Qwen: Qwen3 VL 32B Instruct

qwen/qwen3-vl-32b-instruct

Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...

Context

131K

Input MA / 1M

0.0624

Output MA / 1M

0.2496

Billing follows the provider's own reported cost rather than the catalogue price — cached input is cheaper, and reasoning tokens bill as output. Your usage page is the record of what you actually paid.

Capabilities

ChatVisionToolsStructured

Try it

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Call it

Every model goes through the same endpoint — just use this page's ID as model.

curl https://macdecloud.com/v1/chat/completions \
  -H "Authorization: Bearer dcld-sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen/qwen3-vl-32b-instruct","messages":[{"role":"user","content":"Hello"}]}'

Streaming

Add "stream": true for token-by-token output over standard SSE.

curl -N https://macdecloud.com/v1/chat/completions \
  -H "Authorization: Bearer dcld-sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen/qwen3-vl-32b-instruct","stream":true,"messages":[{"role":"user","content":"hi"}]}'

Function calling

This model supports tools. Replies may carry tool_calls; run them and append the results as role: "tool" messages, then call again.

{
  "model": "qwen/qwen3-vl-32b-instruct",
  "messages": [{ "role": "user", "content": "What's the weather in Paris?" }],
  "tools": [{
    "type": "function",
    "function": {
      "name": "get_weather",
      "parameters": {
        "type": "object",
        "properties": { "city": { "type": "string" } },
        "required": ["city"]
      }
    }
  }]
}

Structured output

Supports response_format, so the model returns JSON matching a schema you give it instead of prose you have to parse.

{
  "model": "qwen/qwen3-vl-32b-instruct",
  "messages": [{ "role": "user", "content": "..." }],
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "result",
      "schema": {
        "type": "object",
        "properties": { "answer": { "type": "string" } },
        "required": ["answer"]
      }
    }
  }
}

Image input

Make content an array mixing text and image_url parts. The image can be a public URL or a data: base64 payload.

{
  "model": "qwen/qwen3-vl-32b-instruct",
  "messages": [{
    "role": "user",
    "content": [
      { "type": "text", "text": "What is in this image?" },
      { "type": "image_url", "image_url": { "url": "https://example.com/photo.jpg" } }
    ]
  }]
}

Use it from Claude Code / Cursor / opencode →