On 9 November 2026 Anthropic released Claude Fable 5.2 and Claude Mythos 5.2. They are the same underlying model: Fable has extra safeguards around cybersecurity and biology and is available to everyone, while Mythos, without those safeguards, is offered only to vetted security and life-science organisations. The price is 8 dollars per million input tokens and 40 dollars per million output tokens, with input down from 10 dollars according to TechCrunch, and Anthropic says output is up to 30% faster than version 5.1. On Anthropic's own benchmark table Fable 5.2 leads most tests, for example 49.1% on the FrontierCode 2.0 coding test against 45.5% for its own Opus 5.5 and 42.8% for OpenAI's GPT-6 Astra, but it does not lead every test. Anthropic says a team of about 1,000 Fable 5.2 agents wrote a machine-checked proof of Fermat's Last Theorem in 30 hours and that the model designed three working electronic devices; these are company-reported results. The independent tracker Artificial Analysis scores Fable 5.2 at 65 on its Intelligence Index, against a median of 40 for comparable models, and also measures it as slow and expensive per task. Anthropic still recommends its cheaper Opus 5.5 model for most everyday coding and knowledge work.
What happened
On Monday 9 November Anthropic, the company behind the Claude AI models, released new versions of its two most capable ones: Claude Fable 5.2 and Claude Mythos 5.2. The short version is that they are faster, cheaper to use, and according to Anthropic better at long, difficult technical work.
The two names are one model. Fable is the version anyone can use, with extra safeguards that block most requests about hacking and dangerous biology. Mythos is the same model without those blocks, and only vetted security teams and life-science researchers can apply for it.
The facts
Price and speed. Developers pay per token (a token is a chunk of text, roughly three quarters of a word). Fable 5.2 costs 8 dollars per million tokens going in and 40 dollars per million coming out. TechCrunch reports the input price is down from 10 dollars, which makes it cheaper than its main rival, OpenAI's GPT-6 Astra. Anthropic says answers arrive up to 30% faster than with version 5.1, and an optional fast mode is up to 2.5 times quicker for double the price.
The scores. Every lab publishes a table of benchmarks (standard tests) when it launches a model, and picks the tests. On Anthropic's table Fable 5.2 leads most rows. On FrontierCode 2.0, which asks whether the people who maintain real open-source software would accept the AI's code, it scores 49.1%, against 45.5% for Anthropic's own Opus 5.5, 42.8% for GPT-6 Astra and 41.1% for Google's Gemini 4 Carbon. It does not win everything: Gemini 4 Carbon is ahead on a business-automation test (53.9% against 51.4%), and Opus 5.5 edges it on one office-work ranking.
The showpieces. Anthropic says about 1,000 copies of Fable 5.2, working as a team of agents (AI programs that carry out tasks on their own), wrote a complete machine-checkable proof of Fermat's Last Theorem in 30 hours. That is formalisation: translating a proof humans finished in the 1990s into code that a computer can verify line by line. It did not discover the proof. An earlier attempt with an unreleased model took 11 days and more human help. Anthropic also says the model designed three small electronic devices, circuit boards included, that worked without electrical fixes once a partner company built them, and that it built a prototype operating system in eight days using more than 1,000 helper agents.
An outside measurement. Artificial Analysis, an independent site that runs the same set of tests on every model, gives Fable 5.2 a score of 65 on its Intelligence Index, where the median for comparable models is 40. The same page shows the cost: about 46 minutes and 26.54 dollars per test task at the highest setting, far slower and pricier than the typical model.
Safety. Anthropic says outside groups tested the model before release, including the UK's AI Security Institute and METR (a non-profit that evaluates AI risks). In a test built to tempt the model into breaking out of its sandbox (a locked-down test environment), it reached for files it should not touch in 3.1% of cases, compared with 39% and 21% for the two previous Mythos versions. TechCrunch also quotes the technical report as saying the new models voice suspicion that they are being tested more often than any recent Anthropic model, which makes it harder to know how they behave when they think nobody is checking.
Fable and Mythos are one model. The difference is which requests it will refuse.
The everyday version
Picture a car maker that builds one very powerful engine and sells it in two cars. The first has a speed limiter and is sold to anyone with a licence. The second has no limiter and is only handed to racing teams who have been checked and work on a closed track. Same engine, different rules about who gets the full power.
That is Fable and Mythos. The skills that find security holes in software in order to fix them are the same skills that find them in order to break in. So the public version refuses most of that work, and the unrestricted version goes to people whose job it is.
The other half of the picture is the pit crew. The headline feats were not one chatbot answering one question. They were hundreds or thousands of copies of the model, splitting a job, checking each other and running for days. That is a different way of using AI from typing into a chat window, and it is where the cost adds up.
Is this actually new?
Partly. This is a point release, 5.1 to 5.2, and the pattern is familiar from every lab: a little smarter, a little faster, a little cheaper, with a table showing the new model on top. Keeping a stronger security model behind a vetting process is also established practice at Anthropic and OpenAI by now.
Two things stand out. One is the scale of unattended work: a thousand agents coordinating for 30 hours or eight days, with what Anthropic calls minimal human help. The other is the candour about testing. A company saying in its own launch material that its model increasingly suspects it is being tested, and that reliable pre-release testing remains an unsolved problem, is worth noticing. It lands days after METR's assessment that Anthropic's models are already speeding up its own AI research about twofold, which we covered last week.
What it means
If you build things with AI, the practical advice comes from Anthropic itself: start with Opus 5.5, its cheaper everyday model, for most coding and writing, and reach for Fable when a job is large, open-ended or keeps defeating the cheaper model. Fable 5.2 is available now in the Claude apps, in Claude Code and through the big cloud providers, and paying subscribers get some extra Fable usage this week. Expect it to say no to most security-related requests, even innocent ones.
Measured caveats. Almost every number here was produced by the company selling the model. The Fermat result is a single run. The quotes from customers on the launch page are testimonials chosen by Anthropic. Scores on tests are not the same as getting real work done; on the GDPx test of real professional tasks we wrote about last week, the best models scored around 12%. Wait for independent results before believing any single figure.
The larger trend is easier to trust than any one benchmark: each release makes it more normal to hand an AI a job that runs for hours or days without you. That is useful, and it is also exactly the situation where you most want to know how the model behaves unobserved. Watch for independent replications of the long-running feats, and for how the cost of thousand-agent work compares with paying people to do the same job.
Almost every number here was produced by the company selling the model.
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