qwen3.6-35b-a3b: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": [
{
"role": "user",
"content": [
{
"type": "input_image",
"detail": "auto",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
},
{
"text": "What animal is this, and what is it doing?",
"type": "input_text"
}
]
}
],
"model": "qwen3.6-35b-a3b"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"input": [
{
"role": "user",
"content": [
{
"type": "input_image",
"detail": "auto",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
},
{
"text": "What animal is this, and what is it doing?",
"type": "input_text"
}
]
}
],
"model": "qwen3.6-35b-a3b"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: [
{
role: 'user',
content: [
{
type: 'input_image',
detail: 'auto',
image_url: 'https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg'
},
{text: 'What animal is this, and what is it doing?', type: 'input_text'}
]
}
],
model: 'qwen3.6-35b-a3b'
})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_image\",\n \"detail\": \"auto\",\n \"image_url\": \"https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg\"\n },\n {\n \"text\": \"What animal is this, and what is it doing?\",\n \"type\": \"input_text\"\n }\n ]\n }\n ],\n \"model\": \"qwen3.6-35b-a3b\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_image\",\n \"detail\": \"auto\",\n \"image_url\": \"https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg\"\n },\n {\n \"text\": \"What animal is this, and what is it doing?\",\n \"type\": \"input_text\"\n }\n ]\n }\n ],\n \"model\": \"qwen3.6-35b-a3b\"\n}"
response = http.request(request)
puts response.read_body{}{}By model
qwen3.6-35b-a3b
Model details for qwen3.6-35b-a3b. Fast open-weight multimodal generation across text, images, and video.
POST
/
responses
qwen3.6-35b-a3b: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"input": [
{
"role": "user",
"content": [
{
"type": "input_image",
"detail": "auto",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
},
{
"text": "What animal is this, and what is it doing?",
"type": "input_text"
}
]
}
],
"model": "qwen3.6-35b-a3b"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"input": [
{
"role": "user",
"content": [
{
"type": "input_image",
"detail": "auto",
"image_url": "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
},
{
"text": "What animal is this, and what is it doing?",
"type": "input_text"
}
]
}
],
"model": "qwen3.6-35b-a3b"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: [
{
role: 'user',
content: [
{
type: 'input_image',
detail: 'auto',
image_url: 'https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg'
},
{text: 'What animal is this, and what is it doing?', type: 'input_text'}
]
}
],
model: 'qwen3.6-35b-a3b'
})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_image\",\n \"detail\": \"auto\",\n \"image_url\": \"https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg\"\n },\n {\n \"text\": \"What animal is this, and what is it doing?\",\n \"type\": \"input_text\"\n }\n ]\n }\n ],\n \"model\": \"qwen3.6-35b-a3b\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_image\",\n \"detail\": \"auto\",\n \"image_url\": \"https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg\"\n },\n {\n \"text\": \"What animal is this, and what is it doing?\",\n \"type\": \"input_text\"\n }\n ]\n }\n ],\n \"model\": \"qwen3.6-35b-a3b\"\n}"
response = http.request(request)
puts response.read_body{}{}Alibaba’s Qwen3.6-35B-A3B is a fast, open-weight multimodal model for applications that need to understand text, images, and video, served on ZeroGPU as an FP8 build. It suits visual question answering, document and screenshot analysis, video understanding, content extraction, agent workflows, and tool calling, and it supports structured output. Its Mixture-of-Experts architecture keeps inference efficient while it works through large amounts of multimodal content. When the input is images or video rather than text alone, this is the model.References: Model docs • Terms • Privacy
Authorizations
Headers
Optional project identifier. Scopes the request to a specific project when provided.
Body
application/json
Model identifier (fixed for this playground). Use request examples to change use cases.
Allowed value:
"qwen3.6-35b-a3b"Example:
"qwen3.6-35b-a3b"
Multi-line text or document content to send to the model.
Required string length:
1 - 131072Maximum number of tokens to generate in the response.
Required range:
x >= 1Example:
800
Response
Success
The response is of type object.

