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Qwen3.7 Plus Thinking

Added Jun 1, 2026

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Use in the app

Qwen3.7 Plus with thinking mode enabled for deeper multimodal reasoning, coding, tool use, screen reading, and productivity workflows.

  • 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
983.6K
Max output
65.5K

Side by side

Qwen3.7 Plus Thinking compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Qwen3.7 Plus ThinkingQwenThis modelMistral Medium 3MistralRemove
ProviderQwenMistral
Discovery tierBalancedBalanced
AddedJun 1, 2026Sep 25, 2025
Limits
Context window983.62KBest131.07K
Max output65.54KBest32.77K
Average output—683 tokens
Pricing, USD per 1M tokens
Input$0.40$0.40
Output$1.60Best$2.00
Cache read$0.08—
Cache write$0.50—
Performance
Throughput—107.8 tok/s
Time to first token—0.7 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—9.0
Math Index—30.3
Capabilities
ReasoningYesNo
ToolsYesNo
VisionYesYes
Video inputYesNo
Reasoning effortnone, high—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Qwen3.7 Plus Thinking
  • Mistral Medium 3

Intelligence Index

  • Qwen3.7 Plus Thinking—
  • Mistral Medium 39.0
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 30.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 31.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 4.1%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 57.8%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 76.0%

  • IFBench

    Following precise output instructions

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 39.3%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 40.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 30.3%

  • AIME

    Competition mathematics

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 44.0%

  • MATH-500

    Competition math problems

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 90.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 3.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 24.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.7 Plus Thinking: —
    • Mistral Medium 3: 31.3%

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 $0.40 per 1M input tokens with caching

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.

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

Qwen

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