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Step 3.7 Flash

By StepFun. 201.4 billion parameters, a context window of 262,144 tokens and the licence apache-2.0.

Facts

Released
2026-05-23 the day the repository was first published on Hugging Face Hugging Face, read
Licence
apache-2.0 the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
201.4 billion counted from the safetensors weight files Hugging Face, read
Active parameters
Unknown
Knowledge cutoff
Unknown
Context window
  • 262,144 tokens the maximum position embeddings in the model configuration model configuration, read
  • 262,144 tokens the context length OpenRouter lists OpenRouter, read
Longest output
  • 230,400 tokens the largest output OpenRouter's first provider allows OpenRouter, read
Tool calling
  • Yes OpenRouter lists tools among the supported parameters OpenRouter, read
Structured output
  • Yes OpenRouter lists structured outputs among the supported parameters OpenRouter, read
Reasoning controls
  • Yes OpenRouter lists reasoning controls, with the efforts high, medium, low OpenRouter, read
Inputs and outputs
text, image in, text out Hugging Face, read
Good for
  • Vision: Hugging Face files it under image-text-to-text Hugging Face, read
  • Reasoning: OpenRouter lists reasoning controls for it OpenRouter, read

Prices

Prices in US dollars per million tokens, as each source lists them
ProviderInputOutputContextAs ofSource
Nvidia direct0 USD0 USD256,000 older than 30 days; check the providermodels.dev, read
DeepInfra through OpenRouter0.160 USD0.920 USD262,144OpenRouter endpoints, read
Deep Infra direct0.200 USD1.15 USD262,144 older than 30 days; check the providermodels.dev, read
Hugging Face direct0.200 USD1.15 USD262,144 older than 30 days; check the providermodels.dev, read
Novita through OpenRouter0.200 USD1.15 USD262,144OpenRouter endpoints, read
StepFun through OpenRouter0.200 USD1.15 USD256,000OpenRouter endpoints, read

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
IQ3_XXS70.6 GiB86.6 GiB123 GiB
Q3_K_M87.4 GiB104 GiB140 GiB
Q3_K_L95.5 GiB113 GiB149 GiB
IQ4_XS97.8 GiB115 GiB151 GiB
Q4_K_S104 GiB122 GiB158 GiB

Start it with a local runtime

  • Ollama
    ollama run hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
  • LM Studio
    lms get stepfun-ai/Step-3.7-Flash-GGUF
    lms server start
  • llama.cpp server
    llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S --jinja
  • vLLM
    vllm serve stepfun-ai/Step-3.7-Flash
  • SGLang
    sglang serve --model-path stepfun-ai/Step-3.7-Flash --port 30000

Use it from your harness

Set up for DeepInfra with the model stepfun-ai/Step-3.7-Flash. Each endpoint page has the same setup for its own address.

OpenCode

Put this in opencode.json in your project folder:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "deepinfra": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "DeepInfra",
      "options": {
        "baseURL": "https://api.deepinfra.com/v1/openai",
        "apiKey": "{env:DEEPINFRA_TOKEN}"
      },
      "models": {
        "stepfun-ai/Step-3.7-Flash": {
          "name": "Step 3.7 Flash",
          "limit": {
            "context": 262144,
            "output": 230400
          }
        }
      }
    }
  }
}
  • OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.

From OpenCode documentation, read .

Pi

Put this in ~/.pi/agent/models.json:

{
  "providers": {
    "deepinfra": {
      "baseUrl": "https://api.deepinfra.com/v1/openai",
      "api": "openai-completions",
      "apiKey": "$DEEPINFRA_TOKEN",
      "models": [
        {
          "id": "stepfun-ai/Step-3.7-Flash"
        }
      ]
    }
  }
}
  • The apiKey field can name an environment variable as $NAME.

From Pi documentation, read .

Codex

Codex speaks only the Responses API, and DeepInfra documents no Responses address. A gateway that offers one can sit in between.

  • Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.

From Codex documentation, read .

Claude Code

Put this in ~/.claude/settings.json, or variables in your shell:

export ANTHROPIC_BASE_URL="https://api.deepinfra.com/anthropic"
export ANTHROPIC_AUTH_TOKEN="$DEEPINFRA_TOKEN"
export ANTHROPIC_MODEL="stepfun-ai/Step-3.7-Flash"
claude
  • Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.

From Claude Code documentation, read .

Other ways to reach it

  • Hugging Face, model stepfun-ai/Step-3.7-Flash
  • Nvidia, model stepfun-ai/step-3.7-flash
  • OpenRouter, model stepfun/step-3.7-flash
  • Ollama, model hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S

Published results

These are results other people published. Baltor did not run them and does not rank models by them.