stepfun-ai/Step-5-Preview

Step 5 Preview

StepFun's flagship model for agentic work

600B total parameters · 27B active parameters · Up to 1M context

Introduction · Capabilities · Specifications · Performance · Deployment · Resources · License

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Introduction

Step 5 Preview is StepFun's flagship model for agentic work. It delivers frontier-level performance across software engineering and professional knowledge work, with particular strength in finance.

Step 5 Preview is a sparse Mixture-of-Experts (MoE) model with 600B total parameters and 27B active parameters. It supports up to 1 million tokens of context and accepts text, images, and video, enabling sustained reasoning and tool use across complex tasks.

Key Capabilities

Software Engineering

Step 5 Preview supports repository-level software engineering, frontend and visual development, and programming for devices. It can navigate large codebases, coordinate changes across files, run and verify code, and iterate on the results.

Long-Horizon Agentic Execution

Step 5 Preview can carry out tasks that require repeated tool calls, execution, verification, and revision, drawing on long-context information throughout the workflow.

Professional Knowledge Work

The model combines research, multimodal understanding, and tool use to analyze long documents, charts, screenshots, images, and video, and produce structured reports and analyses.

A Focus on Finance

Step 5 Preview is particularly strong in financial tasks that combine quantitative reasoning with analysis of financial data and supporting documents.

Model Specifications

| Specification | Step 5 Preview | | --- | --- | | Architecture | Sparse Mixture-of-Experts (MoE) | | Total Parameters | 600B | | Active Parameters | 27B | | Context Window | Up to 1M tokens | | Maximum Output | Up to 1M tokens | | Input | Text, image | | Output | Text |

Performance

The following results summarize Step 5 Preview's performance across coding, agentic tasks, professional knowledge work, and finance.

Evaluation Notes

• Recommended inference settings: temperature=1.0, top_p=0.95, top_k=0.
• Step 5 Preview results shown above use the High reasoning setting.
• GDPval-AA v2.1 scores are based on results from Artificial Analysis as of September 20, 2026.
• DeepSWE v1.1 evaluations use the SWE-agent harness with temperature=1.0 and top_p=0.95.

For evaluation context, methodology, and detailed case studies, see the Step 5 Preview Model Page.

Deployment

Step 5 Preview can be served through an OpenAI-compatible API with vLLM or SGLang, or used for offline generation with the standalone Transformers implementation. The examples below cover FP8 and BF16 checkpoints.

Use a framework build that includes Step 5 Preview support, including the reasoning, tool-call, and speculative decoding features used in the serving commands.

Deployment Configurations

| Framework | Precision | Hardware | Parallelism | Interface | | --- | --- | --- | --- | --- | | vLLM | FP8 | One B300 node, 4 GPUs | TP4, TEP4, or DEP4 | OpenAI-compatible API | | vLLM | FP8 | One H200 node, 8 GPUs | TP8, TEP8, or DEP8 | OpenAI-compatible API | | vLLM | BF16 | One B300 node, 8 GPUs | TP8, TEP8, or DEP8 | OpenAI-compatible API | | vLLM | BF16 | Two H200 nodes, 16 GPUs | TP16, TEP16, or DEP16 | OpenAI-compatible API | | Transformers | FP8 | One H200 node, 8 GPUs | TP8 / EP8 | Offline generation | | Transformers | BF16 | Two H200 nodes, 16 GPUs | TP16 / EP16 | Offline generation (see validation note below) | | SGLang | FP8 | One B300 node, 4 GPUs | TP4/EP4 | OpenAI-compatible API | | SGLang | FP8 | One H200 node, 8 GPUs | TP8/EP8 | OpenAI-compatible API |

DP, TP, and EP refer to data, tensor, and expert parallelism, respectively. TEP combines tensor and expert parallelism; DEP combines data and expert parallelism. These example configurations are sized for the deployments below. Actual memory requirements, usable context length, and concurrency vary with the checkpoint, inference framework, and KV cache allocation.

Deployment guides: [vLLM](#vllm) · [Transformers](#transformers) · [SGLang](#sglang) · [Send a Request](#send-a-request)

vLLM

The examples below expose Step 5 Preview through an OpenAI-compatible API at port 18000, with step5 as the model name used in requests.

Environment Setup

Pull the Step 5 Preview image and start an interactive container with GPU access:

docker pull vllm/vllm-openai:step-5-preview

docker run --rm -it \
--gpus all \
--ipc=host \
--network=host \
--volume "$HOME/.cache/huggingface:/root/.cache/huggingface" \
--entrypoint /bin/bash \
vllm/vllm-openai:step-5-preview

Run the serving commands below inside the container. If the checkpoint requires authentication, sign in with hf auth login before starting the server.

FP8: Single Node on 4 B300 GPUs

Choose one of the following parallel configurations for a single node with four B300 GPUs:

| Mode | Tensor parallel | Data parallel | Expert parallel | | :---: | :---: | :---: | :---: | | TP4 | 4 | 1 | Disabled | | TEP4 | 4 | 1 | 4 | | DEP4 | 1 | 4 | 4 |

Make all four GPUs visible, then run the command for your chosen configuration:

export CUDA_VISIBLE_DEVICES=0,1,2,3

On B300 (Blackwell), the TEP4 and DEP4 commands select MegaMoE with --moe-backend deep_gemm_mega_moe. The TP4 command uses the default MoE backend, since MegaMoE requires expert parallelism.

##### TP4: Tensor Parallelism

vllm serve stepfun-ai/Step-5-Preview \
--host 0.0.0.0 \
--port 18000 \
--served-model-name "step5" \
--tensor-parallel-size 4 \
--data-parallel-backend mp \
--distributed-executor-backend mp \
--disable-custom-all-reduce \
--max-model-len 1048576 \
--max-num-batched-tokens 16384 \
--max-num-seqs 64 \
--gpu-memory-utilization 0.85 \
--enable-prefix-caching \
--spec-method mtp \
--spec-tokens 3 \
--reasoning-parser step5 \
--enable-auto-tool-choice \
--tool-call-parser step5

##### TEP4: Tensor and Expert Parallelism

vllm serve stepfun-ai/Step-5-Preview \
--host 0.0.0.0 \
--port 18000 \
--served-model-name "step5" \
--tensor-parallel-size 4 \
--enable-expert-parallel \
--moe-backend deep_gemm_mega_moe \
--data-parallel-backend mp \
--distributed-executor-backend mp \
--disable-custom-all-reduce \
--max-model-len 1048576 \
--max-num-batched-tokens...