qwen/qwen2.5-vl-72b-instructQwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
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.
One free message per account, no deposit needed. The reply is length-capped — it's a taste, not a quota.
Connect a wallet and sign in to use your free try.
Connect a walletEvery 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/qwen2.5-vl-72b-instruct","messages":[{"role":"user","content":"Hello"}]}'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/qwen2.5-vl-72b-instruct","stream":true,"messages":[{"role":"user","content":"hi"}]}'Supports response_format, so the model returns JSON matching a schema you give it instead of prose you have to parse.
{
"model": "qwen/qwen2.5-vl-72b-instruct",
"messages": [{ "role": "user", "content": "..." }],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "result",
"schema": {
"type": "object",
"properties": { "answer": { "type": "string" } },
"required": ["answer"]
}
}
}
}Make content an array mixing text and image_url parts. The image can be a public URL or a data: base64 payload.
{
"model": "qwen/qwen2.5-vl-72b-instruct",
"messages": [{
"role": "user",
"content": [
{ "type": "text", "text": "What is in this image?" },
{ "type": "image_url", "image_url": { "url": "https://example.com/photo.jpg" } }
]
}]
}