Anthropic has officially launched Claude Opus 5.5, the first model in its new Claude 5.5 family, with a clear focus on coding, long-running AI agents, professional research and complex knowledge work.

The new model arrived on September 22, 2026, and Anthropic is making a different kind of argument with this release.

Instead of simply making Claude larger or more expensive, the company says Opus 5.5 can complete difficult tasks more efficiently than Opus 5 while approaching the performance of the much more expensive Claude Fable 5.1 on many workloads.

Anthropic says typical workloads can cost around 40% less than Opus 5, while output generation is more than 30% faster.

That makes Opus 5.5 particularly interesting for developers building AI coding agents and other systems that may run for hours, consume millions of tokens and repeatedly work with the same large context.

What Is Claude Opus 5.5?

Claude Opus 5.5 is Anthropic’s latest high-end general-purpose model.

It is designed for tasks that require more than a quick response, including large software projects, research, business analysis, computer use and autonomous workflows involving multiple tools.

Anthropic says the model performs at roughly the level of Claude Fable 5.1 on most work, despite sitting in a cheaper pricing tier.

This is important because Fable 5.1 was introduced as Anthropic’s model for demanding reasoning and long-horizon agentic work.

With Opus 5.5, much of that capability appears to be moving into a model that is easier to justify for everyday production use.

Claude Opus 5.5 Specifications

Claude Opus 5.5 offers a 1 million-token context window, making it suitable for very large codebases, extensive documentation and long-running conversations.

It supports up to 128,000 output tokens.

The model accepts text and images as input and produces text output.

Adaptive thinking is always enabled, while developers can control reasoning effort depending on how difficult the task is.

Available effort levels include:

Low, Medium, High, XHigh and Max.

The model’s reliable knowledge cutoff is June 2026.

For developers, the API model ID is:

claude-opus-5-5

These specifications make Opus 5.5 particularly suitable for tasks where the model needs to keep a large amount of information available while continuing to reason over it for an extended period.

Coding Is One of the Biggest Improvements

Software engineering is at the center of Anthropic’s Opus 5.5 launch.

The company is increasingly moving away from the idea of AI as simple code autocomplete.

Instead, it wants Claude to work on entire engineering tasks.

That might involve understanding a large repository, investigating a problem, editing dozens of files, running tests, checking the results and continuing until the issue is resolved.

Anthropic says one early tester used Opus 5.5 to complete a migration involving roughly 680,000 lines of code in less than a day.

Another test involved auditing and repairing a codebase containing around 200,000 lines. Anthropic says Opus 5.5 finished the work in less than three hours, while Opus 5 took considerably longer and used significantly more tokens.

These are examples selected by Anthropic rather than independent scientific evaluations, so they should not be treated as a guarantee of how every project will perform.

They do, however, show what the model is designed for.

The goal is no longer simply:

“Write this function.”

It is increasingly:

“Take responsibility for this engineering task and finish it.”

Strong Agentic Coding Performance

Anthropic also published benchmark results focused on agentic coding.

On Terminal-Bench 4.0, Anthropic reports a score of 66.4% for Opus 5.5.

For comparison, Anthropic lists:

Claude Fable 5.1 at 55.8%

Claude Opus 5 at 52.3%

and GPT-6 Astra at 57.9%.

On FrontierCode v1.1 Main, Anthropic reports Opus 5.5 at 54.4%, ahead of Fable 5.1 in its evaluation.

The company also reports improvements on CursorBench, another evaluation focused on real software-engineering work.

These results suggest Opus 5.5 is especially strong when coding requires the model to keep working through multiple steps rather than simply answer an isolated programming question.

There is an important caveat.

These benchmark figures were published as part of Anthropic’s own launch materials. Different models may have been tested using different reasoning settings, environments or tool configurations.

They are useful indicators, but developers should still test the models on their own workloads.

Long-Running AI Agents Are a Major Focus

Perhaps the more important story behind Opus 5.5 is not coding itself.

It is persistence.

Modern AI agents may need to work for several hours, interact with tools, make repeated decisions and recover from mistakes without losing track of the original objective.

Anthropic says early users have tested Opus 5.5 on workflows spanning many hours and multiple repositories.

The company highlights improvements in the model’s ability to delegate work to subagents, verify its own output and maintain focus across complex projects.

These capabilities matter because long-running agents create problems that normal chatbots do not face.

An agent must remember what it has already tried.

It must understand why something failed.

It must know when to change approach.

And it must avoid repeatedly making the same mistake.

A model that is slightly smarter on an academic benchmark but constantly loses context during a real project may ultimately be less useful than one that can reliably finish the job.

Lower Pricing Changes the Opus Equation

One of the biggest changes is pricing.

Claude Opus 5.5 costs:

$4 per million input tokens

and

$20 per million output tokens

Opus 5 costs $5 per million input tokens and $25 per million output tokens.

That means Anthropic has reduced the basic token price by 20%.

But the company says the real reduction in typical workload cost is closer to 40% because Opus 5.5 also uses fewer tokens to complete many tasks.

That distinction matters.

Two AI models can have identical token prices while producing very different final bills.

If one model needs fewer attempts, fewer tool calls and less output to successfully finish a task, it may be cheaper even if the advertised token price looks similar.

That is why AI companies are increasingly talking about cost per completed task instead of only cost per million tokens.

Prompt Caching Is Much Cheaper

The cache pricing may be particularly important for AI agents.

Opus 5.5 cache reads cost:

$0.20 per million tokens

compared with:

$0.50 per million tokens for Opus 5.

That represents a 60% reduction.

Cache writes are also cheaper, falling from $6.25 to $5 per million tokens for the standard cache-write tier highlighted by Anthropic.

Why does this matter?

Imagine a coding agent working on a large repository.

The repository structure, documentation, system instructions and many source files may remain unchanged while the agent performs dozens of different actions.

Without caching, the system may repeatedly pay to process the same information.

Caching allows that context to be reused at a much lower price.

For agents working with hundreds of thousands of tokens over long sessions, this can become a major part of the final cost.

Opus 5.5 vs Claude Fable 5.1

The most interesting comparison may actually be inside Anthropic’s own model family.

Claude Fable 5.1 costs:

$10 per million input tokens

and

$50 per million output tokens.

Opus 5.5 costs:

$4 input

and

$20 output.

That makes Opus 5.5 60% cheaper on standard input and output token pricing.

Yet Anthropic says Opus 5.5 reaches Fable 5.1-level performance on much of its real-world work and exceeds Fable on several coding evaluations published with the launch.

That does not mean Fable 5.1 suddenly has no purpose.

Fable remains aimed at especially demanding reasoning and long-horizon workloads.

But many developers who previously considered using Fable for difficult coding tasks may now find that Opus 5.5 provides a better balance between capability and cost.

Opus 5.5 vs GPT-6 Astra

OpenAI’s GPT-6 Astra remains one of the most important competitors at the high end of the market.

Astra costs:

$10 per million standard input tokens

and

$50 per million output tokens.

That puts it in roughly the same raw pricing class as Claude Fable 5.1 rather than Opus 5.5.

Opus 5.5, at $4/$20, is considerably cheaper on raw token pricing.

Anthropic also reports Opus 5.5 ahead of Astra on some agentic coding evaluations, including Terminal-Bench 4.0.

But Astra remains highly competitive in areas such as scientific agents, computer use and difficult end-to-end workflows.

The important point is that no single benchmark tells developers which model will be cheaper or more reliable for their application.

A model that costs more per token may still finish a task using fewer tokens.

Another model may be cheaper but require additional retries.

Real-world evaluation remains essential.

GPT-6 Sol Creates Another Competitor

The market changed again on the same day Opus 5.5 launched.

OpenAI also introduced GPT-6 Sol, a lower-cost member of the GPT-6 family.

GPT-6 Sol costs:

$2 per million input tokens

and

$10 per million output tokens.

That is exactly half the standard token price of Opus 5.5.

This makes Sol a potentially important competitor for developers who want strong coding and agent capabilities but do not need Astra-level performance.

The comparison between Opus 5.5 and GPT-6 Sol may therefore become more relevant for many production applications than Opus versus Astra.

Opus currently carries the higher raw API price, while Anthropic is emphasizing its efficiency and ability to complete agentic tasks using fewer steps and tokens.

Again, the winner may depend more on the workload than the price sheet.

Faster Output

Anthropic says Opus 5.5 generates output more than 30% faster than Opus 5.

There is also a Fast mode available for customers who need even lower latency.

Fast mode can provide up to approximately 2.5 times normal speed, according to Anthropic.

The tradeoff is price.

Fast mode costs:

$8 per million input tokens

and

$40 per million output tokens.

This allows developers to choose between lower-cost background processing and faster interactive experiences.

A coding agent running overnight may not need Fast mode.

An interactive development assistant where the user is waiting for every response might benefit from it.

Better Knowledge Work

Anthropic is not positioning Opus 5.5 as a coding-only model.

The company also reports strong results in professional research and knowledge work.

This includes tasks involving large-scale data collection, business analysis, document research and professional reporting.

The underlying pattern is similar to coding.

Anthropic is trying to make Claude better at completing an entire workflow rather than producing one isolated response.

A research agent might search for information, compare multiple sources, run calculations, inspect documents and then produce a finished report.

The ability to maintain context and use tools reliably becomes just as important as raw question-answering ability.

Better Computer Use

Computer interaction is another area Anthropic says has improved.

This matters because not every useful business application provides a clean API.

Sometimes an AI agent needs to interact with the same graphical software that a human uses.

That can include internal dashboards, browsers, development tools and other desktop applications.

Improved computer-use capabilities could therefore allow Claude agents to automate workflows that previously required custom software integrations.

Safety Matters More as Agents Become More Autonomous

More capable agents also create additional risks.

An AI system that only produces text has limited ability to affect external systems directly.

An agent capable of editing code, executing commands and interacting with software has much greater responsibility.

Anthropic says Opus 5.5 performed better than previous models on its internal behavioral evaluations and is less likely to take difficult-to-reverse actions or operate outside the boundaries of its assigned task.

The company also says the model has stronger resistance to prompt-injection attacks.

Opus 5.5 was tested before release by external evaluators including METR and Frontier Design.

That does not mean agent failures have been solved.

Anthropic itself acknowledges that highly capable models still have limitations.

But safety evaluation is increasingly becoming part of the product itself rather than something separate from model performance.

Availability

Claude Opus 5.5 is available through Anthropic’s Claude Platform using:

claude-opus-5-5

It is also available through major cloud providers, including:

Amazon Web Services

Google Cloud

and Microsoft Azure.

Amazon Bedrock lists the model as active with a September 22, 2026 launch date.

Anthropic has also confirmed that Claude Sonnet 5.5 and Claude Haiku 5.5 are planned for the coming weeks.

Those releases may ultimately be important for developers who need lower-cost models for much higher-volume applications.

Who Should Use Claude Opus 5.5?

Opus 5.5 appears particularly well suited for:

Large software projects.

Long-running coding agents.

Repository-wide debugging and migrations.

Professional research.

Business analysis.

Multi-step autonomous workflows.

Computer-use agents.

Tasks that repeatedly reuse large amounts of cached context.

For very simple summarization or high-volume classification, using a smaller model may still make more economic sense.

Final Thoughts

Claude Opus 5.5 may be one of Anthropic’s most practical frontier-model releases so far.

The interesting part is not simply that benchmark scores increased.

Anthropic has simultaneously lowered token prices, dramatically reduced cache-read costs, increased output speed and improved the model’s efficiency on long-running tasks.

That combination reflects where the AI industry is heading.

The competition is moving beyond:

“Which model is smartest?”

Developers increasingly want to know:

Which model can reliably finish the most useful work for the least money, time and human intervention?

Opus 5.5 is Anthropic’s latest answer.

And with GPT-6 Astra targeting maximum capability, GPT-6 Sol pushing prices lower and Fable 5.1 remaining available for demanding long-horizon reasoning, developers now have more meaningful choices than ever.

The best model will increasingly depend not on a single leaderboard score, but on the work you actually need it to finish.

Anthropic has introduced Claude Mythos 5.1, a specialized version of its newest frontier AI model designed for advanced cybersecurity defense and life-sciences research.

At first glance, the name makes Mythos 5.1 sound like a model sitting above Claude Fable 5.1 in Anthropic’s lineup.

That is not actually what is happening.

Anthropic says Claude Mythos 5.1 and Claude Fable 5.1 use the same underlying model. They share the same core intelligence, reasoning capabilities and long-context architecture.

The key difference is access and safeguards.

Fable 5.1 is the generally available model. Mythos 5.1 uses more permissive safeguards for vetted researchers and organizations working in areas where the standard restrictions could interfere with legitimate cybersecurity or life-sciences research.

That makes Mythos 5.1 one of Anthropic’s most unusual releases yet.

It is not designed for ordinary chatbot users. It is a restricted research model built for some of the most technically demanding—and potentially sensitive—applications of frontier AI.

What Is Claude Mythos 5.1?

Claude Mythos 5.1 launched on September 1, 2026, alongside Claude Fable 5.1.

Amazon describes it as Anthropic’s most capable model for cybersecurity defense and life-sciences research, including threat intelligence, vulnerability discovery, defensive red teaming, drug discovery and biodefense screening. Access is gated because many of these domains are inherently dual-use.

The easiest way to understand Anthropic’s new lineup is this:

Fable 5.1 = frontier model + general-purpose safeguards

Mythos 5.1 = the same frontier model + specialized safeguards for vetted professional research

This distinction matters because Mythos is not simply a premium model that anyone can unlock by paying more.

For most coding, reasoning, research and AI-agent workloads, Fable 5.1 already provides essentially the same underlying intelligence.

Mythos becomes relevant when specialized safety restrictions would otherwise prevent approved researchers from completing legitimate work.

Claude Mythos 5.1 Specifications

Mythos 5.1 has specifications built for extremely large and complex workloads.

• Launch date: September 1, 2026
• Context window: 1 million tokens
• Maximum output: 128,000 tokens
• Inputs: Text and images
• Output: Text
• Reasoning: Adaptive thinking
• Effort levels: Low, Medium, High, XHigh and Max
• Default effort: High
• Knowledge cutoff: June 2026
• Amazon Bedrock model ID: anthropic.claude-mythos-5-1

Adaptive thinking is always enabled on the Amazon Bedrock version, meaning the model can dynamically spend more reasoning effort on harder problems rather than treating every request the same way.

A one-million-token context window also gives Mythos enough room for very large research collections, code repositories, scientific papers, technical documentation and long-running agent histories.

Cybersecurity Is One of Mythos 5.1’s Main Jobs

Cybersecurity is one of the biggest reasons Mythos exists.

Anthropic says Mythos 5.1 demonstrates the strongest cyber capabilities of any model the company has released so far when evaluated without the additional cybersecurity safeguards used on the public Fable version.

That does not mean Anthropic is simply releasing an unrestricted cybersecurity model.

Access remains tightly controlled.

Instead, Mythos is intended to give vetted defenders more freedom to perform legitimate security work where aggressive model restrictions could become a problem.

Examples include vulnerability research, threat analysis, defensive testing, investigating complex software behavior and helping security teams understand weaknesses before attackers exploit them.

Anthropic has created a Cyber Verification Program (CVP) for this purpose. Mythos-class access is being added to the program for approved defensive-security organizations.

Anthropic’s own Claude Security product, which scans codebases for vulnerabilities and proposes patches for human review, is also now powered by Mythos 5.1.

Mythos 5.1 Shows an Advantage in Agentic Coding

Because Mythos and Fable share the same underlying model, their normal coding abilities should be similar.

But safeguards can affect benchmark results.

Anthropic reported 60.9% for Mythos 5.1 on Terminal-Bench 4.0, compared with 55.8% for Fable 5.1.

The company specifically explains that this difference does not come from Mythos using a smarter underlying model. Instead, some tasks can be affected by cybersecurity safeguards on Fable.

That distinction is important.

Mythos is not necessarily better at building a normal web application, debugging JavaScript or generating a backend API.

For normal software development, both models have essentially the same foundation.

Its advantage appears when specialized security restrictions become relevant to the task.

Life Sciences May Be Even More Interesting

Cybersecurity is only half of the Mythos story.

Anthropic is also positioning Mythos 5.1 as a serious research tool for advanced biology and life sciences.

The company has created a separate Life Sciences Verification Program (LSVP) to provide vetted professionals with access to research capabilities that are more restricted in generally available Claude models.

Anthropic developed the program in partnership with the U.S. government and says it plans to gradually expand access to a broader life-sciences community.

One of Anthropic’s headline experiments involved protein design.

Researchers gave Mythos 5.1 access to open-source protein-design and folding tools and experimentally tested the designs produced by the system.

Anthropic reports that Mythos generated high-affinity binders across multiple targets, with a hit rate approaching 50% across 12 targets in its experiment. The company notes that typical protein-design hit rates can be substantially lower.

The important point is not that an AI chatbot answered biology questions.

The model participated in an iterative scientific workflow whose outputs were later physically tested.

That is a much more ambitious use of AI.

Optimizing Scientific Software

Another example shows how coding and biology can overlap.

Anthropic says Mythos 5.1 optimized GPU kernels for seven open-source deep-learning models used in protein and genomics research.

According to the company, those optimizations made some models run as much as 2.5 times faster while preserving identical outputs.

Anthropic estimates that in certain genome-wide workloads, the resulting optimizations could reduce GPU costs by roughly 30% to 60%.

This illustrates one area where highly capable AI agents could have a practical impact on science without directly making scientific conclusions.

Instead, they can improve the tools scientists already use.

A researcher who previously needed performance-engineering specialists to optimize computational workloads may eventually be able to delegate part of that work to an AI agent.

Mythos 5.1 vs Fable 5.1

The biggest misunderstanding around this release will probably be the assumption that Mythos 5.1 is simply “Fable 5.1 Pro.”

It isn’t.

Here is the practical difference:

Anthropic explicitly describes Mythos 5.1 as identical to Fable 5.1 apart from the more permissive safeguards available to vetted users.

So if you are building websites, coding applications, analyzing documents or creating a normal AI agent, Mythos offers little reason to choose it over Fable.

For an approved cybersecurity laboratory or life-sciences organization, the story is different.

Is Mythos 5.1 More Powerful Than Fable 5.1?

Technically, no.

They use the same underlying model.

Practically, Mythos can be more capable for certain specialized workloads because fewer domain-specific safeguards interfere with legitimate approved tasks.

This explains why Mythos scored higher on some cybersecurity-heavy agentic evaluations even though its underlying intelligence is the same.

Think of it less as:

Fable → Mythos = intelligence upgrade

and more as:

Fable → Mythos = specialized research-access upgrade

That is a much more accurate description of Anthropic’s strategy.

Mythos 5.1 on Amazon Bedrock

Mythos 5.1 is also appearing through Amazon Bedrock for approved users.

AWS lists the model as an active Preview/Beta Service and provides a Bedrock model identifier of:

anthropic.claude-mythos-5-1

The model supports Bedrock features including response streaming, prompt caching, guardrails, knowledge bases, model evaluation, prompt management, flows and agents.

Prompt caching supports both five-minute and one-hour cache durations on Bedrock, with caching available across system prompts, messages and tools.

That combination is particularly relevant to persistent research agents that repeatedly work with the same large collection of documents or tools.

How Much Does Claude Mythos 5.1 Cost?

Anthropic’s public announcement focuses primarily on Fable 5.1 pricing because Mythos is distributed through restricted access programs.

A detailed Fable/Mythos comparison published by Ampere reports that the two share the same standard pricing structure:

$10 per million input tokens

$50 per million output tokens

with cache reads at $0.25 per million tokens.

Amazon Bedrock directs customers to its own Bedrock pricing system, so actual cloud costs can depend on how and where the model is deployed.

The bigger limitation, however, is not price.

It is eligibility.

You cannot simply create a normal Claude API account and select Mythos 5.1.

Who Can Access Mythos 5.1?

At launch, Anthropic says Mythos 5.1 is available only to a vetted group of cybersecurity defenders and life scientists.

Access currently focuses on selected U.S. organizations, although Anthropic says it is coordinating with the U.S. government to expand access to additional domestic and international organizations.

The two main routes are the Cyber Verification Program and the Life Sciences Verification Program.

This means that for most individual developers, startups and businesses, Claude Fable 5.1 remains the appropriate model.

Mythos solves a specialized access problem rather than replacing Fable.

Why Anthropic Is Restricting Mythos

There is an obvious question:

If Mythos is more useful for scientific and cybersecurity research, why not simply release it to everyone?

Anthropic’s answer is essentially that the same capabilities that help legitimate researchers can sometimes be useful for harmful purposes.

This is the classic dual-use problem.

A model capable of deeply understanding software vulnerabilities can help defenders fix systems, while related knowledge can also create security risks.

Likewise, more capable biological reasoning can accelerate legitimate life-sciences research while raising safety concerns around certain advanced applications.

Anthropic says it tested Mythos extensively across biological, chemical, cyber, agentic and alignment risks before release.

The company therefore chose controlled access instead of either making all capabilities public or blocking them entirely.

Improved Safety and Alignment

Interestingly, more permissive domain safeguards do not mean Anthropic removed its safety systems altogether.

According to the company’s evaluations, Mythos 5.1 improved on several alignment measures compared with Mythos 5.

Anthropic says the model was less likely to ignore explicit constraints, attempt to access resources outside its assigned environment or use questionable reasoning to justify behavior when facing impossible tasks.

It also showed lower rates of attempted and successful reward hacking in Anthropic’s evaluations.

Anthropic additionally reports that Mythos 5.1 is its most robust model so far on an external prompt-injection benchmark.

That is especially important for agents.

As AI systems gain more access to tools, files, browsers and other software, defending them against malicious instructions hidden inside external content becomes increasingly important.

Who Is Mythos 5.1 Really For?

Mythos 5.1 is aimed at a relatively narrow but important audience.

Its strongest use cases include approved cybersecurity research, vulnerability discovery, defensive security analysis, advanced life-sciences R&D, computational biology, drug-discovery research and research agents operating across these domains.

For normal coding, document analysis, content generation, business automation or general research, Fable 5.1 makes more sense.

And that is probably exactly how Anthropic intended the two-model structure to work.

Final Thoughts

Claude Mythos 5.1 is interesting precisely because it is not a conventional AI-model launch.

Anthropic has not created a simple hierarchy where Sonnet is good, Opus is better, Fable is better again and Mythos sits at the top.

Instead, Fable 5.1 and Mythos 5.1 represent two ways of deploying the same frontier model.

Fable brings that intelligence to general developers and businesses.

Mythos opens more specialized capabilities to vetted professionals whose work would otherwise collide with restrictions designed for general-purpose AI systems.

That approach may become increasingly common.

As frontier models become capable enough to contribute meaningfully to cybersecurity, biological research and scientific discovery, AI companies will have to answer a difficult question:

How do you make advanced capabilities available to legitimate experts without simply releasing every capability without controls?

Mythos 5.1 is Anthropic’s latest answer.

And for cybersecurity and life-sciences researchers who qualify for access, it could become one of the most capable AI research tools available.

Anthropic has launched Claude Fable 5.1, its newest high-end AI model for coding, research, knowledge work and long-running autonomous agents.

Released on September 1, 2026, Fable 5.1 is not positioned as a cheaper alternative to Anthropic’s existing models. In fact, its standard API pricing is higher than Claude Opus 5.

The interesting part is what happens when the model is used the way Anthropic expects many advanced AI systems to work: repeatedly reading large codebases, documents, tool definitions and conversation history over long periods of time.

For those workloads, Anthropic has dramatically reduced the cost of cached context.

Fable 5.1’s cache-read price is now just $0.25 per million tokens, a 75% reduction from Fable 5’s $1 rate. Anthropic estimates this can reduce the total cost of typical Fable workloads by around 25%, with savings reaching roughly 45% for highly agentic tasks.

But cheaper caching is only part of the story.

Anthropic is also claiming major improvements in coding, scientific research, computer use and long-duration problem solving, putting Fable 5.1 directly into competition with models such as Claude Opus 5 and OpenAI’s GPT-5.6 Sol.

What Is Claude Fable 5.1?

Claude Fable 5.1 is Anthropic’s new model for what the company calls demanding reasoning and long-horizon agentic work.

The model is designed for tasks that may continue for hours rather than seconds.

That includes software engineering projects spanning multiple files and services, large research jobs, complicated professional workflows, document analysis and autonomous agents that repeatedly call tools while working toward a goal.

Anthropic says Fable 5.1 establishes a new performance frontier for coding, knowledge work and long-running problem solving.

That positioning makes Fable somewhat unusual inside the Claude family.

Anthropic still recommends Claude Opus 5 for most workloads, while suggesting Fable 5.1 when a task requires particularly demanding reasoning or long-horizon agentic performance, or when Opus 5 at higher effort levels does not provide enough capability.

In other words, Fable 5.1 is not necessarily the Claude model you use for everything.

It is the model Anthropic wants you to reach for when the task gets difficult.

Claude Fable 5.1 Specifications

Fable 5.1 comes with specifications clearly aimed at large workloads.

The official model ID is:

claude-fable-5-1

It offers a 1 million-token context window, allowing the model to keep an enormous amount of information available in a single workflow.

Maximum output is 128,000 tokens, which is particularly useful for large coding tasks, lengthy reports and agent workflows that may generate substantial amounts of structured output.

The model accepts text and images as input and produces text output.

Its reliable knowledge cutoff and training data cutoff are both listed as June 2026.

Adaptive thinking is always enabled, while developers can control how much reasoning effort the model uses.

Key specifications

• Model: Claude Fable 5.1
• Model ID: claude-fable-5-1
• Context window: 1 million tokens
• Maximum output: 128K tokens
• Input: Text and images
• Output: Text
• Thinking: Adaptive
• Default effort: High
• Knowledge cutoff: June 2026
• Release date: September 1, 2026

Fable 5.1 is available through the Claude API as well as Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS.

Coding Is One of Fable 5.1’s Biggest Strengths

Anthropic has increasingly turned Claude into a platform for serious software engineering, and Fable 5.1 continues that strategy.

The model is designed to remain useful during long coding sessions rather than simply producing a function or answering a programming question.

That means it can potentially be used to:

• Explore large repositories
• Debug complex problems
• Trace bugs across multiple services
• Refactor existing systems
• Implement features end to end
• Use terminal tools
• Run tests and inspect failures
• Review code
• Maintain progress over long sessions
• Verify its own work before finishing

Anthropic’s published results put Fable 5.1 at 73.4% on CursorBench 3.2.0, compared with 70.5% for Fable 5, 70.0% for Opus 5 and 67.2% for GPT-5.6 Sol in Anthropic’s evaluation.

On Terminal-Bench 4.0, which measures agentic terminal coding, Fable 5.1 scored 55.8%, while the less-restricted Mythos 5.1 version reached 60.9%. Anthropic reports 52.3% for Opus 5 and 37.3% for GPT-5.6 Sol under the same evaluation setup.

These are impressive numbers, but they should be read carefully.

They are vendor-reported benchmark results, and Anthropic notes that model safeguards can affect some scores. Real-world performance can also vary significantly depending on tools, prompts, repository structure and agent design.

Still, the direction is clear: Anthropic wants Fable 5.1 to handle much larger units of software work.

From Code Generation to Long-Running Engineering

One of the more interesting examples from the launch came from investment firm Millennium.

According to Anthropic, the company had an extremely rare software crash that had remained unexplained for several years. Fable 5.1 reportedly disassembled an external vendor library, compared it against a core dump and traced the failure to a bug inside that library.

MongoDB also reported testing the model on a complex prototype where Fable 5.1 researched services, code and documentation before working autonomously for hours to implement the system.

These are customer examples supplied as part of Anthropic’s launch, so they should not be treated as independent scientific evaluations.

But they illustrate an important shift.

The goal is no longer:

“Can an AI write this piece of code?”

The more ambitious question is:

“Can an AI investigate, plan, build, test and finish an engineering project?”

Fable 5.1 is designed around that second question.

Long-Running AI Agents May Be the Real Story

Fable 5.1’s biggest impact may ultimately come from autonomous agents.

A traditional chatbot responds to individual requests.

An agent receives a goal and may need to take dozens or hundreds of actions before completing it.

It might search documentation, inspect files, execute code, use external tools, analyze results, correct mistakes and continue working.

That creates a very different challenge for an AI model.

It needs to remember what it is doing.

It needs to avoid losing direction.

It needs to understand when a step failed.

And it needs to decide what to do next without constantly asking a human.

Anthropic says Fable 5.1 performs particularly well on these long-running workflows.

Ramp, for example, reported an unattended machine-learning workflow that ran for 38 hours, revisited an earlier result, launched six parallel experiments and returned with findings and proposed next steps. Again, this is an early-access customer report rather than an independently reproduced benchmark, but it shows the kind of workload Anthropic is targeting.

Better Knowledge Work and Research

Fable 5.1 is not only a coding model.

Anthropic is also positioning it heavily around research and professional knowledge work.

On the company’s GDPval-AA v2 knowledge-work evaluation, Fable 5.1 reached an Elo score of 1,853, compared with 1,824 for Opus 5, 1,723 for Fable 5 and 1,711 for GPT-5.6 Sol.

On Humanity’s Last Exam, Anthropic reports:

60.9% without tools

and

65.0% with tools

for Fable 5.1.

The distinction between performance with and without tools is increasingly important.

A modern frontier model does not need to store every fact internally if it is capable of finding information, using software and correctly reasoning over the results.

This is especially relevant for research agents.

Scientific Research Is Becoming a Serious Use Case

Anthropic went unusually far with the scientific examples accompanying Fable 5.1 and Mythos 5.1.

The company says Fable 5.1 was used to train a neural network that produced a new high-resolution elevation map covering approximately one-third of Venus using data from NASA’s Magellan mission and existing mapping data.

Anthropic says the resulting map provides substantially finer detail than earlier altimetry data and has released the map under a Creative Commons license.

This is an interesting example because it moves beyond summarizing existing research.

The model was involved in a computational research workflow that produced a new research artifact.

Anthropic sees that as an early indication of where frontier AI systems could eventually contribute to scientific discovery.

What Is Claude Mythos 5.1?

Anthropic launched Claude Mythos 5.1 alongside Fable 5.1.

This can initially sound like a completely separate model, but the distinction is mostly about access and safeguards.

Anthropic says Fable 5.1 and Mythos 5.1 use the same underlying model.

Fable 5.1 is the generally available version with Anthropic’s normal production safeguards.

Mythos 5.1 uses more permissive safeguards for vetted organizations working in areas such as cybersecurity and life sciences.

Access to Mythos is therefore restricted through trusted programs rather than being broadly available to ordinary users.

That makes Fable 5.1 the relevant model for the overwhelming majority of developers.

Fewer Cybersecurity False Positives

Anthropic has also changed how Fable handles cybersecurity requests.

Fable 5.1 can now help identify software vulnerabilities for defensive purposes.

Anthropic says the updated cyber safeguards produce approximately 60% fewer interventions per Claude Code session compared with the safeguards used for Fable 5.

That could be meaningful for developers and security teams who previously saw legitimate defensive requests interrupted.

There are still boundaries.

Tasks such as exploit generation, penetration testing and certain binary vulnerability-scanning activities may be redirected to models and access environments with different safeguards.

Claude Fable 5.1 Pricing

Here is where Fable 5.1 becomes particularly interesting — and a little confusing.

The standard API prices are:

Input: $10 per million tokens

Output: $50 per million tokens

Those are exactly the same headline prices as Fable 5.

So Fable 5.1 is not 75% cheaper overall.

The 75% reduction applies specifically to cache reads.

Fable 5 charged:

$1.00 per million cached tokens

Fable 5.1 charges:

$0.25 per million cached tokens

That is a 75% reduction.

Cache writes cost:

$12.50 per million tokens for a five-minute cache

and

$20 per million tokens for a one-hour cache.

Anthropic also offers a 50% discount on regular input and output pricing through its Batch API.

Why the Cache Price Matters

At first glance, caching sounds like a minor technical detail.

For AI agents, it isn’t.

Imagine an AI coding agent working inside a large repository.

Every time the agent performs another task, it may need access to the same system prompt, tool definitions, documentation, repository files and previous conversation history.

Without caching, repeatedly processing that information becomes expensive.

With prompt caching, much of that unchanged context can be reused at a dramatically lower token price.

That is why Anthropic estimates Fable 5.1 will cost around 25% less for typical workloads and potentially up to approximately 45% less for highly agentic workloads, even though normal input and output prices have not changed.

It’s an important distinction.

The future AI pricing battle may be less about the advertised price of one million fresh tokens and more about the total cost of successfully completing a long-running task.

Fable 5.1 vs Claude Opus 5

This comparison is unusual.

Claude Opus 5 costs:

$5 per million input tokens

$25 per million output tokens

Fable 5.1 costs:

$10 input

$50 output

So Fable’s standard token rates are exactly twice as high.

But cached context reverses part of that equation.

Fable 5.1 cache reads cost only $0.25 per million tokens, while Opus 5 cache reads cost $0.50 per million.

That means a persistent agent repeatedly working with the same large context could have a very different cost profile than the headline token prices suggest.

Anthropic itself recommends starting with Opus 5 for most workloads.

Fable becomes more interesting when the task is difficult enough that its stronger long-horizon behavior produces better results or fewer retries.

Fable 5.1 vs GPT-5.6 Sol

OpenAI’s GPT-5.6 Sol is another obvious competitor.

Sol currently costs $4 per million input tokens and $20 per million output tokens, with cached input priced at $0.40 per million tokens. It also offers roughly a 1.05-million-token context window and up to 128K output tokens.

On raw list pricing, GPT-5.6 Sol is therefore considerably cheaper than Fable 5.1 for uncached input and output.

Fable’s cache reads, however, are cheaper:

Fable 5.1: $0.25

GPT-5.6 Sol: $0.40

per million cached input tokens at current published prices.

Anthropic’s own benchmark table also shows Fable 5.1 ahead of GPT-5.6 Sol on several of the evaluations it published, including Terminal-Bench 4.0, CursorBench and GDPval-AA v2.

But these are Anthropic-run comparisons.

OpenAI publishes its own evaluations showing different strengths for GPT-5.6, so independent testing remains important before concluding that one model is universally better.

For developers, the better question is likely to be:

Which model completes my actual workload most reliably at the lowest total cost?

Fable 5.1 vs Gemini 3.8 Flash

The timing makes another comparison impossible to ignore.

Just one day after Fable 5.1 arrived, Google introduced Gemini 3.8 Flash, its newest model for long-horizon coding and autonomous agents.

That means Anthropic and Google are now targeting many of the same emerging workloads almost simultaneously.

But their pricing strategies are dramatically different.

Gemini 3.8 Flash launched at an introductory price of $0.75 per million input tokens and $3.75 per million output tokens, while Fable 5.1 costs $10 and $50 respectively.

That does not make Gemini automatically better.

Fable is positioned as a premium model for extremely demanding work, while Gemini Flash is aggressively targeting price-performance.

But the gap means developers now have a fascinating comparison to test:

Can Fable’s higher reasoning and long-running reliability justify the much higher base cost?

Or can Gemini 3.8 Flash complete enough of the same agentic workloads at a fraction of the price?

Independent real-world testing will be more informative than launch-day benchmark charts.

Who Should Use Claude Fable 5.1?

Fable 5.1 makes the most sense when the value of completing a difficult task outweighs the cost of inference.

That could include:

Complex software engineering

Large repositories, difficult debugging, architecture work and long-running implementation tasks.

AI coding agents

Systems that repeatedly use terminals, files and developer tools over extended periods.

Research agents

Workflows requiring many searches, documents, tool calls and reasoning steps.

Financial and professional analysis

High-value knowledge work where accuracy and persistence matter more than raw token price.

Large document workflows

Contracts, reports, technical documentation, spreadsheets and presentations.

Computer-use agents

Systems that need to interact with software interfaces and work through multi-stage processes.

For simple chat, summarization or high-volume low-value classification, Fable’s premium pricing will usually be difficult to justify.

Is Claude Fable 5.1 Worth It?

For everyday AI use, probably not.

Anthropic itself points most users toward Opus 5 first.

For the hardest coding, research and autonomous-agent workloads, however, Fable 5.1 is much more interesting.

Its $10/$50 headline pricing makes it expensive.

But that number tells only part of the story.

The 75% reduction in cache-read pricing changes the economics for persistent agents, while Anthropic’s benchmark results suggest significant improvements in the model’s ability to keep working through difficult tasks rather than stopping at a plausible-looking first answer.

If that translates into fewer failed runs, fewer retries and less human intervention, the higher token price could be justified for certain workloads.

Final Thoughts

Claude Fable 5.1 shows where Anthropic believes the next phase of AI competition is heading.

The industry spent years comparing models based on how well they answered a single prompt.

That benchmark is becoming less useful.

Developers are increasingly asking AI systems to spend hours working inside repositories, searching through information, using external tools, making decisions and correcting their own mistakes.

In that world, intelligence still matters.

But so do persistence, context management, caching, tool reliability and the cost of reaching a successful outcome.

Claude Fable 5.1 is Anthropic’s attempt to optimize for that world.

And with GPT-5.6 Sol and Google’s newly released Gemini 3.8 Flash chasing many of the same workloads, the competition around autonomous AI agents is becoming far more interesting than the traditional chatbot race.