Qwen: Qwen3 VL 235B A22B Instruct✓ Catalog verified
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
Overview
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.
Access: available through the official Qwen API. Context window: 262K.
Specifications
| API identifier | qwen/qwen3-vl-235b-a22b-instruct |
| Provider | Qwen |
| Model type | Multimodal LLM |
| Context window | 262K catalog |
| Input modalities | text · image |
| Output modalities | text |
| Released | Sep 23, 2025 |
| Tokenizer | Qwen3 |
| Moderated | No |
| Architecture modality | text+image->text |
API defaults
{
"top_p": 0.8,
"temperature": 0.7,
"frequency_penalty": null
}Pricing
Live pricing components for qwen/qwen3-vl-235b-a22b-instruct as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $2e-7 | $0.20 / 1M | Per input token |
| Completion tokens | $8.8e-7 | $0.88 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.28Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | ✓ Supported | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | ✓ Supported | Supports tools / function calling |
| Self-Hosting | ✓ Supported | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call Qwen: Qwen3 VL 235B A22B Instruct through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="qwen/qwen3-vl-235b-a22b-instruct",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| ByteDance Seed: Seed 1.6 | Bytedance Seed | 262K | $0.25 | View → |
| ByteDance Seed: Seed 1.6 Flash | Bytedance Seed | 262K | $0.07 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does Qwen: Qwen3 VL 235B A22B Instruct cost?
$0.20 per 1M input tokens and $0.88 per 1M output tokens on the official Qwen API.
What is the context window of Qwen: Qwen3 VL 235B A22B Instruct?
Qwen: Qwen3 VL 235B A22B Instruct supports up to 262K tokens of context.
Does Qwen: Qwen3 VL 235B A22B Instruct support tool / function calling?
Yes, Qwen: Qwen3 VL 235B A22B Instruct supports tool / function calling — you can register tools and the model will emit structured tool calls.
How do I access Qwen: Qwen3 VL 235B A22B Instruct?
Use the official Qwen API with the model id `qwen/qwen3-vl-235b-a22b-instruct`. See the Quick start section above for code examples.