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Qwen · Balanced

Qwen3 Max 2026-01-23

Added Jan 26, 2026

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Use in the app

Qwen3 Max is Alibaba's flagship Qwen 3 reasoning model with native tool use (web search, web extractor, code interpreter) and a 256K context window.

Input
Text
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$1.20
Output
$6.00

Reference prices as published by the model provider. API example

Specifications

Context window
256K
Max output
32.8K

Side by side

Qwen3 Max 2026-01-23 compared

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

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Qwen3 Max 2026-01-23QwenThis modelMuse Spark 1.1MetaRemove
ProviderQwenMeta
Discovery tierBalancedBalanced
AddedJan 26, 2026Jul 9, 2026
Limits
Context window256K1MBest
Max output32.77K65.54KBest
Average output—460 tokens
Pricing, USD per 1M tokens
Input$1.20Best$1.25
Output$6.00$4.25Best
Cache read—$0.15
Benchmark indexes
Intelligence Index—33.7
Coding Index—71.3
Capabilities
ReasoningNoYes
VisionNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Qwen3 Max 2026-01-23
  • Muse Spark 1.1

Intelligence Index

  • Qwen3 Max 2026-01-23—
  • Muse Spark 1.133.7
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 71.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 38.8%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 13.7%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 850 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 1,211 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 14.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 77.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 7.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 46.2%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 89.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 6.1%

  • SciCode

    Scientific computing in Python

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 58.8%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 Max 2026-01-23: —
    • Muse Spark 1.1: 77.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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-max-2026-01-23",
    "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-max-2026-01-23",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

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