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Tuesday63 items

  7. Latent SpaceModel releases
    AI score74

    Meta MSL releases Muse Wonder and Muse Wonder Full models

    Meta Superintelligence Labs releases its second-generation model family Muse Wonder and Muse Wonder Full; the former is positioned as a fast frontier model, the latter as the full version, and Meta says they target personal and professional agent work. On Artificial Analysis's Intelligence Index v4.3, Wonder Full scores 56 and Wonder scores 52, higher than Muse Spark 1.3's 48, while Opus 5.5 leads with 58. Specifications are 1M context, input supports text, images, video and audio, output is text only, and pricing is 4 USD per million tokens for input, 0.20 USD for cached input, and 20 USD for output.

    #Model releases#Agents#English text is being prepared.

  9. HuxiuModel releases
    AI score72

    Meta releases Muse Wonder and Muse Wonder Full, surpassing Claude on long-horizon coding tests…

    Meta Superintelligence Labs launched its new-generation flagship models Muse Wonder and Muse Wonder Full on October 13. The Full version scored 56 on the Artificial Analysis Composite Intelligence Index, 2 points behind Claude Opus 5.5's 58. On the DeepSWE 1.1 long-horizon software engineering test, Wonder Full scored 79.0, surpassing Opus 5.5's 74.2 and GPT-6 Astra's 74.1, but it still trails Opus on Terminal-Bench 4.0 and ProgramBench. Artificial Analysis estimates its average task cost at $4.52, up 183% from the previous generation Muse Spark 1.3; a developer comparison on the same prompt shows its 3D game generation is noticeably worse than Opus, and Meta's model card has acknowledged 3D tasks as a known limitation.

    Same story · 22 reports

    #Model releases#Agents#English text is being prepared.

  10. HuxiuModel releases
    AI score80

    Meta releases the Muse Wonder series of models, focused on personal agent memory and long-horizon tasks

    Meta released two models, Muse Wonder and Muse Wonder Full, internally codenamed Watermelon, targeting personal and professional agent tasks, with a focus on improving cross-conversation memory and long-duration task execution. Artificial Analysis evaluations show that Wonder Full placed in the top three on six of ten tests, and at the highest reasoning intensity, Muse Code paired with Wonder Full matches Claude Code paired with Sonnet 5.5 in coding performance. API pricing is $4 per million input tokens and $20 per million output tokens; Wonder Full is available only to Muse Code subscribers, and Meta AI head Alexandr Wang acknowledged that its coding capability still lags behind Opus 5.5.

    Same story · 22 reports

    #Model releases#Agents#English text is being prepared.

  11. David SingletonModel releases
    AI score55

    Muse Wonder model released, aimed at personal agents and made available first to subscribers

    The Muse Wonder model has been released, positioned to power personal agents, and has been made available first to subscribers. The model includes three capabilities: Active Memory, Active Focus, and domain expertise. The company says its memory spans day, week, and month scales, and that it achieves SOTA in finance and legal domains and performs strongly in health. David Singleton said he has been using it for some time and believes that under Active Focus and Active Memory the model works harder and calls on memory more precisely.

    Quoted postAlexandr Wang · @alexandr_wang

    MUSE WONDER 🍉 this model was developed to power personal agents. three highlights: - ACTIVE MEMORY: best memory across days/weeks/months - ACTIVE FOCUS: gets your tasks done - DOMAIN EXPERTISE: sota in finance & legal, strong in health https://t.co/Zoa66r4WOR

    Same story · 22 reports

    #Product updates#Agents#OpenAI

Monday1 items

Sunday6 items

  1. Hacker News TrendingModel releases
    AI score71

    Microsoft releases the Decision-1 decision scoring model, post-trained on Qwen3.5-9B

    On October 9, Microsoft launched Microsoft-Decision-1, which does not generate text but instead performs a single forward-pass scoring of preset answer options and returns a calibrated probability for each option, for use in routing, classification, prioritization, and verification. The model is post-trained on Alibaba's Qwen3.5-9B, is in public beta on Microsoft Foundry, and is available on OpenRouter under the identifier microsoft/microsoft-decision-1, with weights not distributed externally. Microsoft says it achieved 83.5% accuracy across 36 benchmarks and about 150,000 held-out questions, and plans to later migrate the base to MAI and OpenAI models; the OpenRouter listing shows a price of $0.042 per million input tokens, with output free.

    Same story · 5 reports

    #Model releases#Reasoning#English text is being prepared.