Qwen · Efficient

Qwen3.7 Flash

Added Jul 25, 2026

Qwen3.7 Flash is Qwen's fast multimodal model for coding, search and computer-use agents, visual understanding, object recognition, spatial reasoning, and stable end-to-end task execution.

Reasoning
Tools
Vision
Structured output
Video input

Input: Text, Images, Video
Output: Text
Reasoning effort: none · high
Discovery tier: Efficient

Pricing

USD per 1M tokens

Input: $0.03
Output: $0.13
Cache read: $0.006
Cache write: $0.038

Reference prices as published by the model provider. API example

Specifications

Context window: 991.8K
Max output: 65.5K

Side by side

Compare models

Put Qwen3.7 Flash next to up to three other models. The comparison stays in the link, so you can share it.

TrySchematron V2 Turbo Synth 2.5 Flash Preview Solar Pro 4 All 3

Public evaluations

Benchmarks

There are no public benchmark results for Qwen3.7 Flash yet.

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: 34.7%; About $0.0217 per 1M input tokens with caching
Request success (24h): 99.9%; 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.

Hourly request success · last 72 hoursLowest hour 98.1%

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.

Read the quickstart

curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.7-flash",
    "reasoning_effort": "none",
    "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="qwen/qwen3.7-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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