Anthropic 发布 Claude Fable 5.2 与 Mythos 5.2，IPO 前重夺 AI 性能领先
Claude Fable 5.2 and Mythos 5.2 cement Anthropic's AI lead just before its IPO
- 1Anthropic 发布 Claude Fable 5.2 与 Mythos 5.2，公司在 IPO 前重新夺回 AI 性能领先
- 2Artificial Analysis 的 Index v5 榜单上 Fable 5.2 以 65 分居首，领先 Gemini 4 Carbon 5 分、领先上代 Fable 5.1 达 15 分
- 3官方称约 1000 个 Fable 5.2 智能体用 30 小时完成费马大定理证明的 Lean 形式化，此前内部版本需 11 天
- 4定价降至每百万输入 token 8 美元、输出 40 美元，但最高档单任务平均成本 26.50 美元，约为 Opus 5.5 的 2.6 倍
Anthropic 在 IPO 前以双版本策略重夺榜首，独立榜单领先成立，但顶级能力伴随明显的单任务溢价和安全分级门槛。
- 影响：单任务成本约为 Opus 5.5 的 2.6 倍，高端场景预算需按任务而非 token 单价测算；公开版 Fable 屏蔽渗透测试等安全任务，相关业务要走 Mythos 的组织审核通道；输出提速与 token 用量下降可部分对冲溢价。
- 参考：可考虑先挑科研自动化、形式化验证等高价值任务试点，按单任务成本与 Opus 5.5 对比测算预算；有漏洞研究或渗透测试需求的团队可评估 Mythos 5.2 验证计划的申请门槛。
- 依据：榜单成绩出自 Artificial Analysis 的 Index v5；CVE 提升、费马形式化等关键数据均为 Anthropic 官方口径，暂无独立验证。
Anthropic's Claude Fable 5.2 and Mythos 5.2 reclaim the AI performance lead shortly before the company's IPO, in some cases by a wide margin. Opus 5.5 remains Anthropic's recommended model for everyday use.
Claude Fable 5.2 and Claude Mythos 5.2 share the same model foundation but, just like their predecessors, differ in access. Fable includes safeguards that block risky tasks in cybersecurity and biology and is available to all users. Mythos removes certain of these restrictions and is open only to organizations that apply through Anthropic's Verification Programs for Cybersecurity or Life Sciences.
Pricing is $8 per million input tokens and $40 per million output tokens. On paper, at least, that's a cut from the predecessors, which cost $10 and $50. What users actually pay depends on token consumption, which varies with tasks, settings, and software environments. Anthropic positions the new models for the most demanding technical work and continues to recommend Claude Opus 5.5 for everyday use.
The models produce output up to 30 percent faster than Fable 5.1 and Mythos 5.1, Anthropic says. An optional fast mode for select organizations speeds up token output by up to a factor of 2.5 but costs twice as much and is available only on certain platforms, including Claude Code, the Claude Platform, Google Cloud, and Microsoft Foundry.
Fable 5.2 jumps to a clear lead on benchmarks
Artificial Analysis’s benchmarks put Fable 5.2 clearly ahead of its rivals. On the current Index v5 at the highest compute tier, it takes first place with 65 points, five ahead of Gemini 4 Carbon (60, High tier), which Google still hasn't released, six ahead of Claude Opus 5.5 (59, Max tier), and a striking 15 ahead of Claude Fable 5.1, which came out only in early September.
Fable 5.2 scores best in five of nine individual tests, according to Artificial Analysis. It hits 80 percent on HLE-Diamond, a test with expert-level academic questions, 73 percent on Terminal-Bench-Science, which tests whether an AI agent can independently run scientific workflows in a terminal, and 49 percent on FrontierCode 2.0, which evaluates whether an agent can deliver useful code changes in real open-source projects.
Fable 5.2 is weaker in agentic knowledge work and terminal-based coding. It reaches 1,785 Elo on AA-Briefcase, a benchmark for multi-step knowledge work tasks, just behind Claude Sonnet 5.5 (1,813) and within the confidence interval of Opus 5.5 (1,800). On Terminal-Bench 4.0, which measures multi-step terminal tasks in areas like software development and security, it scores 60 percent, tied with Opus 5.5. Kimi K3.1 Pro (67 percent), Gemini 4 Carbon (65 percent), and Sonnet 5.5 (64 percent) rank higher here.
Peak quality comes at a price, though. Per-task costs at the highest tier average $26.50, about 2.6 times as much as Opus 5.5 ($10) and roughly double Fable 5.1 ($12.99). Fable 5.2 still scores 62 points on the Intelligence Index at the lower High and Xhigh tiers, ahead of every other model at any tier according to Artificial Analysis, while consuming far fewer tokens: 98,000 output tokens per task compared to 173,000 for Opus 5.5 at the Max tier.
| Anthropic Benchmark Results | Fable 5.2 | Fable 5.1 | Opus 5.5 | GPT-6 Astra | Gemini 4 Carbon |
|---|---|---|---|---|---|
| Agentic coding FrontierCode 2.0 |
49.1% Xhigh effort |
32.4% Xhigh effort |
45.5% | 42.8% | 41.1% Medium effort |
| Agentic coding FrontierSWE v2 |
69.9% | 56.3% | 62.3% | 65.5% | 61.2% |
| Business Workflows AutomationBench |
51.4% | 31.4% | 42.5% | 41.4% | 53.9% |
| Agentic scientific research Terminal-Bench Science 0.1 |
74.6% 75.3% (Myth 5.2) |
52.6% | 58.7% | 68.1% | 64.8% |
| Cybersecurity CyScenarioBench† |
76.3% Myth 5.2 |
61.7% Myth 5.1 |
67.6% without safeguards |
61% GPT-6 Cyber |
— |
| Cybersecurity CVE Rediscovery |
89.6% Myth 5.2 |
78.3% Myth 5.1 |
81.3% without safeguards |
— | — |
| Protein design De novo binder design (24 hr) |
85.1% Myth 5.2 |
79.1% Myth 5.1 |
82.6% without safeguards |
— | — |
| Agentic search WANDR (relaxed score) |
82.1% | 68.7% | 72.3% | — | — |
| Computer use OSWorld 2.1 (offline set, partial) |
89.3% | 83.5% | 87.2% | 79.5% | 81.5% |
| Robotics RoboDojoSim‡ |
64.9% | 27.6% | 39.4% | 29.8% | — |
| Knowledge work AA-Briefcase v1.1 |
66.1% rubric |
61.5% rubric |
61.2% rubric |
52.0% rubric |
61.8% rubric |
| 1785 Elo |
1669 Elo |
1800 Elo |
1569 Elo |
1611 Elo |
|
| Knowledge work Remote Labor Index (RLI) |
41.3% High effort |
17.9% | 21.3% | 20.8% | — |
| Multidisciplinary reasoning Humanity's Last Exam (with tools) |
74.2% | 65.6% | 67.7% | 57.2% | — |
| Visual chart recognition Chartography (no tools) |
73.4% | 44.8% | 64.4% | 71.0% | 72.5% |
Mythos 5.2 finds far more security flaws than all predecessors
The new models also set records in cybersecurity, which is seeing intense debate right now. Mythos 5.2 finds more vulnerabilities than earlier models and is better at reproducing and fixing them, Anthropic says. Fable, the publicly available variant, blocks most security-related tasks as a result, including penetration tests and exploit code generation. Opus 4.8 responds to blocked cybersecurity requests instead, and Opus 5 responds to blocked biology requests.
Anthropic uses an internal test called CVE Rediscovery to check how many published vulnerabilities from the CVE database (Common Vulnerabilities and Exposures, a system for cataloging known software flaws) a model can independently find when given the project's state before the fix.
Mythos 5.2 found 10.2 percent more vulnerabilities than Opus 5.5 and 14.4 percent more than Mythos 5.1 in the October 2026 run with 1,829 CVEs, Anthropic says. It led at every compute tier. One security firm reported that one in six confirmed bugs Mythos 5.2 found had been missed by all other frontier models tested, according to Anthropic.
Anthropic throttles capabilities that could accelerate AI development
Anthropic says it deliberately held the model back during training from improving at capabilities that could most directly accelerate AI development, so-called RSI, which is widely discussed as a risk right now. Fable 5.2 and Mythos 5.2 perform roughly on par with Opus 5.5 in areas like optimizing compute kernels for accelerator chips and improving large language model architectures, the company says.
Related capabilities like agentic coding, hardware development, and traditional AI research, including interpretability research, aren't affected, according to Anthropic. Claude now writes over 90 percent of the changes merged into Anthropic's core codebase, under human guidance, the company says. Anthropic also claims the model programmed an operating system from scratch in an internal test.
Claude Fable 5.2 formalizes Fermat's Last Theorem in 30 hours
The example likely to draw the most attention comes once again from mathematics. About 1,000 Fable 5.2 agents fully formalized the proof of Fermat's Last Theorem, translating it into the Lean programming language so a computer can verify it step by step, Anthropic says.
Fermat's Last Theorem states that the familiar equation a² + b² = c² has no whole-number solutions for powers higher than squares. Nobody could prove this for centuries until Andrew Wiles presented a proof over a hundred pages long in 1995. Its machine verification has now been completed for the first time. It took 30 hours.
An earlier, unpublished research version of Claude needed 11 days for the same task and relied on a purpose-built environment and guidance from a human researcher. The Fable 5.2 agents started with brief instructions, organized their collaboration on their own, and used no mathematical references, Anthropic says. The only progress metric was a list of intermediate lemmas. The model used one-third of the action tokens of the first attempt and produced a proof seven times shorter.
Anthropic ran Opus 5.5 in the same setup with 1,000 agents for comparison. Opus managed just 26 percent of the formalization in 18 hours and made barely any progress after that through hour 30.
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