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Friday

  1. QbitAISelected

    Google Releases Gemini 4 Argon, Limited-Time Half-Price API

    Google has released its flagship model Gemini 4 Argon, focused on coding, professional knowledge work, and sustained execution of complex tasks. The API launch price is $2 per million input tokens and $10 per million output tokens, with the discount running through November 30; from December 1, prices return to $4 and $20. On the third-party Artificial Analysis Intelligence Index, Argon High ties with GPT-6 Astra Max and Claude Fable5.1 Max at 53 points, at an average cost of $1.99 per task, about 39% lower than Astra. Google says that on an ADK project it worked unattended continuously for 11 hours, proposed six optimization plans involving 32 files, and ran 16,892 tests, two of which were merged, reducing long-session memory preparation overhead by about 40%.

    Google has released its flagship model Gemini 4 Argon, focused on coding, professional knowledge work, and sustained execution of complex tasks. The API launch price is $2 per million input tokens and $10 per million output tokens, with the discount running through November 30; from December 1, prices return to $4 and $20. On the third-party Artificial Analysis Intelligence Index, Argon High ties with GPT-6 Astra Max and Claude Fable5.1 Max at 53 points, at an average cost of $1.99 per task, abo

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    #chinese#Model releases#Google#Multimodal
  2. The Guardian Technology

    Meta releases open-weight model Muse Spark Open

    Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on the desktop with two Mac Studios equipped with that chip.

    Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on th

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    #hot-signal#Model releases#Open-source ecosystem#Meta
  4. The New StackSelected

    Meta releases 816B open-source model Muse Spark Open

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses the Muse Model License; companies with annual revenue over $20 million must first notify Meta and sign a separate agreement to offer model services, and users must prominently label Built with Muse Spark Open in product interfaces.

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchma

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    #hot-signal#Model releases#Open-source ecosystem#Reasoning
  5. 36Kr · AISelected

    StepFun open-sources Step 5 Preview, a 600B-parameter MoE model that ranks second on the OpenRouter weekly chart

    StepFun officially releases the Step 5 Preview model weights, with BF16 and FP8 versions launched simultaneously on Hugging Face and ModelScope. The model uses a sparse MoE architecture, with 600B total parameters and 27B activated parameters, supports a 1 million Token context window, and scores 44 on Artificial Analysis's Intelligence Index, placing it in the global top three open-source models. As of October 15, 2026, its Token usage ranks second on both the OpenRouter weekly and daily charts, with about 18.8 trillion Tokens used in the past seven days; according to evaluation data at release, its average cost per task is $0.71, about 1/8 that of Claude Opus 5.

    StepFun officially releases the Step 5 Preview model weights, with BF16 and FP8 versions launched simultaneously on Hugging Face and ModelScope. The model uses a sparse MoE architecture, with 600B total parameters and 27B activated parameters, supports a 1 million Token context window, and scores 44 on Artificial Analysis's Intelligence Index, placing it in the global top three open-source models. As of October 15, 2026, its Token usage ranks second on both the OpenRouter weekly and daily charts

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    #hot-signal#Model releases#Open-source ecosystem#Hugging Face

Tuesday

  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
  7. Latent SpaceSelected

    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.

    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 tex

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

Wednesday

  3. The New StackSelected

    Google releases flagship model Gemini 4 Argon, with a shared 1 million token budget for input and output

    Google has opened the new flagship model Gemini 4 Argon to developers in the paid tier of the Gemini API, Google AI Studio, and tools such as the Gemini App, with the call name gemini-4-argon. The model uses a shared budget of 1 million tokens combined for input and output, different from the 1 million output cap stated at its September 30 release; long prompts will eat into reply space. Pricing before November 30 is $2 per million input tokens and $10 for output, rising to $4 and $20 afterward, with cached input discounted 95% during the promotional period. Google says it ranks first on the Vals Index, scores 87% on ERQA, and has already been made available to coding agents in Antigravity.

    Google has opened the new flagship model Gemini 4 Argon to developers in the paid tier of the Gemini API, Google AI Studio, and tools such as the Gemini App, with the call name gemini-4-argon. The model uses a shared budget of 1 million tokens combined for input and output, different from the 1 million output cap stated at its September 30 release; long prompts will eat into reply space. Pricing before November 30 is $2 per million input tokens and $10 for output, rising to $4 and $20 afterward,

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    #hot-signal#Model releases#Google#Agents