What exactly is Step 3.7 Flash?
Step 3.7 Flash is StepFun's latest multimodal Mixture-of-Experts model, built for agentic coding, visual understanding, and long-context tasks. It pairs a 196B-parameter language backbone with a 1.8B Vision Transformer encoder for native image and video processing.
The MoE architecture activates approximately 11B parameters per token during inference — keeping compute cost close to an 11B dense model while maintaining a 198B total parameter budget. The model ships with selectable reasoning depth, tool use, and multimodal fusion as production defaults.
API Pricing
Architecture: what makes it fast and capable
Sparse MoE (mixture of experts)Each token is routed to a specialized subset of expert sub-networks within the 198B parameter space. With only ~11B parameters active per forward pass, the model achieves frontier-adjacent reasoning at a fraction of the compute cost of a comparable dense model.
Native multimodal fusionText, images, and video frames are processed in a single forward pass through a dedicated 1.8B Vision Transformer encoder. There are no separate vision adapters — the model was trained on multimodal tokens from scratch, enabling natural cross-modal reasoning.
Selectable reasoning depthCallers can set reasoning intensity to low, medium, or high per request. Low is optimized for speed and cost; high applies deeper chain-of-thought computation suited for complex coding, planning, and visual analysis tasks.
256K context windowPass entire codebases, document collections, long conversation histories, or multi-frame video sequences in a single request — no chunking required for most workloads.
Core capabilities
Text, image & video inputInclude screenshots, charts, documents, and short video clips alongside text in the same prompt. Natively understood — not approximated by a separate model.
Agentic coding workflowsScores 56.3% on SWE-Bench Pro and 76.5% on SWE-Bench Verified. Supports Advisor Mode — runs the full agentic loop and escalates to a larger model only at planning or failure recovery points, reaching 97% of Claude Opus 4.6 performance at ~1/9th the per-task cost.
Visual analysis95.3% on V* with Python Tool, 89.1% on HR-Bench 4K, 61.9% on Android Daily (beats Kimi K2.6 and GLM 5V Turbo). Handles high-resolution images, GUI inspection, bounding box analysis, and screenshot-based debugging.
Search and research71.7% on ResearchRubrics (vs GPT 5.5 at 61.5%), 92.8% on DeepSearchQA. Search is integrated into the reasoning loop — not a separate add-on.
Thinking preservationReasoning traces persist across turns, reducing redundant computation in iterative development and multi-step planning workflows.
Benchmark performance
Who should use Step 3.7 Flash?
Agent developersEngineers building multi-step coding agents that need consistent behavior across different scaffolds (KiloCode, OpenClaw, Claude Code, RooCode). Step 3.7 Flash narrows cross-harness variance to 64.5–71.5% — more predictable than most alternatives.
Visual AI product teamsApplications that process images, screenshots, charts, or video alongside text — from UI-aware agents to document intelligence pipelines — without managing a separate vision model.
Research and analysis teamsTeams running competitive analysis, literature reviews, or multi-source synthesis tasks where both text and visual inputs need to be processed together coherently.
Cost-conscious production teamsAt $0.26/M input, Step 3.7 Flash delivers multimodal reasoning and vision at a fraction of frontier model pricing. Advisor Mode brings SWE-Bench Verified performance close to Claude Opus 4.6 at $0.19/task vs $1.76/task.
Long-context workloadsLegal, finance, and research teams processing large documents or multi-frame video that exceed standard context windows. The 256K window eliminates most chunking pipelines entirely.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Step 3.7 Flash is documented as suited to simple Q&A, summarization, rewriting, and information extraction. It is also suited to general reasoning and multi-step tasks.
The model is documented for complex reasoning, math, planning, and code analysis. It is also suited to code generation, file editing, and complex task coordination in terminal, IDE, or agent workflows.
It is documented for multimodal tasks such as image understanding, image-text-to-text, and video-text-to-text workflows. It is also intended for GUI-oriented tasks that use text, screenshots, or images as input.
The model is documented for agentic workflows that combine perception, search, and reasoning, including tool calling and stable structured outputs.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "stepfun/step-3.7-flash", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "stepfun/step-3.7-flash", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"stepfun/step-3.7-flash","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 19.5 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Coding | 39.6 | Composite score across standardised coding evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| SWE-bench Verified | 76.5% | Resolving verified real GitHub issues | Source | September 22, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Step 3.7 Flash This page | Coding + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents | ||||
| Agentic workflows and structured output |
Step 3.7 Flash has a 256,000 tokens context window and can return up to 256,000 tokens.
Step 3.7 Flash takes image, text, video as input and returns text.
Use stepfun/step-3.7-flash as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Step 3.7 Flash became available on June 1, 2026.
Step 3.7 Flash is priced at input $0.27508 / 1M tokens, output $1.58171 / 1M tokens, cached input $0.055016 / 1M tokens.
Yes, Step 3.7 Flash can stream responses as they are generated.
Yes, Step 3.7 Flash accepts image input alongside text.
Step 3.7 Flash was built by StepFun.
Yes, its capabilities include reasoning alongside function calling and structured outputs.
Yes, it supports function calling, tool use, and parallel tool calls.
