{"id":18782,"date":"2026-09-02T14:47:14","date_gmt":"2026-09-02T18:47:14","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=18782"},"modified":"2026-09-02T14:47:15","modified_gmt":"2026-09-02T18:47:15","slug":"claude-fable-5-1","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/pt-br\/hub\/claude-fable-5-1","title":{"rendered":"Claude Fable 5.1: O que mudou, quanto custa e a quem se destina"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>Claude Fable 5.1 is Anthropic&#8217;s new general-availability model for coding, knowledge work, long-running problem solving, and scientific research.<\/strong> It was released on September 1, 2026. The headline change is not a higher API list price: input remains $10 per million tokens and output remains $50. Instead, Anthropic cut cache-read pricing to $0.25 per million tokens and estimates lower costs for workloads that reuse a lot of context.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That makes Fable 5.1 worth a serious look when your work depends on long documents, repeated context, or carefully constrained analysis. It does not make every access route identical, and it does not turn a vendor benchmark into an independent verdict. The useful question is simpler: does the route you plan to use give you the context, controls, privacy terms, and model behavior your workflow needs?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For teams that compare several model families in one place, <a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">O espa\u00e7o de trabalho multimodelo do GlobalGPT<\/a> is a practical option alongside the official route. It is useful for comparing available models on the same task; confirm the current directory and plan coverage before treating any platform as your Fable 5.1 access path.<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_popup&amp;login=1\"><img alt=\"\" fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"640\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1024x640.png\" class=\"wp-image-15877\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1024x640.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-300x187.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-768x480.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1536x960.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-2048x1279.png 2048w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-18x12.png 18w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-black-color has-luminous-vivid-amber-background-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_popup&amp;login=1\">Experimente mais de 100 modelos de ponta no GlobalGPT<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<nav class=\"cf51-toc\" aria-label=\"\u00cdndice\"><style>.cf51-toc{max-width:920px;margin:28px auto;padding:18px 20px;border:1px solid #d8e0eb;border-top:4px solid #2c8c94;background:#f7fafc;font-family:Arial,sans-serif;color:#17223b}.cf51-toc b{display:block;margin-bottom:10px;font-size:14px;text-transform:uppercase;letter-spacing:.05em;color:#41526d}.cf51-toc ol{margin:0;padding-left:20px;columns:2;gap:34px}.cf51-toc li{margin:7px 0}.cf51-toc a{color:#17223b;text-decoration:none;border-bottom:1px solid #97bec5}@media(max-width:640px){.cf51-toc ol{columns:1}}<\/style><b>Nesta p\u00e1gina<\/b><ol><li><a href=\"#what-is-claude-fable-5-1\">O que \u00e9<\/a><\/li><li><a href=\"#access\">Access routes<\/a><\/li><li><a href=\"#capabilities\">What it is built to do<\/a><\/li><li><a href=\"#pricing\">Pricing calculator<\/a><\/li><li><a href=\"#fable-5-comparison\">Fable 5 comparison<\/a><\/li><li><a href=\"#hands-on-tests\">Testes pr\u00e1ticos<\/a><\/li><li><a href=\"#limits\">Safety and limits<\/a><\/li><li><a href=\"#who-should-try\">Who should try it<\/a><\/li><li><a href=\"#faq\">PERGUNTAS FREQUENTES<\/a><\/li><\/ol><\/nav>\n\n\n\n<h2 id=\"what-is-claude-fable-5-1\" class=\"wp-block-heading\">What Is Claude Fable 5.1?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic introduced Fable 5.1 together with Mythos 5.1. The company describes them as the same underlying model with different safeguards and access rules: Fable is generally available, while Mythos is reserved for trusted-access programs in cybersecurity and life sciences. That distinction matters. Mythos capabilities should not be read as ordinary Fable access.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic positions Fable 5.1 around coding, knowledge work, long-running problem solving, and scientific research. Those are product claims, not a promise that every API venue exposes the same tools or limits.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What makes the release more consequential than a routine model rename is the combination of capability, effort controls, cache economics, and a more explicit deployment boundary. For a developer, that can mean a model that is easier to justify on repeated-context tasks. For a research team, it means separating a strong source-bounded answer from a workflow that still needs tool access, human review, and route-specific retention terms.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Use it for:<\/strong> code diagnosis, document synthesis, constraint-heavy analysis, and image-to-text interpretation.<\/li>\n\n\n\n<li><strong>Verify first:<\/strong> context limits, tool availability, data retention, region, and plan access on your chosen route.<\/li>\n\n\n\n<li><strong>Do not infer:<\/strong> that Mythos access, a cloud-specific specification, or an official benchmark automatically applies to your account.<\/li>\n<\/ul>\n\n\n\n<figure class=\"cf51-evidence\"><style>.cf51-evidence{max-width:920px;margin:28px auto;padding:16px;border:1px solid #d8e0eb;border-top:5px solid #df6b57;background:#fff;box-shadow:0 12px 28px rgba(25,39,67,.08);box-sizing:border-box;color:#17223b;font-family:Arial,sans-serif}.cf51-evidence *{box-sizing:border-box}.cf51-evidence .label{display:inline-block;margin:0 0 12px;padding:5px 9px;background:#f1f8f7;color:#176d65;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-evidence img{display:block;width:100%;height:auto;border:1px solid #d7deea;background:#f4f7fb}.cf51-evidence figcaption{margin:11px 3px 1px;font-size:14px;line-height:1.55;color:#59677d}<\/style><span class=\"label\">Comunicado oficial<\/span><img decoding=\"async\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/claude-fable-5-1-official-intro.webp\" alt=\"Anthropic announcement introducing Claude Fable 5.1 and Claude Mythos 5.1\" width=\"1440\" height=\"980\"><figcaption>Anthropic introduced Fable 5.1 and Mythos 5.1 as the same underlying model with different safeguards and access.<\/figcaption><\/figure>\n\n\n\n<div class=\"cf51-identity\"><style>.cf51-identity{max-width:920px;margin:24px auto;padding:20px;border:1px solid #d8e0eb;border-top:4px solid #df6b57;background:#fff;color:#17223b;font-family:Arial,sans-serif}.cf51-identity h3{margin:0 0 12px;font-size:20px}.cf51-identity .row{display:grid;grid-template-columns:1fr 1fr;gap:14px}.cf51-identity .cell{padding:14px;background:#f5f8fc;border-left:3px solid #2c8c94}.cf51-identity b{display:block;margin-bottom:6px}@media(max-width:640px){.cf51-identity .row{grid-template-columns:1fr}}<\/style><h3>Identity at a glance<\/h3><div class=\"row\"><div class=\"cell\"><b>Fable 5.1<\/b>General availability, with standard safeguards.<\/div><div class=\"cell\"><b>Mythos 5.1<\/b>Same underlying model, but trusted-access programs and different safeguards.<\/div><\/div><\/div>\n\n\n\n<h2 id=\"access\" class=\"wp-block-heading\">Where Can You Access Claude Fable 5.1?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic says Fable 5.1 is available across its platforms and names AWS, Google Cloud, and Microsoft Azure. On Anthropic&#8217;s API, the model identifier is <code>claude-fable-5-1<\/code>. On the AWS Bedrock route, documented context, output, input, cache, and retention rules belong to AWS documentation and should not be assumed for every other route.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Anthropic API:<\/strong> use the documented model identifier and current platform limits.<\/li>\n\n\n\n<li><strong>AWS Bedrock:<\/strong> AWS documents a 1M-token context window, 128K maximum output, text and image input, and text output for its route.<\/li>\n\n\n\n<li><strong>Other cloud routes:<\/strong> confirm region, plan, tool support, and data handling before migration.<\/li>\n<\/ul>\n\n\n\n<section class=\"cf51-route-card\" aria-label=\"Claude Fable 5.1 access route comparison\"><style>.cf51-route-card{max-width:920px;margin:26px auto;border:1px solid #d8e0eb;border-top:5px solid #df6b57;background:#fff;color:#17223b;font-family:Inter,Arial,sans-serif;box-shadow:0 10px 26px rgba(25,39,67,.06)}.cf51-route-card *{box-sizing:border-box}.cf51-route-card header{padding:18px 20px 15px;background:#f7fafc;border-bottom:1px solid #dfe6ef}.cf51-route-card h3{margin:0 0 5px;font-size:20px;line-height:1.25}.cf51-route-card header p{margin:0;color:#59677d;font-size:14px;line-height:1.5}.cf51-route-card .head,.cf51-route-card .row{display:grid;grid-template-columns:1.05fr 1.35fr 1.55fr}.cf51-route-card .head{padding:11px 20px;background:#17223b;color:#fff;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-route-card .row{border-bottom:1px solid #e4eaf1}.cf51-route-card .row:last-of-type{border-bottom:0}.cf51-route-card .cell{padding:16px 20px;line-height:1.5;font-size:14px}.cf51-route-card .route{font-weight:800;background:#fff8ed}.cf51-route-card .verified{background:#f1f8f7}.cf51-route-card .check{background:#f7f9fc;color:#41526d}.cf51-route-card .tag{display:inline-block;margin-bottom:6px;padding:3px 7px;background:#dff1ed;color:#176d65;font-size:10px;font-weight:800;letter-spacing:.05em;text-transform:uppercase}.cf51-route-card .check .tag{background:#e7edf5;color:#52657c}.cf51-route-card footer{padding:13px 20px;background:#fff8ed;color:#6d5736;font-size:13px;line-height:1.5}@media(max-width:680px){.cf51-route-card .head{display:none}.cf51-route-card .row{grid-template-columns:1fr}.cf51-route-card .cell{padding:13px 16px}.cf51-route-card .cell:before{display:block;margin-bottom:5px;font-size:10px;font-weight:800;letter-spacing:.06em;text-transform:uppercase;color:#59677d}.cf51-route-card .route:before{content:'Route'}.cf51-route-card .verified:before{content:'Verified here'}.cf51-route-card .check:before{content:'Check before use'}}<\/style><header><h3>Access routes: what transfers and what does not<\/h3><p>Read each specification as route-specific. A model name alone does not guarantee the same limits, inputs, tools, or data terms.<\/p><\/header><div class=\"head\"><div>Rota<\/div><div>Verified here<\/div><div>Verifique antes de usar<\/div><\/div><div class=\"row\"><div class=\"cell route\">API Antr\u00f3pica<\/div><div class=\"cell verified\"><span class=\"tag\">Verificado<\/span><br>Model ID: <code>claude-fable-5-1<\/code><\/div><div class=\"cell check\"><span class=\"tag\">Account-specific<\/span><br>Current limits, regions, and supported controls.<\/div><\/div><div class=\"row\"><div class=\"cell route\">AWS Bedrock<\/div><div class=\"cell verified\"><span class=\"tag\">Documented by AWS<\/span><br>1M context, 128K output, text\/image input, text output.<\/div><div class=\"cell check\"><span class=\"tag\">Account-specific<\/span><br>Region and account eligibility.<\/div><\/div><div class=\"row\"><div class=\"cell route\">Anywhere API test route<\/div><div class=\"cell verified\"><span class=\"tag\">Checked in this article<\/span><br>Exact model ID, structured JSON output, and PNG image input.<\/div><div class=\"cell check\"><span class=\"tag\">Not exposed in test<\/span><br>Filesystem tools and agent execution.<\/div><\/div><footer>Specifications belong to the route that documents or exposes them; they are not interchangeable.<\/footer><\/section>\n\n\n\n<h2 id=\"capabilities\" class=\"wp-block-heading\">What Is Fable 5.1 Built to Do?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The official positioning is broad, so it helps to separate what is documented from what a small hands-on check can establish. In our closed-input first-output tests through the Anywhere API, Fable 5.1 produced a minimal JavaScript fix that retained zero values, traced a document-set budget contradiction without resolving it by guesswork, kept an observational result non-causal, and extracted labeled values from a PNG chart. Those are useful signals for constrained tasks, not a reproduction of Anthropic&#8217;s long-running agent benchmarks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same API route did not expose filesystem or validator tools. For that reason, we did not claim a real autonomous-agent result.<\/p>\n\n\n\n<div class=\"cf51-map\"><style>.cf51-map{max-width:920px;margin:24px auto;border:1px solid #d8e0eb;background:#fff;font-family:Arial,sans-serif;color:#17223b}.cf51-map header{padding:16px 18px;background:#17223b;color:#fff}.cf51-map h3{margin:0;font-size:20px}.cf51-map .r{display:grid;grid-template-columns:1.2fr 1fr 1.1fr;gap:0;border-top:1px solid #e4eaf1}.cf51-map .r>*{padding:14px}.cf51-map .r b{display:block;margin-bottom:4px}.cf51-map .official{background:#f5f8fc}.cf51-map .check{background:#f0f8f7}.cf51-map .limit{background:#fff8ed}@media(max-width:700px){.cf51-map .r{grid-template-columns:1fr}.cf51-map .r>*{border-bottom:1px solid #e4eaf1}}<\/style><header><h3>Capability map: claim, check, boundary<\/h3><\/header><div class=\"r\"><div><b>Codifica\u00e7\u00e3o<\/b>Official focus area<\/div><div class=\"check\"><b>Marcado<\/b>Minimal diff retained zero values<\/div><div class=\"limit\"><b>Limite<\/b>No repository execution on this route<\/div><\/div><div class=\"r\"><div><b>Trabalho do conhecimento<\/b>Official focus area<\/div><div class=\"check\"><b>Marcado<\/b>Found a closed-source contradiction<\/div><div class=\"limit\"><b>Limite<\/b>Not a 1M-token test<\/div><\/div><div class=\"r\"><div><b>An\u00e1lise visual<\/b>AWS route documents image input<\/div><div class=\"check\"><b>Marcado<\/b>Read labeled PNG values<\/div><div class=\"limit\"><b>Limite<\/b>SVG was rejected by this API route<\/div><\/div><\/div>\n\n\n\n<h2 id=\"pricing\" class=\"wp-block-heading\">Claude Fable 5.1 Pricing and Cost Changes<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic lists Fable 5.1 at $10 per million input tokens and $50 per million output tokens, the same list rates as Fable 5. Cache reads are $0.25 per million tokens. Anthropic estimates about 25% lower cost for typical workloads and up to about 45% for complex, context-heavy agentic work; those are Anthropic estimates based on its stated workload assumptions, not a guaranteed discount.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The key variable is reuse. A one-off prompt will not benefit much from cache-read pricing, while a workflow that repeatedly sends a large brief, codebase summary, or policy pack can. For the older-generation context, see our <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-fable-5-pricing\/\">Guia de pre\u00e7os do Claude Fable 5<\/a>. If the comparison you are making is between separate subscriptions rather than API token billing, <a href=\"https:\/\/www.glbgpt.com\/order?inviter=hub_blog_top_pricing&amp;login=1\">Planos GlobalGPT<\/a> provide a separate multi-model subscription route; they are not a conversion of Anthropic API pricing.<\/p>\n\n\n\n<figure class=\"cf51-evidence\"><style>.cf51-evidence{max-width:920px;margin:28px auto;padding:16px;border:1px solid #d8e0eb;border-top:5px solid #df6b57;background:#fff;box-shadow:0 12px 28px rgba(25,39,67,.08);box-sizing:border-box;color:#17223b;font-family:Arial,sans-serif}.cf51-evidence *{box-sizing:border-box}.cf51-evidence .label{display:inline-block;margin:0 0 12px;padding:5px 9px;background:#fff0e6;color:#ae442e;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-evidence img{display:block;width:100%;height:auto;border:1px solid #d7deea;background:#f4f7fb}.cf51-evidence figcaption{margin:11px 3px 1px;font-size:14px;line-height:1.55;color:#59677d}<\/style><span class=\"label\">Official pricing<\/span><img decoding=\"async\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/claude-fable-5-1-official-pricing.webp\" alt=\"Official Claude Fable 5.1 pricing information\" width=\"1440\" height=\"980\"><figcaption>The official pricing evidence sits next to the calculator so its assumptions are easy to check.<\/figcaption><\/figure>\n\n\n\n<div class=\"cf51-price\"><style>.cf51-price{font-family:Arial,sans-serif;max-width:920px;margin:24px auto;padding:20px;border:1px solid #d8e0eb;border-top:4px solid #df6b57;color:#17223b;background:#fff}.cf51-price h3{margin:0 0 14px}.cf51-price .grid{display:grid;grid-template-columns:repeat(3,1fr);gap:12px}.cf51-price .n{padding:15px;background:#17223b;color:#fff}.cf51-price .n b{display:block;font-size:26px;color:#ffcf70}.cf51-price .calc{margin-top:16px;padding:16px;background:#f5f8fc}.cf51-price input{width:100%;accent-color:#df6b57}.cf51-price output{display:block;margin-top:12px;font-size:26px;font-weight:800}.cf51-price small{display:block;margin-top:14px;color:#59677d;line-height:1.5}@media(max-width:640px){.cf51-price .grid{grid-template-columns:1fr}}<\/style><h3>Official API list prices<\/h3><div class=\"grid\"><div class=\"n\"><b>$10<\/b>input \/ 1M tokens<\/div><div class=\"n\"><b>$50<\/b>output \/ 1M tokens<\/div><div class=\"n\"><b>$0.25<\/b>cache reads \/ 1M tokens<\/div><\/div><div class=\"calc\"><b>Monthly cost explorer<\/b><p>Move the slider for monthly input volume. Estimate assumes output equals 20% of input and 50% of input tokens are cache reads.<\/p><input id=\"cf51-vol\" type=\"range\" min=\"1\" max=\"100\" value=\"10\" step=\"1\" aria-label=\"Monthly input volume in millions of tokens\"><span id=\"cf51-label\">10M input tokens \/ month<\/span><output id=\"cf51-total\">Estimated API cost: $205.00<\/output><\/div><small>Illustrative calculation, not a quote: input is split 50% standard input and 50% cache read; output is assumed at 20% of input. Source rates: Anthropic. <script>(function(){var s=document.getElementById('cf51-vol'),l=document.getElementById('cf51-label'),o=document.getElementById('cf51-total');function u(){var v=Number(s.value),c=v*.5*10+v*.5*.25+v*.2*50;l.textContent=v+'M input tokens \/ month';o.textContent='Estimated API cost: $'+c.toFixed(2)}s.addEventListener('input',u);u()})()<\/script><\/small><\/div>\n\n\n\n<h2 id=\"fable-5-comparison\" class=\"wp-block-heading\">Claude Fable 5.1 vs Fable 5: Is It a Meaningful Upgrade?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic reports higher Fable 5.1 scores than Fable 5 on several task-specific evaluations, including 52.6% versus 24.7% on Terminal-Bench-Science, 55.8% versus 42.0% on Terminal-Bench, 31.4% versus 17.1% on AutomationBench, and 73.4% versus 70.5% on CursorBench. Its GDPval-AA v2 result is 1853 versus 1723. These are Anthropic-reported figures with their own harnesses and conditions, so they are useful context rather than an independently reproduced overall ranking.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We did not run a direct Fable 5 baseline in the same venue. The honest upgrade conclusion is therefore conditional: the official evidence points to task-specific gains and lower cache-read cost, while an independent Fable 5.1-versus-Fable 5 judgment remains unfinished.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If your choice is broader than this one upgrade, our <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-fable-5-vs-gpt-5-5\/\">Claude Fable 5 vs GPT 5.5 comparison<\/a> is a better next read than treating a single benchmark row as a universal ranking.<\/p>\n\n\n\n<figure class=\"cf51-evidence\"><style>.cf51-evidence{max-width:920px;margin:28px auto;padding:16px;border:1px solid #d8e0eb;border-top:5px solid #2c8c94;background:#fff;box-shadow:0 12px 28px rgba(25,39,67,.08);box-sizing:border-box;color:#17223b;font-family:Arial,sans-serif}.cf51-evidence *{box-sizing:border-box}.cf51-evidence .label{display:inline-block;margin:0 0 12px;padding:5px 9px;background:#f1f8f7;color:#176d65;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-evidence img{display:block;width:100%;height:auto;border:1px solid #d7deea;background:#f4f7fb}.cf51-evidence figcaption{margin:11px 3px 1px;font-size:14px;line-height:1.55;color:#59677d}<\/style><span class=\"label\">Official benchmark<\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/claude-fable-5-1-official-benchmarks.webp\" alt=\"Anthropic benchmark chart comparing Claude Fable 5.1 and Fable 5\" width=\"1440\" height=\"980\"><figcaption>Anthropic\u2019s published evaluations show task-specific gains; the comparison chart below makes the selected rows easier to scan.<\/figcaption><\/figure>\n\n\n\n<div class=\"cf51-bench\"><style>.cf51-bench{max-width:920px;margin:24px auto;padding:20px;background:#fff;border:1px solid #d8e0eb;border-top:4px solid #2c8c94;font-family:Arial,sans-serif;color:#17223b}.cf51-bench h3{margin:0 0 8px}.cf51-bench .row{display:grid;grid-template-columns:170px 1fr 1fr;gap:8px;align-items:center;margin:13px 0;font-size:13px}.cf51-bench .bar{height:26px;background:#edf2f6;position:relative}.cf51-bench .v{height:100%;display:flex;align-items:center;padding-left:7px;box-sizing:border-box;color:#17223b;font-weight:800;min-width:42px}.cf51-bench .new{background:#df6b57}.cf51-bench .old{background:#97bec5}.cf51-bench .legend{display:flex;gap:18px;font-size:12px;color:#59677d}.cf51-bench .dot{display:inline-block;width:10px;height:10px;margin-right:5px}.cf51-bench small{display:block;margin-top:14px;color:#59677d;line-height:1.5}@media(max-width:680px){.cf51-bench .row{grid-template-columns:1fr}.cf51-bench .bar{max-width:100%}}<\/style><h3>Anthropic-reported task results<\/h3><div class=\"legend\"><span><i class=\"dot\" style=\"background:#df6b57\"><\/i>Fable 5.1<\/span><span><i class=\"dot\" style=\"background:#97bec5\"><\/i>F\u00e1bula 5<\/span><\/div><div class=\"row\"><b>Terminal-Bench-Science<\/b><div class=\"bar\"><div class=\"v new\" style=\"width:52.6%\">52.6%<\/div><\/div><div class=\"bar\"><div class=\"v old\" style=\"width:24.7%\">24.7%<\/div><\/div><\/div><div class=\"row\"><b>Bancada de Terminal<\/b><div class=\"bar\"><div class=\"v new\" style=\"width:55.8%\">55.8%<\/div><\/div><div class=\"bar\"><div class=\"v old\" style=\"width:42%\">42.0%<\/div><\/div><\/div><div class=\"row\"><b>AutomationBench<\/b><div class=\"bar\"><div class=\"v new\" style=\"width:31.4%\">31.4%<\/div><\/div><div class=\"bar\"><div class=\"v old\" style=\"width:17.1%\">17.1%<\/div><\/div><\/div><div class=\"row\"><b>CursorBench<\/b><div class=\"bar\"><div class=\"v new\" style=\"width:73.4%\">73.4%<\/div><\/div><div class=\"bar\"><div class=\"v old\" style=\"width:70.5%\">70.5%<\/div><\/div><\/div><small>Source: Anthropic release page. These are vendor-reported evaluation results with task-specific conditions, not this article&#8217;s independent reproduction.<\/small><\/div>\n\n\n\n<h2 id=\"hands-on-tests\" class=\"wp-block-heading\">Hands-On Tests: What We Actually Checked<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">We used the Anywhere API route <code>\/v1\/chat\/completions<\/code> after verifying the returned model ID <code>claude-fable-5-1<\/code> and a small structured-output probe. Every result below is the first valid output from a frozen, closed-input task. SVG input was rejected before T05 reached the model, so the identical fixture was converted to PNG and run once; no valid result was replaced.<\/p>\n\n\n\n<div class=\"cf51-tests\"><style>.cf51-tests{font-family:Inter,Arial,sans-serif;max-width:980px;margin:24px auto;color:#17223b}.cf51-tests h3{margin:0 0 12px}.cf51-tests .card{border:1px solid #d8e0eb;border-left:5px solid #df6b57;background:#fff;margin:16px 0;padding:18px;box-shadow:0 7px 20px rgba(25,39,67,.05)}.cf51-tests .title{display:flex;justify-content:space-between;gap:12px;align-items:flex-start}.cf51-tests strong{font-size:16px}.cf51-tests .badge{flex:none;padding:4px 7px;background:#fff0e6;color:#ae442e;font-size:10px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-tests p{line-height:1.6}.cf51-tests .meta{display:block;margin:12px 0;color:#59677d;font-size:13px}.cf51-tests .output-label{margin:16px 0 0;padding:9px 11px;background:#17223b;color:#fff;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-tests pre{margin:0;white-space:pre-wrap;background:#f5f8fc;padding:14px;border:1px solid #e3eaf3;border-top:0;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;overflow:auto}.cf51-tests .skip{border-left-color:#8b96a8}.cf51-tests .skip .badge{background:#edf0f4;color:#59677d}@media(max-width:520px){.cf51-tests .title{display:block}.cf51-tests .badge{display:inline-block;margin-top:8px}}<\/style><h3>First-output evidence<\/h3><div class=\"card\"><div class=\"title\"><strong>T01 \u00b7 Minimal code diagnosis<\/strong><span class=\"badge\">Primeira sa\u00edda v\u00e1lida<\/span><\/div><p>The response identified why zero values disappeared, proposed a minimal code change, and explicitly did not claim a test run.<\/p><span class=\"meta\">Objective checks: 3\/3 \u00b7 16s<\/span><div class=\"output-label\">Complete model output<\/div><pre>{&#10;  \"root_cause\": \"scores.filter(Boolean) removes all falsy values, including the numeric score 0, so normalize([0, 4, null, undefined]) drops the 0 and returns [4] instead of [0, 4]. The predicate is too broad; it should only exclude null and undefined.\",&#10;  \"unified_diff\": \"--- a\/normalize.js\\n+++ b\/normalize.js\\n@@ -1,3 +1,3 @@\\n function normalize(scores) {\\n-  return scores.filter(Boolean);\\n+  return scores.filter((score) =&gt; score != null);\\n }\",&#10;  \"expected_test_results\": \"normalize([0, 4, null, undefined]) is expected to return [0, 4] because score != null is false only for null and undefined, so 0 is kept. normalize([7]) is expected to return [7], unchanged. These are expectations based on reading the code; no test run was performed.\",&#10;  \"remaining_risk\": \"Other falsy values that were previously filtered out (NaN, empty string, false) will now be retained, which matches the stated requirement (remove only null\/undefined) but may surface as a behavior change if any caller relied on the old filtering. If input can be non-array, the function still throws, as before.\"&#10;}<\/pre><\/div><div class=\"card\"><div class=\"title\"><strong>T02 \u00b7 Claim and contradiction audit<\/strong><span class=\"badge\">Primeira sa\u00edda v\u00e1lida<\/span><\/div><p>The response mapped supplied claims, found the $18,000\/$20,000 conflict, and kept the cause unresolved rather than inventing one.<\/p><span class=\"meta\">Objective checks: 3\/3 \u00b7 12s<\/span><div class=\"output-label\">Complete model output<\/div><pre>{&#10;  \"claims\": [{\"claim\":\"The pilot began on 3 March with 120 enrolled students.\",\"source\":\"Document A\",\"section\":\"A1\",\"status\":\"stated\"},{\"claim\":\"Attendance was recorded weekly.\",\"source\":\"Document A\",\"section\":\"A2\",\"status\":\"stated\"},{\"claim\":\"At the 31 March checkpoint, 96 of 120 enrolled students had attended at least three sessions.\",\"source\":\"Document B\",\"section\":\"B1\",\"status\":\"stated\"},{\"claim\":\"The programme budget was $18,000.\",\"source\":\"Document B\",\"section\":\"B2\",\"status\":\"contradicted\"},{\"claim\":\"The finance note lists an approved programme budget of $20,000.\",\"source\":\"Document C\",\"section\":\"C1\",\"status\":\"contradicted\"},{\"claim\":\"The note does not state whether the budget difference reflects a revision or a recording error.\",\"source\":\"Document C\",\"section\":\"C2\",\"status\":\"stated\"}],&#10;  \"contradictions\": [{\"topic\":\"Programme budget\",\"sources\":[{\"source\":\"Document B\",\"section\":\"B2\",\"value\":\"$18,000\"},{\"source\":\"Document C\",\"section\":\"C1\",\"value\":\"$20,000\"}],\"description\":\"Document B states the programme budget was $18,000, while Document C lists an approved programme budget of $20,000.\"}],&#10;  \"unresolved\": [{\"issue\":\"Whether the $2,000 difference between the $18,000 budget (Document B, B2) and the $20,000 approved budget (Document C, C1) reflects a revision or a recording error.\",\"source\":\"Document C\",\"section\":\"C2\",\"reason\":\"Document C explicitly states the note does not clarify the cause of the difference, and no other supplied document addresses it.\"}]&#10;}<\/pre><\/div><div class=\"card skip\"><div class=\"title\"><strong>T03 \u00b7 Agentic filesystem workflow<\/strong><span class=\"badge\">No model output<\/span><\/div><p>The verified chat endpoint did not expose file-writing or validator tools. The task was therefore not sent as a model-quality test, and no output or score is claimed.<\/p><span class=\"meta\">Route capability boundary, not a Fable 5.1 failure.<\/span><\/div><div class=\"card\"><div class=\"title\"><strong>T04 \u00b7 Closed-dataset reasoning<\/strong><span class=\"badge\">Primeira sa\u00edda v\u00e1lida<\/span><\/div><p>The response calculated the observed difference and refused the unsupported causal conclusion.<\/p><span class=\"meta\">Objective checks: 3\/3 \u00b7 27s<\/span><div class=\"output-label\">Complete model output<\/div><pre>{&#10;  \"mean_control\": {\"value\": 10, \"units\": \"points\"},&#10;  \"mean_treatment\": {\"value\": 14, \"units\": \"points\"},&#10;  \"difference\": {\"value\": 4, \"units\": \"points\", \"direction\": \"treatment minus control\"},&#10;  \"supported_conclusion\": \"In this dataset, the treatment group had a mean score 4 points higher than the control group (14 vs. 10 points).\",&#10;  \"unsupported_conclusion\": \"That the treatment caused the 4-point higher score; the groups were not randomly assigned and no baseline scores were collected, so the difference may reflect pre-existing differences between groups.\",&#10;  \"limitations\": [\"Non-random group assignment, so selection bias cannot be ruled out\", \"No baseline scores, so pre-existing differences between groups cannot be assessed\", \"Very small sample size (n=4 per group)\", \"No inferential statistics computed; the difference has not been tested for statistical significance\", \"Observational design does not support causal claims\"]&#10;}<\/pre><\/div><div class=\"card\"><div class=\"title\"><strong>T05 \u00b7 Chart extraction<\/strong><span class=\"badge\">Primeira sa\u00edda v\u00e1lida<\/span><\/div><p>The PNG run returned the labeled April values, distinguished visual estimates from labeled values, and avoided unsupported extrapolation.<\/p><span class=\"meta\">Objective checks: 3\/3 \u00b7 7s after a pre-output SVG format correction<\/span><div class=\"output-label\">Complete model output<\/div><pre>{&#10;  \"april_alpha\": 30,&#10;  \"april_beta\": 25,&#10;  \"trend\": \"Both Alpha and Beta increase steadily each month from Jan to Apr, with Alpha consistently about 5 hours above Beta (estimated visually: Jan ~15\/10, Feb ~20\/15, Mar ~25\/20, Apr 30\/25 as labeled).\",&#10;  \"unsupported\": [\"April values are explicitly labeled on the chart; Jan, Feb, and Mar values are visual estimates only\", \"No data beyond April or before January is shown, so no claims about other months can be made\", \"Units are labeled as study hours but no source, sample size, or definition of Alpha\/Beta groups is given\"]&#10;}<\/pre><\/div><\/div>\n\n\n\n<h2 id=\"limits\" class=\"wp-block-heading\">Safety, Data, and Compatibility Limits<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Fable 5.1 can be used to discover software vulnerabilities, but Anthropic says it is not intended for exploit development and may route some dual-use work to Opus. Life-science research and Mythos access have separate restrictions. Data retention is route-specific: AWS documentation, for example, should not be generalized to every Claude or third-party API path.<\/p>\n\n\n\n<figure class=\"cf51-evidence\"><style>.cf51-evidence{max-width:920px;margin:28px auto;padding:16px;border:1px solid #d8e0eb;border-top:5px solid #2c8c94;background:#fff;box-shadow:0 12px 28px rgba(25,39,67,.08);box-sizing:border-box;color:#17223b;font-family:Arial,sans-serif}.cf51-evidence *{box-sizing:border-box}.cf51-evidence .label{display:inline-block;margin:0 0 12px;padding:5px 9px;background:#f1f8f7;color:#176d65;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase}.cf51-evidence img{display:block;width:100%;height:auto;border:1px solid #d7deea;background:#f4f7fb}.cf51-evidence figcaption{margin:11px 3px 1px;font-size:14px;line-height:1.55;color:#59677d}<\/style><span class=\"label\">Official safeguards<\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/claude-fable-5-1-official-safety.webp\" alt=\"Official Claude Fable 5.1 safeguards and data handling notes\" width=\"1440\" height=\"980\"><figcaption>Safeguards, retention, and task-routing rules depend on the access path and eligibility.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic also notes gaps in its own alignment coverage for very long-context, multi-agent, and impossible-task settings. That is a reason to validate a high-stakes deployment with your own bounded task, not a reason to ignore the model&#8217;s documented improvements.<\/p>\n\n\n\n<h2 id=\"who-should-try\" class=\"wp-block-heading\">Who Should Try Claude Fable 5.1?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Try it when your work benefits from careful coding support, source-bounded synthesis, document contradiction checks, or image-to-text extraction. Verify first when you need a specific regional deployment, zero-retention terms, a tool-using agent, or a direct Fable 5 comparison. If you are choosing a general Claude plan rather than API access, the separate <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-ai-pricing-2026-the-ultimate-guide-to-plans-api-costs-and-limits\/\">Guia de pre\u00e7os da Claude AI<\/a> explains the plan-versus-API distinction.<\/p>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">Claude Fable 5.1 FAQ<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What is the practical difference between Claude Fable 5.1 and Mythos 5.1?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic describes them as the same underlying model with different safeguards and access rules. Fable 5.1 is the general-availability route discussed here. Mythos 5.1 is reserved for trusted-access programs in cybersecurity and life sciences, so Mythos capabilities are not a promise about a standard Fable account.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can I use Claude Fable 5.1 in the Claude app, or is it API-only?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Do not infer consumer-app access from an API or cloud announcement. Anthropic names its own platforms and major cloud providers as access routes, but the plan, region, quota, and tool set depend on the route. Check the exact account surface you intend to use before changing a workflow around it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is the correct Claude Fable 5.1 model ID?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic lists <code>claude-fable-5-1<\/code> for its API. Cloud providers can use route-specific identifiers, so copy the model ID from the provider documentation for the endpoint you are actually calling.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does Claude Fable 5.1 have a 1M-token context window?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AWS documents a 1M-token context window and a 128K maximum output for its Bedrock route. That is useful for Bedrock planning, but it should not be generalized to every Claude API, consumer, or third-party route without checking its own documentation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does Fable 5.1 accept images and generate images?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AWS documents text and image input with text output for the Bedrock route. That means image understanding is documented there; it does not mean that the model is an image generator or that every route accepts the same formats. In our bounded API check, PNG input worked after SVG was rejected by the request layer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When does the lower cache-read price make a difference?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It matters when the same large context is reused across requests: for example, a persistent codebase brief, a policy corpus, or a long research pack. For isolated short prompts, normal input and output volume dominate the bill. Use the calculator above as a planning estimate, then compare it with the actual caching behavior of your route.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can Fable 5.1 run a tool-using agent on its own?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">That depends on the environment around the model. A chat-completions endpoint can return a strong text answer without exposing file writing, validators, browsing, or a repository sandbox. Our test route did not expose those tools, so it was not used to claim autonomous-agent execution.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is Claude Fable 5.1 definitely better than Fable 5?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic reports task-specific gains and lower cache-read pricing. That is a strong reason to evaluate the upgrade on work that resembles your own, but it is not an independent universal winner verdict. This article did not run a same-venue Fable 5 baseline.<\/p>\n\n\n\n<script type=\"application\/ld+json\">{\n    \"@context\": \"https:\\\/\\\/schema.org\",\n    \"@type\": \"FAQPage\",\n    \"mainEntity\": [\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What is the practical difference between Claude Fable 5.1 and Mythos 5.1?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"Anthropic describes them as the same underlying model with different safeguards and access rules. Fable 5.1 is generally available, while Mythos 5.1 is reserved for trusted-access programs in cybersecurity and life sciences.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Can I use Claude Fable 5.1 in the Claude app, or is it API-only?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"Do not infer consumer-app access from an API or cloud announcement. Plan, region, quota, and tool availability depend on the exact route and account surface.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"What is the correct Claude Fable 5.1 model ID?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"Anthropic lists claude-fable-5-1 for its API. Cloud providers can use route-specific identifiers.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Does Claude Fable 5.1 have a 1M-token context window?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"AWS documents a 1M-token context window and 128K maximum output for its Bedrock route. Check route-specific documentation before generalizing that limit.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Does Fable 5.1 accept images and generate images?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"AWS documents text and image input with text output for Bedrock. Image understanding does not mean that the model is an image generator or that every route accepts the same formats.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"When does the lower cache-read price make a difference?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"It matters when the same large context is reused across requests, such as a persistent codebase brief or long research pack. Short isolated prompts benefit less.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Can Fable 5.1 run a tool-using agent on its own?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"That depends on the surrounding environment. A chat-completions endpoint can return text without exposing file writing, validators, browsing, or a repository sandbox.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Is Claude Fable 5.1 definitely better than Fable 5?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"Anthropic reports task-specific gains and lower cache-read pricing, but this article did not run a same-venue Fable 5 baseline and does not claim an independent universal winner.\"\n            }\n        }\n    ]\n}<\/script>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Fontes:<\/strong> <a href=\"https:\/\/www.anthropic.com\/claude-fable-and-mythos-5-1\">Anthropic release announcement<\/a>; <a href=\"https:\/\/aws.amazon.com\/blogs\/aws\/introducing-claude-fable-5-1\/\">AWS announcement<\/a>; <a href=\"https:\/\/aws.amazon.com\/bedrock\/claude-fable-5-1\/\">Amazon Bedrock model card<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Claude Fable 5.1 is Anthropic&#8217;s new general-availability model for coding, knowledge work, long-running problem solving, and scientific research. It was released on September 1, 2026. The headline change is not a higher API list price: input remains $10 per million tokens and output remains $50. Instead, Anthropic cut cache-read pricing to $0.25 per million tokens [&hellip;]<\/p>","protected":false},"author":7,"featured_media":18784,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"Claude Fable 5.1: Release, Pricing, Tests & Limits","_seopress_titles_desc":"Claude Fable 5.1 explained: release date, API pricing, access routes, official benchmarks, hands-on tests, safety limits, and who should use it.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-18782","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/posts\/18782","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/comments?post=18782"}],"version-history":[{"count":2,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/posts\/18782\/revisions"}],"predecessor-version":[{"id":18785,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/posts\/18782\/revisions\/18785"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/media\/18784"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/media?parent=18782"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/categories?post=18782"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/pt-br\/wp-json\/wp\/v2\/tags?post=18782"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}