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Tuesday

  2. Unite.AISelected

    Inception releases flagship diffusion large model Mercury 3

    Inception released the diffusion-architecture large model Mercury 3 on October 19, 2026, claiming it surpasses GPT-6 Luna and Claude Haiku 5.5 on multiple benchmarks, reaches a speed of 1068 tokens/s on NVIDIA GPUs, and increases the context window from 260,000 in Mercury 2.5 to 1 million tokens. The launch price is $0.10 per million input tokens and $0.35 for output, with regular pricing at $0.20 and $0.70, and cached input at $0.02. Mercury 3 can be accessed via the Inception API, OpenRouter, Baseten, and Vercel, and is compatible with the OpenAI API; Mercury 2 and 2.5 are no longer available to new users, and existing customers can use them until November 15, 2026.

    diffusion-architecture large model Mercury 3 on October 19, 2026, claiming it surpasses GPT-6 Luna and Claude Haiku 5.5 on multiple benchmarks, reaches a speed of 1068 tokens/s on NVIDIA GPUs, and increases the context window from 260,000 in Mercury 2.5 to 1 million tokens. The launch price is $0.10 per million input tokens and $0.35 for output, with regular pricing at $0.20 and $0.70, and cached input at $0.02. Mercury 3 can be accessed via the Inception API, OpenRouter, Baseten, and Vercel, an

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Reasoning#Agents

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  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.

    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 hallucinati

    Matched fieldsSummary、Body

    #company#Model releases#Open-source ecosystem#Evaluations / benchmarks

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