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Qwen · Open weights

Qwen3.5 397B A17B TEE

Added Feb 28, 2026Open weights

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Use in the app

Qwen 3.5's open-source 397B MoE model (17B active params) with hybrid linear attention. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.55
Output
$3.50

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K

Side by side

Qwen3.5 397B A17B 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 397B A17B TEEQwenThis modelMiniMax M2.1MiniMaxRemove
ProviderQwenMiniMax
Discovery tierOpen weightsOpen weights
AddedFeb 28, 2026Jan 20, 2025
Limits
Context window262.14KBest200K
Max output65.54K131.07KBest
Average output—359 tokens
Pricing, USD per 1M tokens
Input$0.55$0.30Best
Output$3.50$1.20Best
Cache read—$0.03
Benchmark indexes
Intelligence Index21.4Best20.9
Math Index—82.7
Capabilities
ReasoningNoYes
ToolsNoYes
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3.5 397B A17B TEE
  • MiniMax M2.1

Intelligence Index

  • Qwen3.5 397B A17B TEE21.4
  • MiniMax M2.120.9

Qwen3.5 397B A17B TEE: Better than 51% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen3.5 397B A17B TEE: —
    • MiniMax M2.1: 82.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.5 397B A17B TEE: 64.3%
    • MiniMax M2.1: 67.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.5 397B A17B TEE: 19.8%
    • MiniMax M2.1: 23.2%Top

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.5 397B A17B TEE: 86.1%Top
    • MiniMax M2.1: 83.0%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3.5 397B A17B TEE: —
    • MiniMax M2.1: 87.5%

  • IFBench

    Following precise output instructions

    • Qwen3.5 397B A17B TEE: 51.6%
    • MiniMax M2.1: 69.9%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3.5 397B A17B TEE: —
    • MiniMax M2.1: 81.0%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3.5 397B A17B TEE: —
    • MiniMax M2.1: 82.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.5 397B A17B TEE: 35.6%Top
    • MiniMax M2.1: 28.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.5 397B A17B TEE: 83.9%
    • MiniMax M2.1: 85.4%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.5 397B A17B TEE: 64.3%
    • MiniMax M2.1: 67.7%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/qwen3.5-397b-a17b",
    "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/qwen3.5-397b-a17b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.