Overview
The largest dense model in the Qwen3-VL series, in its non-inference version, delivers overall performance second only to Qwen3-VL-235B-Instruct. It excels in document recognition and comprehension, demonstrates strong spatial awareness and object identification capabilities, and achieves state-of-the-art performance in 2D visual detection and spatial reasoning. It is well-suited for complex perception tasks across a wide range of general-purpose scenarios.
Input
TextImageVideo
Output
Text
Features
Prefix Completion
Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docsFunction Calling
Use function calling to connect large language models with external tools and systems.View docsCache
Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docsStructured Outputs
Structured Outputs help ensure the model returns a JSON string in the expected format.View docsPricing
- Input$0.16Per 1M tokens
- Output$0.64Per 1M tokens
Rate Limits & Context
- Max Input129K
- Max Output32K
- Context131K
- TPMTokens Per Minute100K
- RPMRequests Per Minute60
API Reference
Call APICopy success!
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import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://maas.qwencloudapi.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="qwen3-vl-32b-instruct",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/ctdzex/biaozhun.jpg"
},
},
{"type": "text", "text": "Output the text in the image only."},
],
},
],
)
print(completion.choices[0].message.content)