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Qwen · Open weights

Qwen3.6 27B TEE

Added Jun 4, 2026Open weights

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Use in the app

Qwen3.6 27B is a dense language model from Alibaba's Qwen team with text and image input, configurable thinking/reasoning behavior, and a native 262K context window. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

  • Reasoning
  • Vision
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.32
Output
$2.70

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
245 tokens
Longer than 26% of 468 models

Side by side

Qwen3.6 27B TEE compared

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

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Qwen3.6 27B TEEQwenThis modelQwen 3.8 27B KimikoQwenRemove
ProviderQwenQwen
Discovery tierOpen weightsOpen weights
AddedJun 4, 2026Oct 5, 2026
Limits
Context window262.14K262.14K
Max output65.54KBest32.77K
Average output245 tokens210 tokens
Pricing, USD per 1M tokens
Input$0.32$0.10Best
Output$2.70$0.60Best
Cache read—$0.05
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index19.8—
Coding Index46.6—
Capabilities
ReasoningYesYes
ToolsNoYes
VisionYesYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3.6 27B TEE
  • Qwen 3.8 27B Kimiko

Intelligence Index

  • Qwen3.6 27B TEE19.8
  • Qwen 3.8 27B Kimiko—

Qwen3.6 27B TEE: Better than 49% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3.6 27B TEE: 46.6
    • Qwen 3.8 27B Kimiko: —

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3.6 27B TEE: 961 Elo
    • Qwen 3.8 27B Kimiko: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.6 27B TEE: 66.7%
    • Qwen 3.8 27B Kimiko: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.6 27B TEE: 15.1%
    • Qwen 3.8 27B Kimiko: —

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.6 27B TEE: 82.9%
    • Qwen 3.8 27B Kimiko: —

  • IFBench

    Following precise output instructions

    • Qwen3.6 27B TEE: 45.7%
    • Qwen 3.8 27B Kimiko: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.6 27B TEE: 21.2%
    • Qwen 3.8 27B Kimiko: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.6 27B TEE: 93.6%
    • Qwen 3.8 27B Kimiko: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.6 27B TEE: 66.7%
    • Qwen 3.8 27B Kimiko: —

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 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": "TEE/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="TEE/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.