qwen3-vl-32b-thinking
Model Overview
The most advanced vision-language model in the Qwen series as of October 2025 — a thinking-capable version of the model. Designed for complex visual-textual reasoning and extended chains of thought.
How to Make a Call
API Schema
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseControls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.
Whether to enable parallel function calling during tool use.
What sampling temperature to use. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
An object specifying the format that the model must output.
A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition.
async function main() {
const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
method: 'POST',
headers: {
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'alibaba/qwen3-vl-32b-thinking',
messages:[
{
role:'user',
content: 'Hello'
}
],
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();{
"id": "chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl",
"object": "chat.completion",
"created": 1762343744,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! I'm just a program, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?",
"refusal": null,
"annotations": null,
"audio": null,
"tool_calls": null
},
"finish_reason": "stop",
"logprobs": null
}
],
"model": "alibaba/qwen3-vl-32b-thinking",
"usage": {
"prompt_tokens": 137,
"completion_tokens": 914,
"total_tokens": 1051,
"completion_tokens_details": null,
"prompt_tokens_details": null
}
}Code Example
import requests
import json # for getting a structured output with indentation
response = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"alibaba/qwen3-vl-32b-thinking",
"messages":[
{
# Insert your question for the model here:
"content":"Hi! What do you think about mankind?"
}
]
}
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))async function main() {
const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
method: 'POST',
headers: {
// insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'alibaba/qwen3-vl-32b-thinking',
messages:[
{
role:'user',
// Insert your question for the model here:
content:'Hi! What do you think about mankind?'
}
],
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();Last updated
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