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StepFun's Step 5 Preview introduces a significant architectural shift with its 1M-token context window and sparse MoE design, specifically optimized for long-horizon agentic workflows in software engineering and finance. The model leverages deep stacking (92 layers) and on-policy reinforcement learning to enhance multi-hop reasoning capabilities, offering a cost-efficient alternative to current market leaders while maintaining high computational throughput.
StepFun Launches Step 5 Preview: A 600B-Total, 27B-Active MoE Model With 1M Context for Long-Horizon Agentic Work
StepFun has released Step 5 Preview, a sparse Mixture-of-Experts model with 600B total parameters and 27B active per token. It supports a 1M-token context window and accepts text, image, and video input. The model targets long-horizon agentic work in software engineering, professional knowledge work, and finance. API access is live at $1.00 per 1M input tokens and $2.70 per 1M output tokens, and open weights are scheduled for October 15, 2026. The post StepFun Launches Step 5 Preview: A 600B-Total, 27B-Active MoE Model With 1M Context for Long-Horizon Agentic Work appeared first on MarkTechPost.
StepFun has released Step 5 Preview, its new flagship model for agentic work. The target workloads are software engineering, professional knowledge work, and finance. The main pitch is cost. StepFun team states the model delivers comparable intelligence at a substantially lower task cost. That is the ‘Pareto frontier’ framing in the launch title. Is…
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