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Qwen3 VL 235B A22B Instruct Original

Added Sep 25, 2025

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Use in the app

Routed across multiple providers, which may include Alibaba, a Chinese entity; provider-specific privacy and logging guarantees may vary. Qwen3 Vision‑Language model (235B MoE, ≈22B active) tuned for instruction following and grounded visual QA. Excels at image understanding, dense OCR, charts and diagrams, and multi‑image context. Use this variant when you want concise, direct answers grounded in the visuals.

  • Vision
Input
Text, Images
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$0.30
Output
$1.90

Reference prices as published by the model provider. API example

Specifications

Context window
—
Max output
32.8K
Average output (7 days)
288 tokens
Longer than 29% of 468 models

Side by side

Qwen3 VL 235B A22B Instruct Original compared

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

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Qwen3 VL 235B A22B Instruct OriginalQwenThis modelDeepSeek Chat 0324DeepSeekRemove
ProviderQwenDeepSeek
Discovery tierBalancedOpen weights
AddedSep 25, 2025Jan 1, 2024
Limits
Context window—128K
Max output32.77KBest8.19K
Average output288 tokens280 tokens
Pricing, USD per 1M tokens
Input$0.30$0.20Best
Output$1.90$0.77Best
Cache read—$0.14
Performance
Throughput—25.4 tok/s
Time to first token—2.9 s
Request success (24h)100.0%Best99.9%
Benchmark indexes
Intelligence Index9.9Best9.7
Coding Index—21.2
Math Index70.7Best41.0
Capabilities
ToolsNoYes
VisionYesNo
Structured outputNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3 VL 235B A22B Instruct Original
  • DeepSeek Chat 0324

Intelligence Index

  • Qwen3 VL 235B A22B Instruct Original9.9
  • DeepSeek Chat 03249.7

Qwen3 VL 235B A22B Instruct Original: Better than 20% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 21.2

  • Math Index

    Composite of the math evaluations

    • Qwen3 VL 235B A22B Instruct Original: 70.7Top
    • DeepSeek Chat 0324: 41.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 0.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 68 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 69 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 1.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 VL 235B A22B Instruct Original: 32.7%
    • DeepSeek Chat 0324: 40.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 VL 235B A22B Instruct Original: 6.6%Top
    • DeepSeek Chat 0324: 4.7%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 VL 235B A22B Instruct Original: 71.2%Top
    • DeepSeek Chat 0324: 65.5%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 VL 235B A22B Instruct Original: 82.3%Top
    • DeepSeek Chat 0324: 81.9%

  • IFBench

    Following precise output instructions

    • Qwen3 VL 235B A22B Instruct Original: 42.7%Top
    • DeepSeek Chat 0324: 41.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 0.0%

  • SciCode

    Scientific computing in Python

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 39.0%

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 VL 235B A22B Instruct Original: 59.4%Top
    • DeepSeek Chat 0324: 40.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 VL 235B A22B Instruct Original: 70.7%Top
    • DeepSeek Chat 0324: 41.0%

  • AIME

    Competition mathematics

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 52.0%

  • MATH-500

    Competition math problems

    • Qwen3 VL 235B A22B Instruct Original: —
    • DeepSeek Chat 0324: 94.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 VL 235B A22B Instruct Original: 6.8%
    • DeepSeek Chat 0324: 15.2%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 VL 235B A22B Instruct Original: 35.1%
    • DeepSeek Chat 0324: 47.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 VL 235B A22B Instruct Original: 32.7%
    • DeepSeek Chat 0324: 40.7%Top

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.

Request success (24h)
100.0%
Reliable
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": "qwen3-vl-235b-a22b-instruct-original",
    "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="qwen3-vl-235b-a22b-instruct-original",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.