Qwen · Open weights
Qwen3.8 27B TEE
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 468 models
Side by side
Qwen3.8 27B TEE compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Qwen3.8 27B TEEQwenThis model | Qwen 3.8 27B KiwenQwenRemove | |
|---|---|---|
| Provider | Qwen | Qwen |
| Discovery tier | Open weights | Open weights |
| Added | Aug 24, 2026 | Oct 5, 2026 |
| Limits | ||
| Context window | 262.14K | 262.14K |
| Max output | 262.14KBest | 32.77K |
| Average output | 301 tokens | 222 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.40 | $0.10Best |
| Output | $3.00 | $0.60Best |
| Cache read | $0.15 | $0.05Best |
| Performance | ||
| Request success (24h) | 100.0%Best | 94.7% |
| Benchmark indexes | ||
| Intelligence Index | 20.2 | — |
| Coding Index | 44.6 | — |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | No |
| Video input | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Qwen3.8 27B TEE
- Qwen 3.8 27B Kiwen
Intelligence Index
- Qwen3.8 27B TEE20.2
- Qwen 3.8 27B Kiwen—
Qwen3.8 27B TEE: Better than 50% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Qwen3.8 27B TEE: 44.6
- Qwen 3.8 27B Kiwen: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Qwen3.8 27B TEE: 12.4%
- Qwen 3.8 27B Kiwen: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Qwen3.8 27B TEE: 1.5%
- Qwen 3.8 27B Kiwen: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Qwen3.8 27B TEE: 898 Elo
- Qwen 3.8 27B Kiwen: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Qwen3.8 27B TEE: 1,074 Elo
- Qwen 3.8 27B Kiwen: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Qwen3.8 27B TEE: 5.2%
- Qwen 3.8 27B Kiwen: —
AA-LCRv1.1
Reasoning across long documents
- Qwen3.8 27B TEE: 69.3%
- Qwen 3.8 27B Kiwen: —
MLCR-AA
Long-context reasoning over medical records
- Qwen3.8 27B TEE: 10.0%
- Qwen 3.8 27B Kiwen: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Qwen3.8 27B TEE: 12.1%
- Qwen 3.8 27B Kiwen: —
GPQA Diamond
Graduate-level science questions
- Qwen3.8 27B TEE: 81.8%
- Qwen 3.8 27B Kiwen: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Qwen3.8 27B TEE: 0.0%
- Qwen 3.8 27B Kiwen: —
SciCode
Scientific computing in Python
- Qwen3.8 27B TEE: 36.2%
- Qwen 3.8 27B Kiwen: —
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Qwen3.8 27B TEE: 69.3%
- Qwen 3.8 27B Kiwen: —
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.
- Prompt cache hits
- 34.1%
- About $0.31 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.
- 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.
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
Related models
Other Qwen models, starting with the same family.