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Qwen · Open weights

Qwen3.5 27B TEE

Added Mar 13, 2026Open weights

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Use in the app

Qwen3.5 27B is a native vision-language dense model optimized for fast responses while balancing quality and inference speed. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

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

Pricing

USD per 1M tokens
Input
$0.30
Output
$2.40

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
1.4K tokens
Longer than 82% of 468 models

Side by side

Qwen3.5 27B TEE compared

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

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Qwen3.5 27B TEEQwenThis modelGPT 5OpenAIRemove
ProviderQwenOpenAI
Discovery tierOpen weightsFrontier
AddedMar 13, 2026Aug 7, 2025
Limits
Context window262.14K400KBest
Max output65.54K128KBest
Average output1.4K tokens411 tokens
Pricing, USD per 1M tokens
Input$0.30Best$1.25
Output$2.40Best$10.00
Cache read—$0.13
Benchmark indexes
Intelligence Index22.923.0Best
Coding Index—37.8
Math Index—94.3
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
PDF inputNoYes
Video inputYesNo
Reasoning effort—minimal, low, medium, high

Public evaluations

Benchmarks

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

  • Qwen3.5 27B TEE
  • GPT 5

Intelligence Index

  • Qwen3.5 27B TEE22.9
  • GPT 523.0

Qwen3.5 27B TEE: Better than 56% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3.5 27B TEE: —
    • GPT 5: 37.8

  • Math Index

    Composite of the math evaluations

    • Qwen3.5 27B TEE: —
    • GPT 5: 94.3

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3.5 27B TEE: —
    • GPT 5: 918 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.5 27B TEE: 77.7%
    • GPT 5: 78.2%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.5 27B TEE: 23.9%
    • GPT 5: 28.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.5 27B TEE: 85.8%Top
    • GPT 5: 85.4%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3.5 27B TEE: —
    • GPT 5: 87.1%

  • IFBench

    Following precise output instructions

    • Qwen3.5 27B TEE: 75.6%Top
    • GPT 5: 73.1%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3.5 27B TEE: —
    • GPT 5: 84.6%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3.5 27B TEE: —
    • GPT 5: 94.3%

  • AIME

    Competition mathematics

    • Qwen3.5 27B TEE: —
    • GPT 5: 95.7%

  • MATH-500

    Competition math problems

    • Qwen3.5 27B TEE: —
    • GPT 5: 99.4%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.5 27B TEE: 32.6%Top
    • GPT 5: 32.6%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.5 27B TEE: 93.9%Top
    • GPT 5: 84.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.5 27B TEE: 77.7%
    • GPT 5: 78.2%Top

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": "TEE/qwen3.5-27b",
    "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/qwen3.5-27b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.