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Friday

  3. Story4 reports · 2 facts

    StepFun updates Step Code to v0.1.4

    On October 15, 2026, StepFun updated Step Code, the open-source agent harness built around Step 5 Preview, to v0.1.4, helping agents track goals in long and complex tasks, keep making progress, and check against verifiable results whether tasks are truly completed. The company says that since Step Code was open-sourced, four versions have been released, roughly once every 3 working days, driven mainly by community feedback. On the same day, StepFun officially opened 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, and supports a 1 million Token context window.

    ether tasks are truly completed. The company says that since Step Code was open-sourced, four versions have been released, roughly once every 3 working days, driven mainly by community feedback. On the same day, StepFun officially opened 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, and supports a 1 million Token context window.

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Thursday

Wednesday

  1. Story3 reports · 2 facts

    Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model

    On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.

    With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eigh

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