---
title: "StepFun's Step 3.7 Flash Runs Agentic Loops at One-Ninth the Cost of Claude"
subtitle: "StepFun open-sources a 198B MoE vision-language model that runs like an 11B model, targets agentic coding loops at a fraction of frontier costs"
url: "https://alphasignal.ai/news/stepfun-s-step-3-7-flash-runs-agentic-loops-at-one-ninth-the-cost-of-claude"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-05-23T02:13:46.000Z"
updated: "2026-06-09T02:01:41.245Z"
type: "Model"
topics:
  - "Llms"
  - "Agents"
subtopics:
  - "Vision Language"
  - "Mixture Of Experts"
  - "Tool Use"
upvotes: 3105
source_url: "https://huggingface.co/stepfun-ai/Step-3.7-Flash"
comments: 0
truncated: true
---

# StepFun's Step 3.7 Flash Runs Agentic Loops at One-Ninth the Cost of Claude

> StepFun open-sources a 198B MoE vision-language model that runs like an 11B model, targets agentic coding loops at a fraction of frontier costs

[Step 3.7 Flash](https://huggingface.co/stepfun-ai/Step-3.7-Flash) is StepFun's latest open-weight model, and it is built around a single premise: running agentic loops at scale should not cost a fortune. The model is a 198B-parameter sparse Mixture-of-Experts (MoE) vision-language model that activates only about 11B parameters per token during inference. That gap between total and active parameters is the entire cost story.

## A 198B model that runs like an 11B one

In MoE architectures, only a subset of "expert" sub-networks fires per forward pass. This keeps inference compute closer to an 11B dense model while maintaining a 198B total parameter budget. The practical result: it delivers a throughput of up to 400 tokens per second. 

Unlike traditional dense models, Step 3.5 Flash (the predecessor) uses a fine-grained routing strategy with 288 routed experts per layer plus one shared expert that is always active, selecting only the top-8 experts per token. The model retains the "memory" of a 196B parameter model but executes with the speed of an 11B model. Step 3.7 Flash carries this same architecture forward, adding a new critical piece.

Step 3.5 Flash was text-only. Step 3.7 Flash adds a dedicated 1.8B Vision Transformer that injects image representations into the language context, enabling it to parse charts, PDFs, UI wireframes, and app GUIs without a separate vision API call. 

## The real headline: agentic cost efficiency

Benchmarks are useful, but the number that matters most for production teams is cost per agentic task. A model that runs roughly 11B parameters of compute per token, prices output at $1.15 per million tokens, and runs an agentic task for about $0.19 where a frontier model runs the same task for closer to $1.76 , for teams running thousands of agent loops a month, that ratio is the headline. 

StepFun achieves this through a feature they call Advisor Mode. The model runs the agentic loop end-to-end , calling tools, reading results, iterating , and escalates to a larger advisor model only at specific inflection points, such as planning or recovering from repeated failures. Most of the run stays at executor cost. With Advisor Mode enabled on SWE-Bench Verified, StepFun reports Step 3.7 Flash reaches 97% of Claude Opus 4.6's coding performance at roughly one-ninth the per-task cost ($0.19 vs. $1.76 per task). These are StepFun's internal figures.

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## Sources

- [stepfun-ai/Step-3.7-Flash · Hugging Face](https://huggingface.co/stepfun-ai/Step-3.7-Flash)
- [Step 3.7 Flash — Official Blog](https://static.stepfun.com/blog/step-3.7-flash/)
- [StepFun Releases Step 3.7 Flash — MarkTechPost](https://www.marktechpost.com/2026/05/29/stepfun-releases-step-3-7-flash-a-198b-moe-vision-language-model-for-coding-agents-and-search-workflows/)
- [StepFun Open-Sources Step 3.7 Flash — Pandaily](https://pandaily.com/stepfun-open-source-step-3-7-flash-llm-agent-may2026)
- [Step 3.7 Flash — Digital Applied deep-dive](https://www.digitalapplied.com/blog/stepfun-step-3-7-flash-196b-moe-agentic-vision-release)