type\":\"span\",\"marks\":[\"afd4c3d8bb06\"],\"text\":\"first to introduce\"},{\"_key\":\"c0bd5916a125\",\"_type\":\"span\",\"marks\":[],\"text\":\" a general-purpose computer-using model. At the time, we wrote that it was “still experimental—at times cumbersome and error-prone,” but we expected rapid improvement. \"},{\"_key\":\"ffd0c342f7b0\",\"_type\":\"span\",\"marks\":[\"1cb8f647f8f3\"],\"text\":\"OSWorld\"},{\"_key\":\"9f678a4cd95e\",\"_type\":\"span\",\"marks\":[],\"text\":\", the standard benchmark for AI computer use, shows how far our models have come. It presents hundreds of tasks across real software (Chrome, LibreOffice, VS Code, and more) running on a simulated computer. There are no special APIs or purpose-built connectors; the model sees the computer and interacts with it in much the same way a person would: clicking a (virtual) mouse and typing on a (virtual) keyboard.\"}],\"markDefs\":[{\"_key\":\"afd4c3d8bb06\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/3-5-models-and-computer-use\"},{\"_key\":\"1cb8f647f8f3\",\"_type\":\"link\",\"href\":\"https://os-world.github.io/\"}],\"style\":\"normal\"},{\"_key\":\"8ffe6ca52ce8\",\"_type\":\"block\",\"children\":[{\"_key\":\"c993adbaba3e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Across sixteen months, our Sonnet models have made steady gains on OSWorld. The improvements can also be seen beyond benchmarks: early Sonnet 4.6 users are seeing human-level capability in tasks like navigating a complex spreadsheet or filling out a multi-step web form, before pulling it all together across multiple browser tabs.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"95a09c4416a2\",\"_type\":\"block\",\"children\":[{\"_key\":\"dd3dc12b0203\",\"_type\":\"span\",\"marks\":[],\"text\":\"The model certainly still lags behind the most skilled humans at using computers. But the rate of progress is remarkable nonetheless. It means that computer use is much more useful for a range of work tasks—and that substantially more capable models are within reach.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"2d6ab866b5b3\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-1206645ef5a618dabce8587b472b21c67a30a0db-3840x1948-png\",\"_type\":\"reference\"},\"caption\":[{\"_key\":\"b8ca03a98b2b\",\"_type\":\"block\",\"children\":[{\"_key\":\"d20e6567a43e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Scores prior to Claude Sonnet 4.5 were measured on the original OSWorld; scores from Sonnet 4.5 onward use OSWorld-Verified. OSWorld-Verified (released July 2025) is an in-place upgrade of the original OSWorld benchmark, with updates to task quality, evaluation grading, and infrastructure.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"columnWidth\":\"full-width\",\"description\":\"Chart comparing several Sonnet model scores on the OSWorld benchmark\"},{\"_key\":\"4cd8b593bced\",\"_type\":\"block\",\"children\":[{\"_key\":\"8ec9203b4e4f\",\"_type\":\"span\",\"marks\":[],\"text\":\"At the same time, computer use poses risks: malicious actors can attempt to hijack the model by hiding instructions on websites in what’s known as a prompt injection attack. We’ve been working to improve our models’ resistance to prompt injections—our \"},{\"_key\":\"0a9da5fd5313\",\"_type\":\"span\",\"marks\":[\"5375ef1c8c5f\"],\"text\":\"safety evaluations\"},{\"_key\":\"92e3cb26f7bf\",\"_type\":\"span\",\"marks\":[],\"text\":\" show that Sonnet 4.6 is a major improvement compared to its predecessor, Sonnet 4.5, and performs similarly to Opus 4.6. You can find out more about how to mitigate prompt injections and other safety concerns in \"},{\"_key\":\"9b16e8c57639\",\"_type\":\"span\",\"marks\":[\"e94469bb3324\"],\"text\":\"our API docs\"},{\"_key\":\"4eeaebdf9613\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"5375ef1c8c5f\",\"_type\":\"link\",\"href\":\"https://anthropic.com/claude-sonnet-4-6-system-card\"},{\"_key\":\"e94469bb3324\",\"_type\":\"link\",\"href\":\"https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks\"}],\"style\":\"normal\"},{\"_key\":\"4f98507640a8\",\"_type\":\"video\",\"autoplay\":false,\"columnWidth\":\"full-width\",\"embedUrl\":\"https://youtu.be/2fHVTe7c--4\",\"loop\":false,\"muted\":true,\"showControls\":true},{\"_key\":\"362c5d21c965\",\"_type\":\"block\",\"children\":[{\"_key\":\"f671101f7738\",\"_type\":\"span\",\"marks\":[],\"text\":\"Evaluating Claude Sonnet 4.6\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"9d2883d9ceea\",\"_type\":\"block\",\"children\":[{\"_key\":\"376df7fbc414\",\"_type\":\"span\",\"marks\":[],\"text\":\"Beyond computer use, Claude Sonnet 4.6 has improved on benchmarks across the board. It approaches Opus-level intelligence at a price point that makes it more practical for far more tasks. You can find a full discussion of Sonnet 4.6’s capabilities and its safety-related behaviors in \"},{\"_key\":\"4eed81621bdb\",\"_type\":\"span\",\"marks\":[\"cc894244af27\"],\"text\":\"our system card\"},{\"_key\":\"2784f6a8a115\",\"_type\":\"span\",\"marks\":[],\"text\":\"; a summary and comparison to other recent models is below.\"}],\"markDefs\":[{\"_key\":\"cc894244af27\",\"_type\":\"link\",\"href\":\"https://anthropic.com/claude-sonnet-4-6-system-card\"}],\"style\":\"normal\"},{\"_key\":\"119c28e5f9e2\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-10b2602771d21378cd6d76628a081c8a76dcf216-2600x2960-png\",\"_type\":\"reference\"},\"columnWidth\":\"full-width\",\"description\":\"A table of popular benchmarks and Sonnet 4.6's relative performance compared to other frontier models\"},{\"_key\":\"d15af97bd912\",\"_type\":\"block\",\"children\":[{\"_key\":\"630434dcaa39\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"a80a92bbc160\",\"_type\":\"block\",\"children\":[{\"_key\":\"d35cdf4651fa\",\"_type\":\"span\",\"marks\":[],\"text\":\"In Claude Code, our early testing found that users preferred Sonnet 4.6 over Sonnet 4.5 roughly 70% of the time. Users reported that it more effectively read the context before modifying code and consolidated shared logic rather than duplicating it. This made it less frustrating to use over long sessions than earlier models.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"baf87d154400\",\"_type\":\"block\",\"children\":[{\"_key\":\"63b050971738\",\"_type\":\"span\",\"marks\":[],\"text\":\"Users even preferred Sonnet 4.6 to Opus 4.5, our frontier model from November, 59% of the time. They rated Sonnet 4.6 as significantly less prone to overengineering and “laziness,” and meaningfully better at instruction following. They reported fewer false claims of success, fewer hallucinations, and more consistent follow-through on multi-step tasks.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"28f55f62379c\",\"_type\":\"block\",\"children\":[{\"_key\":\"afefd227d7c2\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 4.6’s 1M token context window is enough to hold entire codebases, lengthy contracts, or dozens of research papers in a single request. More importantly, Sonnet 4.6 \"},{\"_key\":\"c6882e7b58cf\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"reasons effectively\"},{\"_key\":\"7a0276c4fa05\",\"_type\":\"span\",\"marks\":[],\"text\":\" across all that context. This can make it much better at long-horizon planning. We saw this particularly clearly in the \"},{\"_key\":\"092c40729a90\",\"_type\":\"span\",\"marks\":[\"cfba19052b09\"],\"text\":\"Vending-Bench Arena\"},{\"_key\":\"6ec4f0892fd9\",\"_type\":\"span\",\"marks\":[],\"text\":\" evaluation, which tests how well a model can run a (simulated) business over time—and which includes an element of competition, with different AI models facing off against each other to make the biggest profits.\"}],\"markDefs\":[{\"_key\":\"cfba19052b09\",\"_type\":\"link\",\"href\":\"https://andonlabs.com/evals/vending-bench-arena\"}],\"style\":\"normal\"},{\"_key\":\"b22069851c84\",\"_type\":\"block\",\"children\":[{\"_key\":\"b9f3d97cbc16\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 4.6 developed an interesting new strategy: it invested heavily in capacity for the first ten simulated months, spending significantly more than its competitors, and then pivoted sharply to focus on profitability in the final stretch. The timing of this pivot helped it finish well ahead of the competition.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"42a0fbd0b786\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-8c2855afe51fc0980596b5369b01b0b87eea7eaf-3840x2160-png\",\"_type\":\"reference\"},\"caption\":[{\"_key\":\"b50ec71216c0\",\"_type\":\"block\",\"children\":[{\"_key\":\"61361eeb6f08\",\"_type\":\"span\",\"marks\":[],\"text\":\"Sonnet 4.6 outperforms Sonnet 4.5 on Vending-Bench Arena by investing in capacity early, then pivoting to profitability in the final stretch.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"columnWidth\":\"full-width\"},{\"_key\":\"797047041033\",\"_type\":\"block\",\"children\":[{\"_key\":\"2c4754e06ced\",\"_type\":\"span\",\"marks\":[],\"text\":\"Early customers also reported broad improvements, with frontend code and financial analysis standing out. Customers independently described visual outputs from Sonnet 4.6 as notably more polished, with better layouts, animations, and design sensibility than those from previous models. Customers also needed fewer rounds of iteration to reach production-quality results.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"abcb778979b8\",\"_type\":\"quoteCarousel\",\"backgroundColor\":\"ivory-medium\",\"quotes\":[{\"_key\":\"828c01e8ab30\",\"_type\":\"quote\",\"companyName\":\"Databricks\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-4661a860c6f9018830d5f95679bbae5c2359109b-170x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"f0b39b1befec\",\"_type\":\"block\",\"children\":[{\"_key\":\"f515ca8e61f9\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 4.6 matches Opus 4.6 performance on OfficeQA, which measures how well a model can read enterprise documents (charts, PDFs, tables), pull the right facts, and reason from those facts. It’s a meaningful upgrade for document comprehension workloads.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Hanlin Tang\",\"speakerTitle\":\"CTO of Neural Networks\"},{\"_key\":\"f501629fcf4b\",\"_type\":\"quote\",\"companyName\":\"Replit\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-0504471eb7da85317c6def349d315e2f8be00b0f-127x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"b56f9b5db9e8\",\"_type\":\"block\",\"children\":[{\"_key\":\"7e3168ced7a9\",\"_type\":\"span\",\"marks\":[],\"text\":\"The performance-to-cost ratio of Claude Sonnet 4.6 is extraordinary—it’s hard to overstate how fast Claude models have been evolving in recent months. Sonnet 4.6 outperforms on our orchestration evals, handles our most complex agentic workloads, and keeps improving the higher you push the effort settings.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Michele Catasta\",\"speakerTitle\":\"President\"},{\"_key\":\"fd7cad86b128\",\"_type\":\"quote\",\"companyName\":\"Cursor\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-f084c88e65466636019709c40cc477aadce2f718-151x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"80bf492a6455\",\"_type\":\"block\",\"children\":[{\"_key\":\"b7fce1aa805e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 4.6 is a notable improvement over Sonnet 4.5 across the board, including long-horizon tasks and more difficult problems.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Michael Truell\",\"speakerTitle\":\"Co-founder and CEO\"},{\"_key\":\"a6b9801d6c96\",\"_type\":\"quote\",\"companyName\":\"GitHub\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-fc55f4db8afa5db479127fde5be3e492940f513d-94x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"064716ba25a8\",\"_type\":\"block\",\"children\":[{\"_key\":\"95369ef389de\",\"_type\":\"span\",\"marks\":[],\"text\":\"Out of the gate, Claude Sonnet 4.6 is already excelling at complex code fixes, especially when searching across large codebases is essential. For teams running agentic coding at scale, we’re seeing strong resolution rates and the kind of consistency developers need.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Joe Binder\",\"speakerTitle\":\"VP of Product\"},{\"_key\":\"2c30272e9ba6\",\"_type\":\"quote\",\"companyName\":\"Cognition\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-ad249bca4e8e195e08764efc43ecbc586ca37482-143x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"ee358d2ad74a\",\"_type\":\"block\",\"children\":[{\"_key\":\"09acf763fb43\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 4.6 has meaningfully closed the gap with Opus on bug detection, letting us run more reviewers in parallel, catch a wider variety of bugs, and do it all without increasing cost.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Scott Wu\",\"speakerTitle\":\"CEO\"},{\"_key\":\"ac31a1ea84d1\",\"_type\":\"quote\",\"companyName\":\"Windsurf\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-31f81aea37256a9c1600e17eb8916ac158443073-145x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"d4b559c111cb\",\"_type\":\"block\",\"children\":[{\"_key\":\"9cb8dfd4b629\",\"_type\":\"span\",\"marks\":[],\"text\":\"For the first time, Sonnet brings frontier-level reasoning in a smaller and more cost-effective form factor. It provides a viable alternative if you are a heavy Opus user.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Jeff Wang\",\"speakerTitle\":\"CEO\"},{\"_key\":\"3cd1b4bc28fe\",\"_type\":\"quote\",\"companyName\":\"Hebbia\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-ac77c59a0734811a829fab67d7ee8e801e45c50c-136x64-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"f9a5a2ce62ec\",\"_type\":\"block\",\"children\":[{\"_key\":\"67bf968824fc\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Sonnet 4.6 meaningfully improves the answer retrieval behind our core product—we saw a significant jump in answer match rate compared to Sonnet 4.5 in our Financial Services Benchmark, with better recall on the specific workflows our customers depend on.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Aabhas Sharma\",\"speakerTitle\":\"CTO\"},{\"_key\":\"cf8402c7ede4\",\"_type\":\"quote\",\"companyName\":\"Box\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-49b99af78924f43f878d39a25d574da293c68596-60x32-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"fd8501635145\",\"_type\":\"block\",\"children\":[{\"_key\":\"88a20cd4f385\",\"_type\":\"span\",\"marks\":[],\"text\":\"Box evaluated how Claude Sonnet 4.6 performs when tested on deep reasoning and complex agentic tasks across real enterprise documents. 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Our initial evals show significant improvements in multi-step reasoning and code comprehension—enabling Copilot's agentic experiences to handle complex, codebase-spanning tasks better.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"speakerName\":\"Mario Rodriguez\",\"speakerTitle\":\"Chief Product Officer\"},{\"_key\":\"10e266a8c153\",\"_type\":\"quote\",\"logo\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-daef759120b29e4db8ba4a5664d7574750964ab9-150x48-svg\",\"_type\":\"reference\"}},\"quote\":[{\"_key\":\"74cc156e95d3\",\"_type\":\"block\",\"children\":[{\"_key\":\"9e433d13dc1b0\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Claude Sonnet 4.5 is excellent at software development tasks\"},{\"_key\":\"18d64378a887\",\"_type\":\"span\",\"marks\":[],\"text\":\", learning our codebase patterns to deliver precise implementations. 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The \"},{\"_key\":\"2da440fccf651\",\"_type\":\"span\",\"marks\":[\"96c164dab91d\"],\"text\":\"Claude Agent SDK\"},{\"_key\":\"2da440fccf652\",\"_type\":\"span\",\"marks\":[],\"text\":\" is the same infrastructure that powers Claude Code, but it shows impressive benefits for a very wide variety of tasks, not just coding. As of today, you can use it to build your own agents.\"}],\"markDefs\":[{\"_key\":\"96c164dab91d\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://anthropic.com/engineering/building-agents-with-the-claude-agent-sdk\"}],\"style\":\"normal\"},{\"_key\":\"4caffbddcfa9\",\"_type\":\"block\",\"children\":[{\"_key\":\"4f2e4d3dd7d40\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"3a54f9b0e0b6\",\"_type\":\"block\",\"children\":[{\"_key\":\"02c8c0602f5c0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We built Claude Code because the tool we wanted didn’t exist yet. 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We encourage you to try it out on \"},{\"_key\":\"1f5971d9df351\",\"_type\":\"span\",\"marks\":[\"143c4e299baf\"],\"text\":\"claude.ai/imagine\"},{\"_key\":\"1f5971d9df352\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"143c4e299baf\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://claude.ai/imagine\"}],\"style\":\"normal\"},{\"_key\":\"b1a02d0042ad\",\"_type\":\"block\",\"children\":[{\"_key\":\"045c8e055d3b0\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"7996b4de5496\",\"_type\":\"block\",\"children\":[{\"_key\":\"afbe66e9cd060\",\"_type\":\"span\",\"marks\":[],\"text\":\"Further information\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"4defc54725f4\",\"_type\":\"block\",\"children\":[{\"_key\":\"85d608935abd0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We recommend upgrading to Claude Sonnet 4.5 for all uses. Whether you’re using Claude through our apps, our API, or Claude Code, Sonnet 4.5 is a drop-in replacement that provides much improved performance for the same price. Claude Code updates are available to all users. \"},{\"_key\":\"06cdbb0b94be\",\"_type\":\"span\",\"marks\":[\"263a8729b686\"],\"text\":\"Claude Developer Platform\"},{\"_key\":\"9fceeda21c91\",\"_type\":\"span\",\"marks\":[],\"text\":\" updates, including the Claude Agent SDK, are available to all developers. Code execution and file creation are available on all paid plans in the Claude apps.\"}],\"markDefs\":[{\"_key\":\"263a8729b686\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://claude.com/platform/api\"}],\"style\":\"normal\"},{\"_key\":\"ff1bdb345cb4\",\"_type\":\"block\",\"children\":[{\"_key\":\"6d893aeb7a4c0\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"259dc33403bf\",\"_type\":\"block\",\"children\":[{\"_key\":\"fd48ac6497e30\",\"_type\":\"span\",\"marks\":[],\"text\":\"For complete technical details and evaluation results, see our \"},{\"_key\":\"fd48ac6497e31\",\"_type\":\"span\",\"marks\":[\"61f902573c91\"],\"text\":\"system card\"},{\"_key\":\"fd48ac6497e32\",\"_type\":\"span\",\"marks\":[],\"text\":\", \"},{\"_key\":\"fd48ac6497e33\",\"_type\":\"span\",\"marks\":[\"5411e3adf1ed\"],\"text\":\"model page\"},{\"_key\":\"fd48ac6497e34\",\"_type\":\"span\",\"marks\":[],\"text\":\", and \"},{\"_key\":\"fd48ac6497e35\",\"_type\":\"span\",\"marks\":[\"76c1864d21de\"],\"text\":\"documentation\"},{\"_key\":\"fd48ac6497e36\",\"_type\":\"span\",\"marks\":[],\"text\":\". For more information, explore our \"},{\"_key\":\"fd48ac6497e37\",\"_type\":\"span\",\"marks\":[\"6032260296fa\"],\"text\":\"engineering\"},{\"_key\":\"fd48ac6497e38\",\"_type\":\"span\",\"marks\":[],\"text\":\" \"},{\"_key\":\"fd48ac6497e39\",\"_type\":\"span\",\"marks\":[\"64464684fc25\"],\"text\":\"posts\"},{\"_key\":\"fd48ac6497e310\",\"_type\":\"span\",\"marks\":[],\"text\":\" and research post on \"},{\"_key\":\"fd48ac6497e311\",\"_type\":\"span\",\"marks\":[\"cfac2a78c01f\"],\"text\":\"cybersecurity\"},{\"_key\":\"fd48ac6497e312\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"61f902573c91\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/claude-sonnet-4-5-system-card\"},{\"_key\":\"5411e3adf1ed\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/claude/sonnet\"},{\"_key\":\"76c1864d21de\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://docs.claude.com/en/docs/about-claude/models/overview\"},{\"_key\":\"6032260296fa\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/engineering/building-agents-with-the-claude-agent-sdk\"},{\"_key\":\"64464684fc25\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents\"},{\"_key\":\"cfac2a78c01f\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://red.anthropic.com/2025/ai-for-cyber-defenders\"}],\"style\":\"normal\"}],\"directories\":[{\"_key\":\"news\",\"_type\":\"tag\",\"label\":\"News\",\"value\":\"news\"}],\"footnotesBody\":[{\"_key\":\"7f24e6deaff7\",\"_type\":\"block\",\"children\":[{\"_key\":\"c6e8302f0fe2\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"1\"},{\"_key\":\"3507b4d319ba\",\"_type\":\"span\",\"marks\":[\"strong\",\"em\"],\"text\":\": \"},{\"_key\":\"d9a2cf13a15c\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\"Customers in the cybersecurity and biological research industries can work with their account teams to join our allowlist in the meantime.\"},{\"_key\":\"23ed238be3ad\",\"_type\":\"span\",\"marks\":[],\"text\":\"\\n\\n\"},{\"_key\":\"89b91a58c3d7\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Methodology\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"f31291e4d0b3\",\"_type\":\"block\",\"children\":[{\"_key\":\"430db722557b0\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"SWE-bench Verified\"},{\"_key\":\"430db722557b1\",\"_type\":\"span\",\"marks\":[],\"text\":\": All Claude results were reported using a simple scaffold with two tools—bash and file editing via string replacements. We report 77.2%, which was averaged over 10 trials, no test-time compute, and 200K thinking budget on the full 500-problem SWE-bench Verified dataset.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"bb7765f3008a\",\"_type\":\"block\",\"children\":[{\"_key\":\"fa21220511140\",\"_type\":\"span\",\"marks\":[],\"text\":\"The score reported uses a minor prompt addition: \\\"You should use tools as much as possible, ideally more than 100 times. You should also implement your own tests first before attempting the problem.\\\"\"}],\"level\":2,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"4e451979bb1b\",\"_type\":\"block\",\"children\":[{\"_key\":\"1a5ee48a60aa0\",\"_type\":\"span\",\"marks\":[],\"text\":\"A 1M context configuration achieves 78.2%, but we report the 200K result as our primary score as the 1M configuration was implicated in our recent \"},{\"_key\":\"1a5ee48a60aa1\",\"_type\":\"span\",\"marks\":[\"d3c3dd205da2\"],\"text\":\"inference issues\"},{\"_key\":\"1a5ee48a60aa2\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"level\":2,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"d3c3dd205da2\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.anthropic.com/engineering/a-postmortem-of-three-recent-issues\"}],\"style\":\"normal\"},{\"_key\":\"5e0012d733c3\",\"_type\":\"block\",\"children\":[{\"_key\":\"c486074cb26c0\",\"_type\":\"span\",\"marks\":[],\"text\":\"For our \\\"high compute\\\" numbers we adopt additional complexity and parallel test-time compute as follows:\"}],\"level\":2,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"cf85fd8197a7\",\"_type\":\"block\",\"children\":[{\"_key\":\"30cb3b40478e0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We sample multiple parallel attempts.\"}],\"level\":3,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"c158092646c0\",\"_type\":\"block\",\"children\":[{\"_key\":\"9c206e4ffcea0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We discard patches that break the visible regression tests in the repository, similar to the rejection sampling approach adopted by \"},{\"_key\":\"9c206e4ffcea1\",\"_type\":\"span\",\"marks\":[\"87cfec113a45\"],\"text\":\"Agentless\"},{\"_key\":\"9c206e4ffcea2\",\"_type\":\"span\",\"marks\":[],\"text\":\" (Xia et al. 2024); note no hidden test information is used.\"}],\"level\":3,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"87cfec113a45\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://arxiv.org/abs/2407.01489\"}],\"style\":\"normal\"},{\"_key\":\"c25208e7b536\",\"_type\":\"block\",\"children\":[{\"_key\":\"0fbe2f3fe0560\",\"_type\":\"span\",\"marks\":[],\"text\":\"We then use an internal scoring model to select the best candidate from the remaining attempts.\"}],\"level\":3,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"ea45359fe9f7\",\"_type\":\"block\",\"children\":[{\"_key\":\"4f2f27b76fdd0\",\"_type\":\"span\",\"marks\":[],\"text\":\"This results in a score of 82.0% for Sonnet 4.5.\"}],\"level\":3,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"ff16794c1cea\",\"_type\":\"block\",\"children\":[{\"_key\":\"909ac5e6fc980\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Terminal-Bench\"},{\"_key\":\"909ac5e6fc981\",\"_type\":\"span\",\"marks\":[],\"text\":\": All scores reported use the default agent framework (Terminus 2), with XML parser, averaging multiple runs during different days to smooth the eval sensitivity to inference infrastructure.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"11901673c3b3\",\"_type\":\"block\",\"children\":[{\"_key\":\"6f8cddd2c3c30\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"τ2-bench: \"},{\"_key\":\"6f8cddd2c3c31\",\"_type\":\"span\",\"marks\":[],\"text\":\"Scores were achieved using extended thinking with tool use and a prompt addendum to the Airline and Telecom Agent Policy instructing Claude to better target its known failure modes when using the vanilla prompt. A prompt addendum was also added to the Telecom User prompt to avoid failure modes from the user ending the interaction incorrectly.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"8a68be1172a6\",\"_type\":\"block\",\"children\":[{\"_key\":\"442ca2490cb80\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"AIME\"},{\"_key\":\"442ca2490cb81\",\"_type\":\"span\",\"marks\":[],\"text\":\": Sonnet 4.5 score reported using sampling at temperature 1.0. 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All Claude model results reported are with extended thinking (up to 64K) and Sonnet 4.5 is reported with interleaved thinking on.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"e2bb126b0d94\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://vals.ai\"}],\"style\":\"normal\"},{\"_key\":\"2cc63b1e8a16\",\"_type\":\"block\",\"children\":[{\"_key\":\"83ca521859c3\",\"_type\":\"span\",\"marks\":[],\"text\":\"All OpenAI scores reported from their \"},{\"_key\":\"7c4d139534f41\",\"_type\":\"span\",\"marks\":[\"203da3adcccb\"],\"text\":\"GPT-5 post\"},{\"_key\":\"7c4d139534f42\",\"_type\":\"span\",\"marks\":[],\"text\":\", \"},{\"_key\":\"7c4d139534f43\",\"_type\":\"span\",\"marks\":[\"722a64e888c8\"],\"text\":\"GPT-5 for developers post\"},{\"_key\":\"7c4d139534f44\",\"_type\":\"span\",\"marks\":[],\"text\":\", \"},{\"_key\":\"7c4d139534f45\",\"_type\":\"span\",\"marks\":[\"88b31a2264c2\"],\"text\":\"GPT-5 system card\"},{\"_key\":\"7c4d139534f46\",\"_type\":\"span\",\"marks\":[],\"text\":\" (SWE-bench Verified reported using n=500), \"},{\"_key\":\"7c4d139534f47\",\"_type\":\"span\",\"marks\":[\"57a27d141f81\"],\"text\":\"Terminal Bench leaderboard\"},{\"_key\":\"7c4d139534f48\",\"_type\":\"span\",\"marks\":[],\"text\":\" (using Terminus 2), and public \"},{\"_key\":\"7c4d139534f49\",\"_type\":\"span\",\"marks\":[\"6096b257a256\"],\"text\":\"Vals AI\"},{\"_key\":\"7c4d139534f410\",\"_type\":\"span\",\"marks\":[],\"text\":\" leaderboard. All Gemini scores reported from their \"},{\"_key\":\"7c4d139534f411\",\"_type\":\"span\",\"marks\":[\"4b08cfb22ed2\"],\"text\":\"model web page\"},{\"_key\":\"7c4d139534f412\",\"_type\":\"span\",\"marks\":[],\"text\":\", \"},{\"_key\":\"7c4d139534f413\",\"_type\":\"span\",\"marks\":[\"65b9356ad30d\"],\"text\":\"Terminal Bench leaderboard\"},{\"_key\":\"7c4d139534f414\",\"_type\":\"span\",\"marks\":[],\"text\":\" (using Terminus 1), and public \"},{\"_key\":\"7c4d139534f415\",\"_type\":\"span\",\"marks\":[\"c6a4e711e62b\"],\"text\":\"Vals AI\"},{\"_key\":\"7c4d139534f416\",\"_type\":\"span\",\"marks\":[],\"text\":\" leaderboard.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"203da3adcccb\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://openai.com/index/introducing-gpt-5/\"},{\"_key\":\"722a64e888c8\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://openai.com/index/introducing-gpt-5-for-developers/\"},{\"_key\":\"88b31a2264c2\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://cdn.openai.com/gpt-5-system-card.pdf\"},{\"_key\":\"57a27d141f81\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.tbench.ai/\"},{\"_key\":\"6096b257a256\",\"_type\":\"link\",\"blank\":true,\"href\":\"http://vals.ai\"},{\"_key\":\"4b08cfb22ed2\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://deepmind.google/models/gemini/pro/\"},{\"_key\":\"65b9356ad30d\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://www.tbench.ai/\"},{\"_key\":\"c6a4e711e62b\",\"_type\":\"link\",\"blank\":true,\"href\":\"https://vals.ai\"}],\"style\":\"normal\"}],\"footnotesTitle\":\"Footnotes\",\"illustration\":{\"backgroundColor\":\"clay\",\"illustration\":{\"_ref\":\"5fdd726b-c5b3-4fe2-8896-b54cdac19231\",\"_type\":\"reference\"}},\"meta\":{\"robotsIndexable\":true,\"seoDescription\":\"Claude Sonnet 4.5 is the best coding model in the world, strongest model for building complex agents, and best model at using computers. \",\"seoTitle\":\"Introducing Claude Sonnet 4.5\",\"socialImage\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-342f9b12d0438c0adac1cfae5824333be720e1de-2400x1260-png\",\"_type\":\"reference\"},\"description\":\"Abstract composition with flowing head silhouette and stylized hand gesture, holding geometric cube structure\"}},\"publishedOn\":\"2025-09-29T17:00:00.000Z\",\"showSidebar\":false,\"slug\":{\"_type\":\"slug\",\"current\":\"claude-sonnet-4-5\"},\"subjects\":[{\"_key\":\"announcements\",\"_type\":\"tag\",\"label\":\"Announcements\",\"value\":\"announcements\"}],\"summary\":\"Claude Sonnet 4.5 sets new benchmark records in coding, reasoning, and computer use while being Anthropic's most aligned model, accompanied by the release of the Claude Agent SDK for building capable agents\",\"title\":\"Introducing Claude Sonnet 4.5\"},\"noSnippet\":true,\"summary\":\"Sonnet 4.5 is the best model in the world for agents, coding, and computer use.  It’s also our most accurate and detailed model for long-running tasks, with enhanced domain knowledge in coding, finance, and cybersecurity.\",\"title\":\"Claude Sonnet 4.5 \"},{\"_key\":\"7d980a6d5387\",\"date\":\"2025-05-22\",\"link\":{\"_createdAt\":\"2025-05-21T03:29:12Z\",\"_id\":\"afe4f9e7-9b78-4528-aecd-43afc2036065\",\"_rev\":\"QnkPsvKZEt9LuBetGYl6bx\",\"_system\":{\"base\":{\"id\":\"afe4f9e7-9b78-4528-aecd-43afc2036065\",\"rev\":\"QnkPsvKZEt9LuBetGYkzyx\"}},\"_type\":\"post\",\"_updatedAt\":\"2025-09-16T16:56:20Z\",\"body\":[{\"_key\":\"d5c7fd06954b\",\"_type\":\"block\",\"children\":[{\"_key\":\"974df8b00e8a0\",\"_type\":\"span\",\"marks\":[],\"text\":\"Today, we’re introducing the next generation of Claude models: \"},{\"_key\":\"3579cea62272\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Claude Opus 4\"},{\"_key\":\"567b9e3820b9\",\"_type\":\"span\",\"marks\":[],\"text\":\" and \"},{\"_key\":\"b886488a1f82\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Claude Sonnet 4\"},{\"_key\":\"c71f6f9275bd\",\"_type\":\"span\",\"marks\":[],\"text\":\", setting new standards for coding, advanced reasoning, and AI agents. \"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"260ca87a7e9a\",\"_type\":\"block\",\"children\":[{\"_key\":\"828435d4c20e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Opus 4 is the world’s best coding model, with sustained performance on complex, long-running tasks and agent workflows. 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The Pro, Max, Team, and Enterprise Claude plans include both models and extended thinking, with Sonnet 4 also available to free users. Both models are available on our API, Amazon Bedrock, and Google Cloud's Vertex AI. Pricing remains consistent with previous Opus and Sonnet models: Opus 4 at $15/$75 per million tokens (input/output) and Sonnet 4 at $3/$15.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"1611114934ba\",\"_type\":\"block\",\"children\":[{\"_key\":\"9419ea1188ca\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude 4\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"88bfd958c523\",\"_type\":\"block\",\"children\":[{\"_key\":\"b1cea6bbe6650\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Opus 4 is our most powerful model yet and the best coding model in the world, leading on SWE-bench (72.5%) and Terminal-bench (43.2%). 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See appendix for more on methodology.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"description\":\"Bar chart comparison between Claude and other LLMs on software engineering tasks\",\"style\":\"inline\"},{\"_key\":\"03efe4438c12\",\"_type\":\"benchmarksTable\",\"benchmarksCSV\":{\"_type\":\"file\",\"asset\":{\"_ref\":\"file-0827c45e02042cf4c6158ea6c04abefedf2b728a-csv\",\"_type\":\"reference\"}},\"footnotes\":[{\"_key\":\"dd6cc405c7ed\",\"footnoteText\":[{\"_key\":\"88a88e4ababf\",\"_type\":\"block\",\"children\":[{\"_key\":\"16258ed42d42\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Methodology\"}],\"markDefs\":[],\"style\":\"normal\"}]},{\"_key\":\"b6cd1de95408\",\"cellItem\":\"subtitle\",\"columnIndex\":1,\"footnoteMarker\":\"1, 5\",\"footnoteText\":[{\"_key\":\"da4fcd1b1000\",\"_type\":\"block\",\"children\":[{\"_key\":\"b8dfb1ce491e\",\"_type\":\"span\",\"marks\":[],\"text\":\"1. Opus 4 and Sonnet 4 achieve 72.5% and 72.7% pass@1 with bash/editor tools (averaged over 10 trials, single-attempt patches, no test-time compute, using nucleus sampling with a top_p of 0.95).\"}],\"markDefs\":[],\"style\":\"normal\"}],\"rowIndex\":2},{\"_key\":\"6e89fb024fc0\",\"cellItem\":\"subtitle\",\"columnIndex\":1,\"footnoteMarker\":\"2, 5\",\"footnoteText\":[{\"_key\":\"b97d5a553211\",\"_type\":\"block\",\"children\":[{\"_key\":\"29d9e9346a4c\",\"_type\":\"span\",\"marks\":[],\"text\":\"2. Opus 4 and Sonnet 4 score 39.2% and 33.5% pass@1 with the same agent as non-Claude models, the above reported 43.2% and 35.5% with Claude Code as agent framework.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"rowIndex\":3},{\"_key\":\"85850da15673\",\"cellItem\":\"subtitle\",\"columnIndex\":1,\"footnoteMarker\":\"3\",\"footnoteText\":[{\"_key\":\"898d40384262\",\"_type\":\"block\",\"children\":[{\"_key\":\"a615bd58b633\",\"_type\":\"span\",\"marks\":[],\"text\":\"3. Claude scores on MMMLU are the average over 14 non-English languages.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"rowIndex\":7},{\"_key\":\"4a04eb607994\",\"cellItem\":\"subtitle\",\"columnIndex\":1,\"footnoteMarker\":\"4, 5\",\"footnoteText\":[{\"_key\":\"ebadc4c1f899\",\"_type\":\"block\",\"children\":[{\"_key\":\"7ba5b283c2c5\",\"_type\":\"span\",\"marks\":[],\"text\":\"4. Opus 4 and Sonnet 4 were run on AIME using nucleus sampling with a top_p of 0.95.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"rowIndex\":9},{\"_key\":\"fa4acbfedbcd\",\"cellItem\":\"subtitle\",\"columnIndex\":1,\"footnoteMarker\":\"5\",\"footnoteText\":[{\"_key\":\"94f0fed0ebe1\",\"_type\":\"block\",\"children\":[{\"_key\":\"5565ec9da2c5\",\"_type\":\"span\",\"marks\":[],\"text\":\"5. On SWE-Bench, Terminal-Bench, GPQA and AIME, we additionally report results that benefit from parallel test-time compute by sampling multiple sequences and selecting the single best via an internal scoring model.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"rowIndex\":4}],\"highlightColumns\":[\"Column 1\",\"Column 2\"],\"title\":\"Benchmarks Table\"},{\"_key\":\"d03c3a4c8db3\",\"_type\":\"block\",\"children\":[{\"_key\":\"1dc20e1b77df0\",\"_type\":\"span\",\"marks\":[],\"text\":\"Model improvements\"}],\"markDefs\":[],\"style\":\"h2\"},{\"_key\":\"2334d914a94f\",\"_type\":\"block\",\"children\":[{\"_key\":\"33ff86a67fe10\",\"_type\":\"span\",\"marks\":[],\"text\":\"In addition to extended thinking with tool use, parallel tool execution, and memory improvements, we’ve significantly reduced behavior where the models use shortcuts or loopholes to complete tasks. Both models are 65% less likely to engage in this behavior than Sonnet 3.7 on agentic tasks that are particularly susceptible to shortcuts and loopholes.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"72dbd45a5df8\",\"_type\":\"block\",\"children\":[{\"_key\":\"3c5c213876940\",\"_type\":\"span\",\"marks\":[],\"text\":\"Claude Opus 4 also dramatically outperforms all previous models on memory capabilities. When developers build applications that provide Claude local file access, Opus 4 becomes skilled at creating and maintaining 'memory files' to store key information. This unlocks better long-term task awareness, coherence, and performance on agent tasks—like Opus 4 creating a 'Navigation Guide' while playing Pokémon.\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"4ca5a293749f\",\"_type\":\"image\",\"asset\":{\"_ref\":\"image-e51564bb5ce9597dbfc59bbab13a0efbe25a7d66-1920x1080-gif\",\"_type\":\"reference\"},\"caption\":[{\"_key\":\"70cd9468ba0e\",\"_type\":\"block\",\"children\":[{\"_key\":\"2166ef57f84e\",\"_type\":\"span\",\"marks\":[],\"text\":\"Memory: When given access to local files, Claude Opus 4 records key information to help improve its game play. The notes depicted above are real notes taken by Opus 4 while playing Pokémon.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"description\":\"A visual note in Claude's memories that depicts a navigation guide for the game Pokemon Red.\",\"style\":\"inline\"},{\"_key\":\"d2a8386e884f\",\"_type\":\"block\",\"children\":[{\"_key\":\"66ddd79aa7bd0\",\"_type\":\"span\",\"marks\":[],\"text\":\"Finally, we've introduced thinking summaries for Claude 4 models that use a smaller model to condense lengthy thought processes. This summarization is only needed about 5% of the time—most thought processes are short enough to display in full. 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The model is encouraged to write down its thoughts as it solves the problem distinct from our usual thinking mode, during the multi-turn trajectories to best leverage its reasoning abilities. To accommodate the additional steps Claude incurs by utilizing more thinking, the maximum number of steps (counted by model completions) was increased from 30 to 100 (most trajectories completed under 30 steps with only one trajectory reaching above 50 steps).\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"c924c8fe1683\",\"_type\":\"block\",\"children\":[{\"_key\":\"a5030df3ee8a0\",\"_type\":\"span\",\"marks\":[],\"text\":\"SWE-bench methodology\"}],\"markDefs\":[],\"style\":\"h4\"},{\"_key\":\"ab4d92bb6911\",\"_type\":\"block\",\"children\":[{\"_key\":\"70046bf9410a0\",\"_type\":\"span\",\"marks\":[],\"text\":\"For the Claude 4 family of models, we continue to use the same simple scaffold that equips the model with solely the two tools described in our prior releases \"},{\"_key\":\"70046bf9410a1\",\"_type\":\"span\",\"marks\":[\"701556517208\"],\"text\":\"here\"},{\"_key\":\"70046bf9410a2\",\"_type\":\"span\",\"marks\":[],\"text\":\"—a bash tool, and a file editing tool that operates via string replacements. We no longer include the \"},{\"_key\":\"70046bf9410a3\",\"_type\":\"span\",\"marks\":[\"590e556f2bfe\"],\"text\":\"third ‘planning tool’\"},{\"_key\":\"70046bf9410a4\",\"_type\":\"span\",\"marks\":[],\"text\":\" used by Claude 3.7 Sonnet. On all Claude 4 models, we report scores out of the full 500 problems. Scores for OpenAI models are reported out of a \"},{\"_key\":\"70046bf9410a5\",\"_type\":\"span\",\"marks\":[\"3831708d807e\"],\"text\":\"477 problem subset\"},{\"_key\":\"70046bf9410a6\",\"_type\":\"span\",\"marks\":[],\"text\":\".\"}],\"markDefs\":[{\"_key\":\"701556517208\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/engineering/swe-bench-sonnet\"},{\"_key\":\"590e556f2bfe\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/engineering/claude-think-tool\"},{\"_key\":\"3831708d807e\",\"_type\":\"link\",\"href\":\"https://openai.com/index/gpt-4-1/\"}],\"style\":\"normal\"},{\"_key\":\"52eb5de38ca8\",\"_type\":\"block\",\"children\":[{\"_key\":\"351ec221f9030\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"79445b71c8af\",\"_type\":\"block\",\"children\":[{\"_key\":\"70d7fc10d41f0\",\"_type\":\"span\",\"marks\":[],\"text\":\"For our “high compute” numbers we adopt additional complexity and parallel test-time compute as follows:\"}],\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"484020743d74\",\"_type\":\"block\",\"children\":[{\"_key\":\"b2b7be3b2a860\",\"_type\":\"span\",\"marks\":[],\"text\":\"We sample multiple parallel attempts.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"afd5e167e664\",\"_type\":\"block\",\"children\":[{\"_key\":\"ad981c9ce35f0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We discard patches that break the visible regression tests in the repository, similar to the rejection sampling approach adopted by \"},{\"_key\":\"ad981c9ce35f1\",\"_type\":\"span\",\"marks\":[\"61db95542956\"],\"text\":\"Agentless (Xia et al. 2024)\"},{\"_key\":\"ad981c9ce35f2\",\"_type\":\"span\",\"marks\":[],\"text\":\"; note no hidden test information is used.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[{\"_key\":\"61db95542956\",\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2407.01489\"}],\"style\":\"normal\"},{\"_key\":\"8887dfcd7735\",\"_type\":\"block\",\"children\":[{\"_key\":\"8c1f75a9135d0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We then use an internal scoring model to select the best candidate from the remaining attempts.\"}],\"level\":1,\"listItem\":\"bullet\",\"markDefs\":[],\"style\":\"normal\"},{\"_key\":\"81858d54f01b\",\"_type\":\"block\",\"children\":[{\"_key\":\"3e175f836d210\",\"_type\":\"span\",\"marks\":[],\"text\":\"This results in a score of 79.4% and 80.2% for Opus 4 and Sonnet 4 respectively.\"}],\"markDefs\":[],\"style\":\"normal\"}],\"footnotesTitle\":\"Appendix\",\"hero\":{\"_type\":\"image\",\"asset\":{\"_ref\":\"image-9890d1bb39c15c41772af22d2282eb612469051c-2880x1620-jpg\",\"_type\":\"reference\"},\"description\":\"Illustration of Claude juggling several tasks in parallel\"},\"illustration\":{\"backgroundColor\":\"clay\",\"illustration\":{\"_ref\":\"DElaXo1A74rjItEmVaiMzr\",\"_type\":\"reference\"}},\"meta\":{\"robotsIndexable\":true,\"seoDescription\":\"Discover Claude 4's breakthrough AI capabilities. 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