StepFun

StepFun: Step 3.7 Flash

stepfun/step-3.7-flash

Access Step 3.7 Flash from StepFun using Puter.js AI API.

Get Started
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms", {
    model: "stepfun/step-3.7-flash"
}).then(response => {
    document.body.innerHTML = response.message.content;
});
<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms", {
            model: "stepfun/step-3.7-flash"
        }).then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>
# pip install openai
from openai import OpenAI

client = OpenAI(
    base_url="https://api.puter.com/puterai/openai/v1/",
    api_key="YOUR_PUTER_AUTH_TOKEN",
)

response = client.chat.completions.create(
    model="stepfun/step-3.7-flash",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
)

print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
  -d '{
    "model": "stepfun/step-3.7-flash",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
  }'

Model Card

Step 3.7 Flash is a multimodal Mixture-of-Experts model from StepFun, combining a 196B-parameter language backbone with a 1.8B vision encoder and activating roughly 11B parameters per token. It natively processes text and images, supporting a 256K-token context window.

The model is designed for agentic coding, tool orchestration, long-context reasoning, and search-intensive workflows. It exposes tunable reasoning tiers (low, medium, high) so developers can trade inference cost against answer depth on a per-call basis.

Compared to Step 3.5 Flash, Step 3.7 Flash adds native vision input, improves cross-harness consistency, and achieves stronger performance on coding agent and terminal benchmarks. It targets developers building production pipelines that combine perception, retrieval, and multi-step execution.

Context Window 256K

tokens

Max Output 256K

tokens

Input Cost $0.2

per million tokens

Output Cost $1.15

per million tokens

Release Date May 28, 2026

 

Model Playground

Try Step 3.7 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.

Chat stepfun/step-3.7-flash
StepFun
Chat with Step 3.7 Flash
Powered by Puter.js

Frequently Asked Questions

How do I use Step 3.7 Flash?

You can access Step 3.7 Flash by StepFun through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.

Is Step 3.7 Flash free?

Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Step 3.7 Flash to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.

What is the pricing for Step 3.7 Flash?
Step 3.7 Flash costs $0.2 per 1M input tokens and $1.15 per 1M output tokens.
Price per 1M tokens
Input$0.2
Output$1.15
Who created Step 3.7 Flash?

Step 3.7 Flash was created by StepFun and released on May 28, 2026.

What is the context window of Step 3.7 Flash?

Step 3.7 Flash supports a context window of 256K tokens. For reference, that is roughly equivalent to 512 pages of text.

What is the max output length of Step 3.7 Flash?

Step 3.7 Flash can generate up to 256K tokens in a single response.

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Step 3.7 Flash API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

Get started with Puter.js

Add Step 3.7 Flash to your app without worrying about API keys or setup.

Read the Docs View Tutorials