stepfun · Open weights
Step 3.7 Flash Thinking
Step 3.7 Flash Thinking is StepFun's high-efficiency multimodal MoE model with visible reasoning enabled for deeper agentic coding, long-context reasoning, tool use, and native image/video understanding. ⚠️ Note: This model routes through StepFun, so privacy and logging guarantees may be limited.
- Reasoning
- Tools
- Vision
- Structured output
- Video input
- Input
- Text, Images, Video
- Output
- Text
- Reasoning effort
- none · low · medium · high
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.20
- Output
- $1.15
- Cache read
- $0.04
Reference prices as published by the model provider. API example
Specifications
- Context window
- 262.1K
- Max output
- 256K
- Average output (7 days)
- 7.1K tokens
- Longer than 99% of 468 models
Side by side
Step 3.7 Flash Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Step 3.7 Flash ThinkingstepfunThis model | Step 3.5 Flash 2603stepfunRemove | |
|---|---|---|
| Provider | stepfun | stepfun |
| Discovery tier | Open weights | Open weights |
| Added | May 29, 2026 | Apr 14, 2026 |
| Limits | ||
| Context window | 262.14K | 262.14K |
| Max output | 256KBest | 65.54K |
| Average output | 7.1K tokens | 3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.20 | $0.10Best |
| Output | $1.15 | $0.30Best |
| Cache read | $0.04 | — |
| Performance | ||
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | — | 17.0 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | No |
| Vision | Yes | No |
| Structured output | Yes | No |
| Video input | Yes | No |
| Reasoning effort | none, low, medium, high | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Step 3.7 Flash Thinking
- Step 3.5 Flash 2603
Intelligence Index
- Step 3.7 Flash Thinking—
- Step 3.5 Flash 260317.0
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 63.0%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 24.5%
GPQA Diamond
Graduate-level science questions
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 82.6%
IFBench
Following precise output instructions
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 66.5%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 32.6%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 87.4%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Step 3.7 Flash Thinking: —
- Step 3.5 Flash 2603: 63.0%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 0.0%
- About $0.20 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "stepfun/step-3.7-flash:thinking",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="stepfun/step-3.7-flash:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)stepfun
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