stepfun-ai

stepfun-ai / Step-3.5-Flash

Public

Fast, Sharp & Reliable Agentic Intelligence

1,473
48
100% credibility
Found Feb 02, 2026 at 374 stars 4x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Step 3.5 Flash is an open-source sparse Mixture-of-Experts language model delivering high-efficiency reasoning, coding, and agentic performance with local deployment options and API access.

How It Works

1
🔍 Discover Step 3.5 Flash

You hear about a super-fast, powerful AI model that rivals top chatbots but runs efficiently on everyday hardware.

2
💬 Chat online for free

Sign up at OpenRouter or StepFun, grab a free trial key, and start talking to the AI right in your browser.

3
📖 Explore its strengths

Check the blog, benchmarks, and demos to see how it excels at reasoning, coding, and handling long conversations.

4
Choose your path
☁️
Cloud mode

Keep chatting via simple apps with your key for instant access.

💻
Local mode

Download the model and launch it on your Mac or PC for private use.

5
🚀 Set up locally

Follow easy guides to run it with tools like llama.cpp on your hardware.

🎉 Your AI powerhouse

Enjoy lightning-fast reasoning and coding help, all running securely on your own machine.

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Star Growth

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AI-Generated Review

What is Step-3.5-Flash?

Step-3.5-Flash is an open-source 196B-parameter MoE model that activates just 11B params per token for fast, sharp agentic intelligence. It powers reasoning, coding agents, and long-context tasks at 100-300 tok/s on local hardware like Mac Studio or NVIDIA DGX Spark, using backends like llama.cpp, vLLM, or SGLang. Developers get elite benchmarks—74% on SWE-bench Verified, 51% on Terminal-Bench—without cloud costs, via simple API calls or cookbooks for hybrid local agents.

Why is it gaining traction?

It crushes agentic evals like τ²-Bench (88%) and xbench-DeepSearch (84%) while sipping VRAM, outpacing denser rivals in speed and cost at 256K context. Cookbooks streamline integrations for fast GitHub actions, Claude Code, Roo Code, and local RAG setups, letting devs deploy agentic workflows instantly. With 1147 stars, it's hooking builders tired of slow, pricey closed models.

Who should use this?

Agent builders crafting coding assistants or research tools, backend devs running privacy-first evals on SWE-bench, or frontend teams needing fast GitHub search/timetable agents without latency spikes. Ideal for those evaluating 3.5-scale models for production prototypes.

Verdict

Grab it for agentic experiments—benchmarks deliver, local deploys shine—but 1.0% credibility and nascent ecosystem mean rigorous testing before prime time. Solid starter with growing docs; watch for maturity. (198 words)

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