Step 3.7 Flash

by StepFun

Released 57 days after Step 3.5 Flash · All StepFun releases

Step 3.7 Flash brings StepFun's Step series down to a high-efficiency footprint, released as open weights in May 2026. The model is a sparse mixture-of-experts design that activates roughly 11 billion of its 198 billion total parameters per token and pairs the language backbone with a compact vision encoder, adding native image input that the earlier 3.5 Flash generation lacked. Selectable reasoning levels let callers trade depth for speed on each request, and the model is tuned for agent frameworks with reliable tool calling, MCP support, and structured output. It suits coding agents, search and retrieval workflows, and screenshot-driven automation that want capable multimodal behavior at small-model inference cost, backed by a 262K context window and outputs up to 256K tokens.

Key info

Input
Output
Features
Context window
262K
Max output
256K
Input price
$0.20 /1M
Output price
$1.15 /1M
Released
  • US residency available
  • Zero data retention on pay-as-you-go
  • No training by default
  • GDPR DPA available

Available routes

Step 3.7 Flash runs on 2 different routes through the Opper gateway. Compare residency, ZDR, and training posture at a glance — full data-handling detail per route below.

ProviderRegionZero data retentionTrainingInputOutput
USNo$0.20$1.15
USNo$0.20$1.15

Uptime and availability

Step 3.7 Flash runs on 2 independently monitored routes through the Opper gateway. A direct integration leaves you on one of them, the gateway serves whichever is healthy.

100%effective uptime, last 30 days
Averaged across these routes on their own, a single provider reached 100% over the same window.
Daily status through the gateway, which serves each request from whichever route is healthy.
2 monitored providers serve Step 3.7 Flash.

Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider Opper monitors.

Data handling per route

Each route hosting Step 3.7 Flash has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

DeepInfra United States🇺🇸

Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; unknown; DPA available.

Zero data retention
On by default on Pay-as-you-go. Derived from the logging, moderation and training facts.
Training
No training on customer data.
Logging
None
Moderation
Not established
Caching
Not established
Subprocessor access
Not established
GDPR DPA
DPA available
Transfer mechanism
unknown

Novita United States🇺🇸

Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; SCCs; DPA available.

Zero data retention
On by default on Pay-as-you-go. Derived from the logging, moderation and training facts.
Training
No training on customer data.
Logging
None
Moderation
Not established
Caching
Not established
Subprocessor access
Not established
GDPR DPA
DPA available
Transfer mechanism
SCCs

Benchmarks

Independent benchmark scores — composite indices for reasoning, coding, and math, plus individual eval scores where available.

Global rank#195 of 641 LLMs
TierEfficient
Output speed114 tok/s
First token1.57s
Intelligence Index19.5
Coding Index39.6
Reasoning & knowledge
GPQA Diamond
81%
Humanity's Last Exam
21%
Long-context reasoning
74%
Coding
SciCode
44%
Agentic & tool use
Terminal-Bench Hard
36%
τ²-Bench Telecom
99%
Math & instruction following
IFBench
67%

Get started

Call Step 3.7 Flash through the Opper gateway with one API key. Let your coding agent set it up, or call it directly — Opper is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.OPPER_API_KEY,
baseURL: "https://api.opper.ai/v3/compat",
});
const completion = await client.chat.completions.create({
model: "deepinfra/stepfun-ai/Step-3.7-Flash",
messages: [{ role: "user", content: "Hello" }],
});
console.log(completion.choices[0].message.content);

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