Release1d ago
Anthropic Releases Claude Fable 5.2 and Mythos 5.2: Price Cut to $8/$40 per Million Tokens, a Record 49.1% on FrontierCode 2.0 Among Released Models, and 1,000 Agents Formalizing Fermat's Last Theorem in 30 Hours
At 18:58 UTC on November 9 (2:58 a.m. on November 10 SGT), the official Claude account released Claude Fable 5.2 and Claude Mythos 5.2. They are the same underlying model: Fable ships with cybersecurity and biology safeguards and is open to all paid plans, the Claude Platform and the major clouds, while Mythos drops some of those safeguards and is offered only to vetted organizations through the Cyber and Life Sciences Verification Programs. Pricing falls from Fable 5.1's $10/$50 to $8/$40 per million input/output tokens, output is up to 30% faster, and an optional fast mode generates up to 2.5x faster for $16/$80. By Anthropic's numbers, Fable 5.2 at Xhigh effort scores 49.1% on FrontierCode 2.0, ahead of Opus 5.5 (45.5%) and GPT-6 Astra (42.8%); at High effort it matches Opus 5.5's best for about half the cost per task. The API model name is claude-fable-5-2.
Release1d ago
Meta Launches Muse Ember, Its Smallest and Most Efficient Model: $0.10/$0.50 per Million Tokens, 1M Context, and an Artificial Analysis Score of 36 That Ties Claude Haiku 5.5 at About a Third of the Cost per Task
Early on November 10 SGT, the official AI at Meta account introduced Muse Ember, which Meta calls its smallest and most efficient model so far. It targets the work agents repeat all day: tool calls, coding subtasks, routing, extraction and long document parsing. Muse Ember has a 1M-token context window, up to 512K output tokens, and native understanding of text, images, audio and video. Pricing is $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens, and it is live on the Meta Model API (as muse-ember-1.0) and in Muse Code. Artificial Analysis measured an Intelligence Index score of 36, the same as Claude Haiku 5.5 (max), at about $0.14 per task, roughly a third of Haiku's cost. It is the first model trained using Muse Wonder Full, and the weights are not open.
Release2d ago
Meituan Releases LongCat-2.5 Weights Under MIT: the 1.6T-Parameter, ~48B-Active Native Multimodal MoE Lands on Hugging Face With a W4A4 Quantized Version
On the evening of November 9 SGT, the official Meituan LongCat account announced that LongCat-2.5 weights are now open under the MIT license on Hugging Face. LongCat-2.5 is the native multimodal MoE model Meituan launched as an API on November 2, with 1.6 trillion total parameters and about 48 billion active per token, aimed at long-horizon agent tasks, agentic coding, creative design and 3D work. Meituan published both the BF16 main model (a repository of about 3.56TB) and a W4A4 quantized version (about 1.34TB). The model card currently only documents NPU deployment, via SGLang-FluentLLM, and the model supports both thinking and non-thinking modes. Vendor-reported scores include 88.4 on Terminal Bench v2.1 and 91.9 on BrowseComp; the model card says LongCat scores are measured in-house and are indicative only.
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