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, 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.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 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 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.

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 (function(){var f=document.

getElementById("mtp-step5-x");window.addEventListener("message",function(e){if(f&&e.source===f.contentWindow&&e.data&&e.data.mtpH){f.style.height=e.data.mtpH+"px";}});})(); Benchmarks: Company-Reported vs Independent Step 5 Preview ran at High effort, while rivals ran at Max.

StepFun reports these results, via RuntimeWire: BenchmarkStep 5 PreviewClaude Opus 5GPT-6 AstraFrontierFinance66.469.755DRACO83.387.676.8 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.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 typePriceInput, cache miss$1.00Input, cache hit$0.05Output, 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 What is Step 5 Preview?It is StepFun’s flagship MoE model for agentic coding, knowledge work, and finance.Is Step 5 Preview open weight?Not yet.

StepFun schedules open weights for October 15, 2026.How large is the context window?1M tokens, per StepFun’s documentation.How much does it cost?$1.00 per 1M input tokens and $2.70 per 1M output tokens.Check out the Technical Details.All credit goes to the researcher of this project.

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