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Thursday

  1. Shao Meng

    Recursive releases its first model Flint: local coding MoE, 125+ tokens/s on a MacBook

    Recursive releases its first model Flint, a MoE with 40 billion total parameters and 2.5 billion activated parameters per token, aimed at local Agent coding, with weights open-sourced under Apache 2.0. With short context, it exceeds 125 tokens per second on an M4 Pro MacBook Pro and over 150 on a 64GB RTX Spark, and supports llama.cpp, Ollama, LM Studio, vLLM, SGLang, and Unsloth. Nearly every stage of its R&D involved the automated research system Eureka Machine, including architecture search, quantizing the output layer to 4 bit with offline distillation, and fusing Metal kernels for M4/M5.

    tivated parameters per token, aimed at local Agent coding, with weights open-sourced under Apache 2.0. With short context, it exceeds 125 tokens per second on an M4 Pro MacBook Pro and over 150 on a 64GB RTX Spark, and supports llama.cpp, Ollama, LM Studio, vLLM, SGLang, and Unsloth. Nearly every stage of its R&D involved the automated research system Eureka Machine, including architecture search, quantizing the output layer to 4 bit with offline distillation, and fusing Metal kernels for M4/M5.

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    #hot-signal#Model releases#Open-source ecosystem#Agents

Friday

  2. Artificial Analysis full articleSelected

    Reflection Releases Its First Model Beam, Scoring 30 on the Artificial Analysis Intelligence Index

    Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% of questions correctly, with a hallucination rate of 47% and a net index score of 1.5.

    model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% o

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    #company#Model releases#Open-source ecosystem#Evaluations / benchmarks
  3. Story32 reports · 3 facts

    Meta releases open-weight model Muse Spark Open

    On October 15, 2026, Meta released the open-weight model Muse Spark Open, with the weights going live on Hugging Face the same day under the Muse Model License. According to the PyTorch Blog, the model has 816B total parameters, 42B activated parameters, and a 1M token context window; it is natively multimodal and aimed at coding and agentic tasks, adds safety post-training on top of Muse Spark, and can run on SGLang and vLLM. On October 16, the model launched on Amazon SageMaker JumpStart with two variants, BF16 and NVFP4; the same day it entered live testing in Arena's Agent Arena and launched in Code Arena's WebDev and other projects. On October 17, Arena announced that Muse Spark Open (Max) ranked first among US open models on the Code Arena WebDev leaderboard with a score of 1511, placing 13th among all open models.

    On October 15, 2026, Meta released the open-weight model Muse Spark Open, with the weights going live on Hugging Face the same day under the Muse Model License. According to the PyTorch Blog, the model has 816B total parameters, 42B activated parameters, and a 1M token context window; it is natively multimodal and aimed at coding and agentic tasks, adds safety post-training on top of Muse Spark, and can run on SGLang and vLLM. On October 16, the model launched on Amazon SageMaker JumpStart with

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Thursday

  3. FactRelated story

    Meta releases open-weight model Muse Spark Open

    On October 15, 2026, Meta released the open-weight model Muse Spark Open, with the weights going live on Hugging Face the same day under the Muse Model License. According to the PyTorch Blog, the model has 816B total parameters, 42B activated parameters, and a 1M token context window; it is natively multimodal and aimed at coding and agentic tasks, adds safety post-training on top of Muse Spark, and can run on SGLang and vLLM. On October 16, the model launched on Amazon SageMaker JumpStart with two variants, BF16 and NVFP4; the same day it entered live testing in Arena's Agent Arena and launched in Code Arena's WebDev and other projects. On October 17, Arena announced that Muse Spark Open (Max) ranked first among US open models on the Code Arena WebDev leaderboard with a score of 1511, placing 13th among all open models.

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    View story:Meta releases open-weight model Muse Spark Open

Wednesday

  2. Story27 reports · 1 facts

    Meta launches Muse Wonder and Muse Wonder Full models

    On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two models scored 52 and 56 respectively on Intelligence Index v4.3 at maximum reasoning effort, higher than the 48 of Muse Spark 1.3 (max); Muse Wonder is already available on Meta's first-party API and Muse Code, while Muse Wonder Full is available only to Muse Code users. On the same day, Arena added the two models to Agent Arena testing, and Simon Willison released the llm-meta-ai 0.2 plugin supporting the new models. On October 14, Arena announced Muse W

    On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artifici

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  1. Story1 reports · 1 facts

    Fireworks Launches Meta's Open-Weight Model Muse Spark Open

    On October 20, 2026, Fireworks launched Meta's open-weight model Muse Spark Open on the day of its release, making it available to users through its serverless API. The model is an 816B-parameter MoE model that activates 42B parameters per token, has a 1M-token context window, and is aimed at long-horizon coding and agentic tasks. Fireworks said the model ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly four times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench, it surpasses Claude Opus 5.5 at every difficulty level. Fireworks also said it validated the weights with its own production stack, scoring 91.1% on AIME26, 89.7% on GPQA Diamond, and 84.3% on SWE-bench Verified, with consistent results when rerun using SGLang. On Fireworks, the model has zero data retention enabled by default, uses only compute located in the United States with no additional surcharge, and supports fine-tuning with serverless LoRA.

    On October 20, 2026, Fireworks launched Meta's open-weight model Muse Spark Open on the day of its release, making it available to users through its serverless API. The model is an 816B-parameter MoE model that activates 42B parameters per token, has a 1M-token context window, and is aimed at long-horizon coding and agentic tasks. Fireworks said the model ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly four times that of Claude Opus 5 (6.7%); on the Rogo

    Matched fieldsTitle

Tuesday

  1. FactRelated story

    Fireworks launches Meta's open-weight model Muse Spark Open

    On October 20, 2026, Fireworks launched Meta's open-weight model Muse Spark Open on the day of its release, making it available to users through its serverless API. The model is an 816B-parameter MoE model that activates 42B parameters per token, has a 1M-token context window, and is aimed at long-horizon coding and agentic tasks. Fireworks said the model ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly four times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench, it surpasses Claude Opus 5.5 at every difficulty level. Fireworks also said it validated the weights with its own production stack, scoring 91.1% on AIME26, 89.7% on GPQA Diamond, and 84.3% on SWE-bench Verified, with consistent results when rerun using SGLang. On Fireworks, the model has zero data retention enabled by default, uses only compute located in the United States with no additional surcharge, and supports fine-tuning with serverless LoRA.

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    View story:Fireworks Launches Meta's Open-Weight Model Muse Spark Open

Tuesday

  1. FactRelated story

    Meta launches the Muse Wonder and Muse Wonder Full models

    On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two models scored 52 and 56 respectively on Intelligence Index v4.3 at maximum reasoning effort, higher than the 48 of Muse Spark 1.3 (max); Muse Wonder is already available on Meta's first-party API and Muse Code, while Muse Wonder Full is available only to Muse Code users. On the same day, Arena added the two models to Agent Arena testing, and Simon Willison released the llm-meta-ai 0.2 plugin supporting the new models. On October 14, Arena announced Muse W

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    View story:Meta launches Muse Wonder and Muse Wonder Full models

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