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  1. Meta AI Research Blog78

    Meta releases Muse Ember, its smallest and most efficient model for agent tasks

    Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

    Why it matters: Meta's smallest model targets the high-volume agent steps, and its cost-per-task comparison against Claude Haiku 5.5 and GPT-6 Luna shows where the efficiency claim comes from.

  1. Meta AI Research Blog60

    Meta brings specialized Muse and Meta AI models with Active Memory and Active Focus

    Meta is rolling out specialized versions of its existing models, trained using Muse Wonder, to people who use Muse and Meta AI. The specialized models add Active Memory and Active Focus, and Meta reports the current Muse model scores 47% on its Personal Superintelligence Index versus 38% for the previous model, 90% versus 76% on the Active Memory eval, and a 67% improvement in completing everyday tasks where unexpected issues arise.

    Why it matters: Meta's specialized Muse and Meta AI models bring Active Memory and Active Focus to more users, with reported gains on memory and task-completion evals.

  1. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Why it matters: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

  1. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Why it matters: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

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