StepFun Launches Step 5 Preview: A 600B-Total, 27B-Active MoE Model With 1M Context for Long-Horizon Agentic Work

StepFun released Step 5 Preview, a sparse Mixture-of-Experts model with about 600B total parameters and about 27B activated per token, offering a 1M-token context window and text, image, and video input through a hosted API. StepFun reports the model scored 66.4 on FrontierFinance and 83.3 on DRACO at High reasoning effort, while Artificial Analysis independently scored it 44 on its Intelligence Index against a 24 median for reasoning models in a similar price tier, with output at 99.8 tokens per second. API list pricing is $1.00 per 1M input tokens on a cache miss, $0.05 on a cache hit, and $2.70 per 1M output tokens including reasoning, with open weights scheduled for October 15, 2026.

StepFun has released Step 5 Preview https://stepfun.com/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 it deployable? Yes, as a hosted API and on the StepFun platform https://platform.stepfun.ai/ . Self-hosting waits for open weights. StepFun says open weights land on October 15, 2026. By simple arithmetic, 600B parameters need about 1.2 TB in BF16, before KV cache. Plan for multi-GPU server hardware once weights ship. What StepFun Shipped Step 5 Preview is a sparse Mixture-of-Experts MoE model. It holds about 600B total parameters and activates about 27B per token. That is roughly 4.5% of the weights per token. The official model documentation https://platform.stepfun.ai/docs/en/guides/models/step-5-preview lists these specs: - Model ID: step-5-preview - Context window: 1M tokens - Input: text, images, and video - Output: text - Reasoning effort: low , medium , and high - Streaming, tool calling, JSON Mode, JSON Schema, and prompt caching On research tasks, StepFun team states the model coordinated 950 web fetches in a single agent action. StepFun team also documents a Claude Code integration https://platform.stepfun.ai/docs/en/step-plan/integrations/claude-code through its Step Plan. Architecture: Narrow and Deep StepFun did not widen the network. It stacked 92 Transformer layers in a narrow-deep layout, according to Pandaily https://pandaily.com/stepfun-step-5-preview-600b-moe-1m-context . The research team argues deeper stacks give longer paths for implicit multi-hop reasoning. This matters during long prefill, when agents search, run code, and read tool returns. Training leans on on-policy, long-horizon reinforcement learning. StepFun cites bit-wise train and inference alignment across MoE routing. Other listed techniques include MTP-3 speculative decoding, FP8 MoE, and KV-cache offload. StepFun reports more than 3x end-to-end speedup for long-horizon RL. Interactive Explainer Benchmarks: Company-Reported vs Independent Step 5 Preview ran at High effort, while rivals ran at Max. StepFun reports these results, via RuntimeWire https://runtimewire.com/article/stepfun-step-5-preview-600b-agent-model-pricing : | Benchmark | Step 5 Preview | Claude Opus 5 | GPT-6 Astra | |---|---|---|---| | FrontierFinance | 66.4 | 69.7 | 55 | | DRACO | 83.3 | 87.6 | 76.8 | On coding, StepFun reports 67.7 on DeepSWE v1.1, 49.0 on StepCodeBench, and 80.5 on ProgramBench. GPT-6 Astra and Claude Opus 5 stay ahead on all 3. StepCodeBench is StepFun’s own benchmark. StepFun also ran 2 agent experiments lasting 24 hours each. In the first, the model tuned an H100 kernel to 508 TFLOPS, against 493 for Claude Opus 5. In the second, it raised Qwen3-30B-A3B on AIME24 from 53.3% to 60% through automated post-training. The independent check comes from Artificial Analysis https://artificialanalysis.ai/models/step-5 . It scores Step 5 Preview at 44 on its Intelligence Index. The median for reasoning models in a similar price tier is 24. It measured output at 99.8 tokens per second on StepFun’s API. Pricing StepFun’s API list prices per 1M tokens: | Token type | Price | |---|---| | Input, cache miss | $1.00 | | Input, cache hit | $0.05 | | Output, including reasoning | $2.70 | Artificial Analysis puts the medians for comparable models at $1.88 input and $10.00 output. There is 1 catch. The model generated 160M output tokens on the index run, against a 92M median. Verbose reasoning eats part of the per-token savings. Key Takeaways - StepFun’s Step 5 Preview is a 600B-total, 27B-active MoE model. - It offers a 1M-token context with text, image, and video input. - API pricing is $1.00 input and $2.70 output per 1M tokens. - Artificial Analysis scores it 44 on its Intelligence Index. - Open weights are scheduled for October 15, 2026. FAQ 1. What is Step 5 Preview? It is StepFun’s flagship MoE model for agentic coding, knowledge work, and finance. 2. Is Step 5 Preview open weight? Not yet. StepFun schedules open weights for October 15, 2026. 3. How large is the context window? 1M tokens, per StepFun’s documentation. 4. How much does it cost? $1.00 per 1M input tokens and $2.70 per 1M output tokens. Check out the Technical Details https://www.stepfun.com/step-5-preview . All credit goes to the researcher of this project. Also, feel free to follow us on Twitter https://x.com/intent/follow?screen name=marktechpost and don’t forget to join our 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ and Subscribe to our Newsletter https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}} . Wait are you on telegram? now you can join us on telegram as well. https://t.me/machinelearningresearchnews Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us https://forms.gle/MJjjVDPS7whH8Ngs6 Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.