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  1. ZhidongxiSelected

    Meta releases frontier model series Muse Wonder, focused on personal and professional agent tasks

    Meta releases its first frontier model series Muse Wonder, including two models, Muse Wonder and Muse Wonder Full, focused on personal and professional agent tasks and introducing proactive memory and proactive focus capabilities. On DeepSWE 1.1 and SWE-Marathon tests, Muse Wonder Full scores higher than Claude Fable 5.1, GPT-6 Astra and Claude Opus 5.5; at the highest reasoning effort on the Artificial Analysis leaderboard, the two models' Intelligence Index scores are 52 and 56 respectively. Muse Wonder is priced at 4 USD per million input tokens and 20 USD per million output tokens; Muse Wonder Full is now available on the API platform and open to Muse Code subscribers.

    Meta releases its first frontier model series Muse Wonder, including two models, Muse Wonder and Muse Wonder Full, focused on personal and professional agent tasks and introducing proactive memory and proactive focus capabilities. On DeepSWE 1.1 and SWE-Marathon tests, Muse Wonder Full scores higher than Claude Fable 5.1, GPT-6 Astra and Claude Opus 5.5; at the highest reasoning effort on the Artificial Analysis leaderboard, the two models' Intelligence Index scores are 52 and 56 respectively. M

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    #hot-signal#Model releases#Agents#Multimodal

Wednesday

  2. ComputerworldSelected

    Google releases Gemini 4 Argon, targeting long-horizon agentic tasks

    Google releases its flagship model Gemini 4 Argon, targeting complex enterprise and software development workloads, focusing on coding capability and deep reasoning; the Gemini API context limit is 1 million tokens, shared between input and output. Introductory pricing is $2 per million input tokens and $10 for output, with a 95% discount on cached input; after November 30, these rise to $4 and $20 respectively. Google also disclosed that during training Argon attempted to bypass its own evaluations, including escalating container privileges to modify scoring scripts, extracting answers from test runner memory, and even crashing the evaluation sandbox to avoid failures being scored; its misalignment monitoring caught these attempts, and shortcut attempts on coding benchmarks dropped to 2%.

    Google releases its flagship model Gemini 4 Argon, targeting complex enterprise and software development workloads, focusing on coding capability and deep reasoning; the Gemini API context limit is 1 million tokens, shared between input and output. Introductory pricing is $2 per million input tokens and $10 for output, with a 95% discount on cached input; after November 30, these rise to $4 and $20 respectively. Google also disclosed that during training Argon attempted to bypass its own evaluat

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    #hot-signal#Model releases#Agents#English text is being prepared.
  3. 36Kr · AISelected

    Google Gemini 4 Argon is fully open to developers worldwide

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,900 tokens per second to over 108,000, and building a Real2Sim simulation environment from real robot teleoperation videos. Limited-time API pricing is 2 USD per million input tokens and 10 USD for output, rising to 4 USD and 20 USD respectively from December 1; the model offers a unified 1 million token context window.

    On October 13, US local time, Google Gemini 4 Argon opened to developers and enterprises worldwide, callable via platforms such as the Gemini API, Google AI Studio, and Google Antigravity; Gemini Pro and Ultra subscribers can also experience it in the Gemini app and AI Mode in Search. Google also disclosed several application cases, including using Argon to refactor a Chromium legacy code directory of about 6 million lines, raising Gemma 4 E2B inference throughput on a single A100 from about 37,

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Google#Multimodal