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Qwen · Open weights

Qwen3.8 27B TEE

Added Aug 24, 2026Open weights

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Qwen3.8 27B is an open-weight dense vision-language model from Alibaba for reasoning, coding, professional workflows, multimodal interaction, tool use, and structured output. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

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

Pricing

USD per 1M tokens
Input
$0.40
Output
$3.00
Cache read
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
262.1K
Average output (7 days)
301 tokens
Longer than 32% of 465 models

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TryOpenAI o3Ling 3.0 FlashMiniMax M2.1All 3

Public evaluations

Benchmarks

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

Intelligence Index

20.2

Better than 50% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    44.6

    Composite of the coding evaluations

    Better than 38% of 168 scored models

Agentic work

  • AutomationBench-AA

    12.4%

    Workflow automation, penalized for guardrail violations

    Better than 32% of 173 scored models

  • AutomationBench-AA tasks completed

    1.5%

    Workflows finished end to end without a guardrail violation

    Better than 33% of 173 scored models

  • AA-Briefcasev1.1

    898 Elo

    Agentic knowledge work, rated head to head

    Better than 40% of 166 scored models

  • GDPval-AAv2.1

    1,074 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 44% of 187 scored models

Document reasoning

  • GDP.pdf

    5.2%

    Professional reasoning over PDF documents, all-pass rate

    Better than 22% of 171 scored models

  • AA-LCRv1.1

    69.3%

    Reasoning across long documents

    Better than 48% of 309 scored models

  • MLCR-AA

    10.0%

    Long-context reasoning over medical records

    Better than 33% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    12.1%

    Expert-level questions across academic fields

    Better than 38% of 323 scored models

  • GPQA Diamond

    81.8%

    Graduate-level science questions

    Better than 50% of 291 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    36.2%

    Scientific computing in Python

    Better than 13% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    69.3%

    Long-context reasoning, earlier grading

    Better than 48% of 309 scored models

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.

Throughput
32.8 tok/s
Faster than 22% of 250 models
Time to first token
11.2 s
Quicker than 8% of 250 models
Prompt cache hits
33.1%
About $0.32 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

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/qwen3.8-27b",
    "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/qwen3.8-27b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.