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Qwen · Open weights

Qwen3.6 27B

Added Apr 23, 2026Open weights

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Use in the app

Qwen3.6 27B is a native vision-language dense model with stronger agentic coding and STEM reasoning than Qwen 3.5 27B. It also improves spatial intelligence (including object localization/detection), plus video understanding, document OCR, and visual-agent workflows.

  • Tools
  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$2.24

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K

Side by side

Qwen3.6 27B compared

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

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Qwen3.6 27BQwenThis modelGemma 3 4B ITGoogleRemove
ProviderQwenGoogle
Discovery tierOpen weightsOpen weights
AddedApr 23, 2026Jan 1, 2024
Limits
Context window262.14KBest128K
Max output65.54KBest8.19K
Average output—115 tokens
Pricing, USD per 1M tokens
Input$0.20$0.20Best
Output$2.24$0.20Best
Performance
Request success (24h)100.0%Best99.8%
Benchmark indexes
Intelligence Index—4.8
Coding Index—2.7
Math Index—12.7
Capabilities
ToolsYesNo
VisionYesNo
PDF inputNoYes
Video inputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3.6 27B
  • Gemma 3 4B IT

Intelligence Index

  • Qwen3.6 27B—
  • Gemma 3 4B IT4.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 2.7

  • Math Index

    Composite of the math evaluations

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 12.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 6.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 5.3%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 29.1%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 41.7%

  • IFBench

    Following precise output instructions

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 28.3%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 11.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 12.7%

  • AIME

    Competition mathematics

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 6.3%

  • MATH-500

    Competition math problems

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 76.6%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 0.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 5.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.6 27B: —
    • Gemma 3 4B IT: 6.7%

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.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 85.7%
  • 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/qwen3.6-27b",
    "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/qwen3.6-27b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.