Qwen · Balanced

Qwen3.7 Plus

Added Jun 1, 2026

Qwen3.7 Plus is Alibaba's cost-effective Qwen 3.7 multimodal agent model for coding, tool use, productivity workflows, visual understanding, screen reading, and GUI interaction.

Reasoning
Tools
Vision
Video input

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

Pricing

USD per 1M tokens

Input: $0.40
Output: $1.60
Cache read: $0.08
Cache write: $0.50

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 Plus next to up to three other models. The comparison stays in the link, so you can share it.

TryMistral Medium 3 Doubao 1.5 Vision Pro 32k GPT-3.5 Turbo All 3

Public evaluations

Benchmarks

There are no public benchmark results for Qwen3.7 Plus 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: 0.0%; About $0.40 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.

Hourly request success · last 72 hoursLowest hour 100.0%

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-plus",
    "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-plus",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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