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Qwen3 VL 235B A22B Instruct

Added Jan 1, 2024Open weights

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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/diagrams, and multi‑image context.

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

Pricing

USD per 1M tokens
Input
$0.30
Output
$1.90

Reference prices as published by the model provider. API example

Specifications

Context window
131.1K
Max output
32.8K

Side by side

Qwen3 VL 235B A22B Instruct 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 InstructQwenThis modelGLM 4.6VZ.aiRemove
ProviderQwenZ.ai
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Dec 11, 2025
Limits
Context window131.07KBest128K
Max output32.77KBest24K
Average output—139 tokens
Pricing, USD per 1M tokens
Input$0.30$0.30
Output$1.90$0.90Best
Performance
Request success (24h)100.0%Best99.7%
Benchmark indexes
Intelligence Index—8.4
Math Index—26.3
Capabilities
VisionYesYes
Video inputNoYes
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
  • GLM 4.6V

Intelligence Index

  • Qwen3 VL 235B A22B Instruct—
  • GLM 4.6V8.4
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 26.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 17.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 3.7%

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 56.6%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 75.2%

  • IFBench

    Following precise output instructions

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 27.9%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 41.1%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 26.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 3.0%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 30.7%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3 VL 235B A22B Instruct: —
    • GLM 4.6V: 17.0%

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
0.0%
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.

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": "qwen/qwen3-vl-235b-a22b-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/qwen3-vl-235b-a22b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.