Qwen Image 3.0

Qwen Image 3.0 is Alibaba's next-generation open-weights AI image generation and editing model built on a Diffusion Transformer (DiT) architecture. Supporting both text-to-image (T2I) and image-to-image instruction editing (I2I), it excels at rich content rendering, hyper-realistic detail, and deep contextual understanding. It achieves industry-leading text-in-image typography, rendering clear small text down to 10px across 12 native languages and 20+ fonts for complex multi-column layouts, UI designs, and graphic documents. With open weights available for self-hosting, Qwen Image 3.0 provides highly cost-effective, high-precision visual generation for localized advertising, product UI prototyping, e-commerce graphics, and creative publishing.

Qwen Image 3.0

Base URL

https://api.icreat.ai

Authentication

All API requests must be authenticated with an API Key. You can obtain an API Key from the console.

export ICREAT_API_KEY="your-api-key-here"

HTTP Request Headers

import os

API_KEY = os.environ.get("ICREAT_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer " + API_KEY,
}

Protect your API Key

Never expose your API Key in client-side code or public repositories. Use environment variables or a backend proxy.

Code Examples

Image and video generation uses a two-step async flow: submit a task to get task_id, then poll via query task result; the response includes status and result ([] while processing; on SUCCEEDED, result holds resources and costUSD is present). The examples below use the same task_id across both steps.

1. Submit Task

Send a generation request to the submit endpoint.

POST/v1/task/submit/aliyun/qwen-image-3-0

curl --fail-with-body --connect-timeout 10 --max-time 60 \
  -X POST https://api.icreat.ai/v1/task/submit/aliyun/qwen-image-3-0 \
  -H "Authorization: Bearer ${ICREAT_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
  "input": {
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "image": "https://example.com/example.png"
          },
          {
            "text": "Replace the main character clothing with a cyberpunk-style glowing jacket, and adjust the background to a rainy night neon street district"
          }
        ]
      }
    ]
  },
  "parameters": {
    "watermark": true,
    "size": "1024*1024"
  }
}'

2. Query Task Result (Poll)

Use the task_id from submit to poll progress (repeat until terminal). The response includes status and result: result is [] while processing; on SUCCEEDED, result holds resources and costUSD is included; FAILED means the task failed.

POST/v1/task/result

curl --fail-with-body --connect-timeout 10 --max-time 60 \
  -X POST "https://api.icreat.ai/v1/task/result" \
  -H "Authorization: Bearer ${ICREAT_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"task_id": "task-xxx"}'

Input Schema

Submit Task — Input

The following parameters are accepted in the submit request body.

Total: 2 Required: 1 Optional: 1

inputobjectrequired

Generation input payload.

parametersobject

Generation parameters.

Query Task Result — Input

Total: 1 Required: 1 Optional: 0

task_idstringrequired

The task ID returned from the submit endpoint.

Output Schema

Submit Task — Output

Total: 1

task_idstring

Async task identifier.

Query Task Result — Output

Total: variable

statusstring

Current task status. result is usually [] until success; on SUCCEEDED, result holds resources and costUSD is present.

SUBMITTEDSUCCEEDEDFAILED

resultarray[object]

Generated resources. Empty array while processing or on failure; array of objects on success.

costUSDnumber

Task cost in USD. Present only when status is SUCCEEDED.

LLM Prompt

The Markdown below is an LLM-friendly prompt you can paste into AI assistants (e.g. Cursor, ChatGPT) to help them understand this model's API, call flow, and key parameters. Use Copy for AI or copy from the code block below.

# aliyun/qwen-image-3-0

> Qwen Image 3.0 is Alibaba's next-generation open-weights AI image generation and editing model built on a Diffusion Transformer (DiT) architecture.

## Overview

Use the iCreat two-step async task API: submit a generation request, then poll the query task result endpoint; on success read resources from `result` (includes `costUSD`).

## API Info

- **Base URL**：`https://api.icreat.ai`
- **Submit endpoint (POST)**：`/v1/task/submit/aliyun/qwen-image-3-0`
- **Query result endpoint (POST)**：`/v1/task/result`
- **Model ID**：`aliyun/qwen-image-3-0`
- **Auth**：`Authorization: Bearer ${ICREAT_API_KEY}`

## Call Flow

1. **Submit**: POST submit path with body per Input Notes; response `{ "task_id": "..." }`
2. **Query result**: POST `/v1/task/result` with `{ "task_id": "..." }`; response includes `status` and `result` (`[]` while processing); on `SUCCEEDED`, `result` holds resources and `costUSD` is present; read `url` or `download_url` when `type` is `Image`

### Input Notes

- Top-level body: `input` (object) + `parameters` (object) as siblings
- `input.messages` (required)
- `input.role` (required): Message role.
- `input.content` (required)
- `input.text` (optional): Text prompt or editing instruction.
- `input.image` (optional): Reference image URL for image-to-image or instruction editing.
- `parameters.size` (required): Output image size.
- `parameters.watermark` (optional): Whether to add a watermark to the output image.

### Output Notes

- Poll: read `status`; `result` is `[]` while processing
- Success: `result` is `[{ "type": "Image", "url": "...", "download_url": "..." }]` plus `costUSD`

## Notes

- Use the same `task_id` across both steps; `result` is `[]` while processing — keep polling
- `FAILED` is terminal — check request parameters or reference media
- `input` and `parameters` are sibling top-level fields; do not nest them