StepFun, an AI startup, has released the open weights for its flagship Step 5 Preview model, with FP8 and BF16 checkpoints now available on Hugging Face and ModelScope. The model uses a sparse mixture-of-experts architecture with 600 billion total parameters and activates 27 billion parameters per token. It supports up to 1 million tokens of context and is positioned for long-horizon agent tasks, including code generation, document analysis, and financial workflows.
According to the details cited by Techub News, running the model requires data center-grade GPUs. The FP8 version needs either four B300 GPUs or eight H200 GPUs. In StepFun’s own benchmark testing, the model scored 67.7 on DeepSWE v1.1 and 66.4% on the FrontierFinance financial benchmark. The report said its performance is close to GLM-5.3 and Kimi K3, while trailing GPT-6 Astra and Claude Opus 5. The item cited MarkTechPost as the source of the benchmark details.
StepFun has released the open weights for its flagship Step 5 Preview model, according to Techub News. FP8 and BF16 checkpoints are now available on Hugging Face and ModelScope.
Model scale and target use cases
Step 5 Preview is built as a sparse mixture-of-experts, or MoE, model with 600 billion total parameters. It activates 27 billion parameters per token and supports context windows of up to 1 million tokens. StepFun positioned the model for long-horizon agent tasks, with use cases that include code generation, document analysis, and financial workflows.
Hardware requirements
Running the model requires data center-grade GPUs. The FP8 version needs either four B300 GPUs or eight H200 GPUs.
Benchmark results
In StepFun’s own benchmark evaluation, the model scored 67.7 on DeepSWE v1.1 and 66.4% on the FrontierFinance financial benchmark. The report said performance was close to GLM-5.3 and Kimi K3, while behind GPT-6 Astra and Claude Opus 5.
The benchmark details were attributed to MarkTechPost.
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