---
title: StepFun: Step 3.5 Flash — Fast Reasoning LLM | ModelsLab
description: Run StepFun: Step 3.5 Flash for 100-300 tok/s agentic reasoning and 256K context. Try this 196B MoE model with 11B active params now.
url: https://modelslab.com/stepfun-step-35-flash
canonical: https://modelslab.com/stepfun-step-35-flash
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:01:02.804730Z
---

Available now on ModelsLab · Language Model

StepFun: Step 3.5 Flash
Flash Reasoning 196B MoE
---

[Try StepFun: Step 3.5 Flash](/models/open_router/stepfun-step-3.5-flash) [API Documentation](https://docs.modelslab.com)

Reason Deep. Run Fast.
---

MoE Efficiency

### 11B Active Params

Activates 11B of 196B params per token via sparse MoE for top reasoning at 11B speed.

Blazing Speed

### 100-300 Tok/s

3-way Multi-Token Prediction delivers 100-300 tok/s, peaking at 350 tok/s for coding.

Long Context

### 256K Window

Hybrid Sliding Window Attention handles 256K context with low compute overhead.

Examples

See what StepFun: Step 3.5 Flash can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/stepfun-step-3.5-flash).

Math Proof

“Solve this AIME-level math problem step-by-step: Prove that for integers n > 1, the sum of divisors function σ(n) satisfies certain bounds. Use chain-of-thought reasoning and verify with code execution if needed.”

Code Agent

“Write a Python function to parse a large codebase, identify bugs in async handlers, and suggest fixes. Output the refactored code with explanations.”

Logic Chain

“Analyze this complex logic puzzle involving 10 agents with constraints. Deduce the solution through multi-step reasoning, listing assumptions and eliminations.”

Data Summary

“Summarize key insights from a 200K token dataset on AI benchmarks, highlighting trends in MoE vs dense models, with quantitative comparisons.”

For Developers

A few lines of code.
Agentic inference. Few lines.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about StepFun: Step 3.5 Flash
---

[Read the docs ](https://docs.modelslab.com)

### What is StepFun: Step 3.5 Flash?

### How fast is stepfun step 3.5 flash?

### What is StepFun: Step 3.5 Flash API used for?

### StepFun: Step 3.5 Flash model specs?

### Is stepfun: step 3.5 flash api an alternative to closed models?

### StepFun: Step 3.5 Flash vs dense models?

Ready to create?
---

Start generating with StepFun: Step 3.5 Flash on ModelsLab.

[Try StepFun: Step 3.5 Flash](/models/open_router/stepfun-step-3.5-flash) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-15*