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Qwen25 VL 72b

Added May 10, 2025Open weights

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Use in the app

Qwen25 VL 72b model with 32k context window

  • 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
32K
Max output
—

Side by side

Qwen25 VL 72b compared

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

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Qwen25 VL 72bQwenThis modelDeepSeek V4 ProDeepSeekRemove
ProviderQwenDeepSeek
Discovery tierOpen weightsOpen weights
AddedMay 10, 2025Apr 24, 2026
Limits
Context window32K1.05MBest
Max output—384K
Average output—416 tokens
Pricing, USD per 1M tokens
Input$0.70Best$0.70
Output$0.70Best$1.40
Cache read—$0.07
Performance
Throughput—56.2 tok/s
Time to first token—2.6 s
Request success (24h)93.6%99.8%Best
Benchmark indexes
Intelligence Index—36.0
Coding Index—68.8
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Structured outputNoYes
Reasoning effort—none, high, max

Public evaluations

Benchmarks

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

  • Qwen25 VL 72b
  • DeepSeek V4 Pro

Intelligence Index

  • Qwen25 VL 72b—
  • DeepSeek V4 Pro36.0
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 68.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 56.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 25.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 1,256 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 1,449 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 11.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 80.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 41.0%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 92.8%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 14.1%

  • SciCode

    Scientific computing in Python

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 51.0%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen25 VL 72b: —
    • DeepSeek V4 Pro: 80.3%

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
0.7%
Request success (24h)
93.6%

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.

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": "qwen/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="qwen/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.