NOUMENOpen app
NOUMEN

Qwen · Open weights

Qwen2.5 VL 72B TEE

Added Jan 1, 2024Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Qwen2.5 Vision-Language 72B model with multimodal capabilities. Running inside a TEE (Trusted Execution Environment), with provider attestation support.

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

Pricing

USD per 1M tokens
Input
$0.70
Output
$0.70

Reference prices as published by the model provider. API example

Specifications

Context window
65.5K
Max output
8.2K
Average output (7 days)
84 tokens
Longer than 8% of 468 models

Side by side

Qwen2.5 VL 72B TEE compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Qwen2.5 VL 72B TEEQwenThis modelGLM 5.1 TEEZ.aiRemove
ProviderQwenZ.ai
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 20, 2026
Limits
Context window65.54K202.75KBest
Max output8.19K65.54KBest
Average output84 tokens675 tokens
Pricing, USD per 1M tokens
Input$0.70$0.70
Output$0.70Best$2.50
Cache read—$0.18
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—26.1
Coding Index—55.8
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Qwen2.5 VL 72B TEE
  • GLM 5.1 TEE

Intelligence Index

  • Qwen2.5 VL 72B TEE—
  • GLM 5.1 TEE26.1
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 55.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 20.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 3.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 964 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 1,117 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 8.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 73.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 30.1%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 86.8%

  • IFBench

    Following precise output instructions

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 76.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 2.0%

  • SciCode

    Scientific computing in Python

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 44.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 43.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 97.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen2.5 VL 72B TEE: —
    • GLM 5.1 TEE: 73.7%

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/qwen2.5-vl-72b-instruct",
    "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/qwen2.5-vl-72b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.