Fable 5.2 and Mythos 5.2: top of most leaderboards, at a price

Fable 5.2 is now in the Claude Platform and Claude Code. Anthropic says it is up to 30% faster than Fable 5.1, and the price drops from $10/$50 to $8/$40 per 1M tokens @ClaudeDevs. Anthropic also says both models beat Opus 5.5 in almost every capability it evaluated. Its other headline claims @kimmonismus:

• On FrontierCode 2.0, Fable 5.2 at High effort matched Opus 5.5 at Max for about half the cost.
• 1,000 Fable 5.2 agents formalized the existing proof of Fermat's Last Theorem in 30 hours. With the same setup, Opus 5.5 got 26% of the way in 18 hours.
• Mythos 5.2 accurately modeled 28 of 34 tested capsids.

What independent testers found:

• Artificial Analysis: Fable 5.2 is #1 on the Intelligence Index at 65 (max effort), ahead of Gemini 4 Carbon (60), Opus 5.5 (59) and GPT-6 Astra (57) @ArtificialAnlys. The gains are mostly in science, knowledge and FrontierCode. On agentic knowledge work and terminal coding it only reaches Opus 5.5's level; for example, it scores 60% on Terminal-Bench 4.0, the same as Opus @ArtificialAnlys.
• Epoch: it set an Epoch Capabilities Index record of 174, above Astra's 169. This came from pre-launch testing and could change @EpochAIResearch.
• Vals: it ranks #1 on Vals and leads seven benchmarks @ValsAI.

Cost per task is going up, even though tokens are cheaper. Artificial Analysis puts max-effort cost at $26.50 per task, about 2.6× Opus 5.5 @ArtificialAnlys. SemiAnalysis's GDPx tasks cost 31% more than on Fable 5.1 @SemiAnalysis_. SemiAnalysis attributes the gains mainly to much more thorough self-testing: Fable 5.2 often writes hundreds or thousands of test cases, where earlier models wrote about 20 @SemiAnalysis_.

Higher effort can hurt. On Vals, max effort dropped the model to #7 at nearly 3× the cost, often because it ran out of time or tokens before finishing @ValsAI. Artificial Analysis found the opposite pattern on its index: at high or xhigh effort Fable 5.2 already scores 62 and can beat Opus at lower cost @ArtificialAnlys. In practice, start at high effort and only raise it if you measure a gain.

Other limits to plan for:

• Refusals. Vals reports refusal rates sometimes in double digits, concentrated in Code Migration and ProgramBench @ValsAI.
• Limited AI-research ability. SemiAnalysis finds Fable 5.2 roughly level with Sonnet 5.5 on RSI tasks and says Anthropic explicitly limited AI-research advances during training @SemiAnalysis_. A researcher reports that kernel and LLM-optimization tasks are refused most of the time, even with Opus 5.5 @omarsar0.
• API restrictions. To deter distillation, preserved thinking now applies to every Fable 5.2 API account, so editing prior context can return a 400 error @ClaudeDevs.
• Data retention. Retention is on by default so Anthropic's safety classifiers can run. Zero data retention is only available to some eligible enterprise customers @code.
• Subscription value. Theo estimates that a $200 Ultra plan buys about $15,000 of Opus 5.5 use but only about $3,000 of Fable. Fable is also capped at 50% of the weekly limit @theo.
• Reward hacking. The system card attributes most of the rise in successful reward hacks to the model guessing what the answer key expects @dbreunig.

Meta's Muse Ember sets the small-model price-performance bar

Muse Ember is Meta's smallest model. It has a 1M-token context window and takes text, image, audio and video input @AIatMeta. It is also the first model Meta trained using Muse Wonder Full @alexandr_wang.

• Price-performance: Artificial Analysis scores it 36, matching Claude Haiku 5.5 (max) at $0.14 versus $0.41 per task @ArtificialAnlys.
• Arena: it scores 1608 on Code Arena WebDev, ahead of Haiku 5.5 and GPT-6 Luna. Its median cost per task is 3.7× lower than Haiku's even though the per-token prices are the same @arena.
• Weaknesses: knowledge is weak, with 21.1% accuracy on AA-Omniscience, largely because it declines 64% of questions @ArtificialAnlys. Vals finds it trails on coding but is competitive on legal and finance tasks @ValsAI.

Capital, security and safety infrastructure

• Anthropic IPO. Bloomberg reports Anthropic could start marketing its IPO as early as Tuesday and debut on Nasdaq the following week. Some investors have discussed a $1.1–1.3T valuation @kimmonismus.
• Fireworks breach. The inference provider disclosed that an October 24 intrusion, which came in through a third-party cloud provider, exposed account metadata and billing data, plus credentials, datasets and models for a limited set of accounts @lqiao. It links the attack to an "LLMJacking" group that it believes used AI to move fast @lqiao.
• OpenAI Control Room. OpenAI's new system tracks every research inference workload and can halt model output within 35 seconds of a decision to stop or pause a job @OpenAI.
• Paris Call. OpenAI, Anthropic, Microsoft, Mistral, Hugging Face and Sakana signed France's Paris Call for AI safety and security @SakanaAILabs.
• GPU lock rumor. A claim that Anthropic wants governments to require firmware locks on GPUs to block Chinese open models @TheDailyMao is unverified. Other commentators called it unsubstantiated @TheZachMueller.

Hardware and power

• Google TPUs. Google added TPU 8i Max, built for large context windows, and TPU 8tx, built for confidential inference. That makes four eighth-generation systems in one year @Google.
• HBM5. Samsung's Hot Chips material shows HBM5 roughly doubling pin speed, from 14 to over 28 Gbps, on the same 2,048 pins as HBM4E @SemiAnalysis_. The PHY can draw more than 4× the power @SemiAnalysis_.
• Epoch's Inference Performance Index. Epoch says raw FLOP/s understates GB300's inference advantage over H100 by 10×. The new index also accounts for memory bandwidth and inter-chip communication @EpochAIResearch.
• Texas permit freeze. Texas stopped issuing TCEQ permits to data-center projects at the end of September. The state is close to 30% of planned US additions for 2028 @SemiAnalysis_.
• Intel. Shares fell 35% in a single day, Intel's worst day ever @vikramskr.

Open models and research

• Inkling 2 Small. Thinking Machines' model ranks #31 in Agent Arena and would be the top US open-weights model there once its weights ship. Median cost is $0.25 per task @arena.
• Marin 535B-A23B. Open Athena's model finished pretraining on 18T tokens with no instabilities, and the full recipe is open @classiclarryd.
• Quantum programs verified in Lean. In a Google paper, teams of agents wrote quantum programs whose correctness and cost are proven in Lean. The verified programs cut quantum-arithmetic cost 290× against the best prior baseline @dair_ai.