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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 modelNvidia Nemotron 3 Ultra 550BnvidiaRemove
ProviderQwennvidia
Discovery tierOpen weightsOpen weights
AddedMar 13, 2026Jun 4, 2026
Limits
Context window262.14K1MBest
Max output65.54K65.54K
Average output1.4K tokens56 tokens
Pricing, USD per 1M tokens
Input$0.30Best$0.50
Output$2.40Best$2.50
Performance
Throughput—111.9 tok/s
Time to first token—1.5 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index22.922.9
Coding Index—49.3
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Video inputYesNo
Parallel toolsNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3.5 27B TEE
  • Nvidia Nemotron 3 Ultra 550B

Intelligence Index

  • Qwen3.5 27B TEE22.9
  • Nvidia Nemotron 3 Ultra 550B22.9

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: —
    • Nvidia Nemotron 3 Ultra 550B: 49.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 3.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 876 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 1,013 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 5.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.5 27B TEE: 77.7%
    • Nvidia Nemotron 3 Ultra 550B: 79.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 5.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.5 27B TEE: 23.9%
    • Nvidia Nemotron 3 Ultra 550B: 28.4%Top

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.5 27B TEE: 85.8%
    • Nvidia Nemotron 3 Ultra 550B: 86.7%Top

  • IFBench

    Following precise output instructions

    • Qwen3.5 27B TEE: 75.6%
    • Nvidia Nemotron 3 Ultra 550B: 81.4%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 0.5%

  • SciCode

    Scientific computing in Python

    • Qwen3.5 27B TEE: —
    • Nvidia Nemotron 3 Ultra 550B: 40.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.5 27B TEE: 32.6%
    • Nvidia Nemotron 3 Ultra 550B: 36.4%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.5 27B TEE: 93.9%Top
    • Nvidia Nemotron 3 Ultra 550B: 83.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.5 27B TEE: 77.7%
    • Nvidia Nemotron 3 Ultra 550B: 79.3%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.