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Qwen · Frontier

Qwen3.7 Max

Added May 21, 2026

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Use in the app

Qwen3.7 Max is built for agentic coding, office automation, and long-running tool workflows in non-thinking mode.

  • Reasoning
  • Tools
Input
Text
Output
Text
Reasoning effort
none · high
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$1.47
Output
$4.43
Cache read
$0.30
Cache write
$1.84

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K

Side by side

Qwen3.7 Max compared

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

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Qwen3.7 MaxQwenThis modelMuse Spark OpenMetaRemove
ProviderQwenMeta
Discovery tierFrontierFrontier
AddedMay 21, 2026Oct 15, 2026
Limits
Context window1M1.05MBest
Max output65.54K—
Average output—1.3K tokens
Pricing, USD per 1M tokens
Input$1.47$1.25Best
Output$4.43$4.25Best
Cache read$0.30$0.15Best
Cache write$1.84—
Benchmark indexes
Intelligence Index—38.9
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputNoYes
Reasoning effortnone, highlow, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Qwen3.7 Max
  • Muse Spark Open

Intelligence Index

  • Qwen3.7 Max—
  • Muse Spark Open38.9
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3.7 Max: —
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3.7 Max: —
    • Muse Spark Open: 20.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen3.7 Max: —
    • Muse Spark Open: 1,279 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3.7 Max: —
    • Muse Spark Open: 1,401 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3.7 Max: —
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.7 Max: —
    • Muse Spark Open: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Qwen3.7 Max: —
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.7 Max: —
    • Muse Spark Open: 43.9%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.7 Max: —
    • Muse Spark Open: 89.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3.7 Max: —
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Qwen3.7 Max: —
    • Muse Spark Open: 58.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.7 Max: —
    • Muse Spark Open: 78.3%

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 $1.47 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-max",
    "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-max",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

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