StepFun opens Step 5 Preview weights: 600B MoE, 1M-token context
StepFun released BF16 and FP8 weights for Step 5 Preview, its flagship long-horizon agent model, on Hugging Face and ModelScope, plus a technical blog and deployment guides; the sparse MoE model has 600B total and 27B active parameters, supports up to a 1M-token context with text and image input, and targets software engineering, professional knowledge work and finance. According to StepFun, the design uses a 92-layer narrow-deep architecture with 3:1 gated sliding-window to sparse global attention and Sparse GQA, where 8-token block indexing cuts index and top-k selection cost to one eighth, plus FP32 key states, fine-grained gradient clipping and long-horizon RL that trains context compression; it can be served via vLLM or SGLang with an OpenAI-compatible API, or run offline with Transformers. StepFun-cited data puts it at 44 on Artificial Analysis's Intelligence Index and second on OpenRouter's weekly and daily token charts (about 18.8T tokens in seven days), at roughly $0.71 per task versus $5.86 for Claude Opus 5, and reports it above Kimi K3 and GLM-5.3 on ALE-CLI, FrontierFinance and DRACO, behind GPT-6 Astra or Claude Opus 5.
Open weights let teams test long-horizon agent behavior on their own repositories and workflows rather than relying on benchmark scores alone.