Wan 3.0 Prime Text-to-Video
Wan 3.0 Prime Text-to-Video is Alibaba's high-speed AI text-to-video model under the Tongyi Wanxiang family. Combining the Prime architecture's rapid inference with the core Wan 3.0 multimodal foundation, it deeply parses complex text prompts to directly render up to 30-second 1080P HD videos with significantly reduced generation latency. Featuring native audio-visual synchronization (ambient audio, sound effects, and multilingual lip-sync), it delivers exceptional physical motion simulation, seamless temporal coherence, and precise camera control—providing rapid turnarounds and high-quality visual output for commercial advertising, short dramas, film VFX, and high-frequency social media content creation.
Wan 3.0 Prime Text-to-Video
Wan 3.0 Prime Text-to-Video is Alibaba's high-speed AI text-to-video model under the Tongyi Wanxiang family. Combining the Prime architecture's rapid inference with the core Wan 3.0 multimodal foundation, it deeply parses complex text prompts to directly render up to 30-second 1080P HD videos with significantly reduced generation latency. Featuring native audio-visual synchronization (ambient audio, sound effects, and multilingual lip-sync), it delivers exceptional physical motion simulation, seamless temporal coherence, and precise camera control—providing rapid turnarounds and high-quality visual output for commercial advertising, short dramas, film VFX, and high-frequency social media content creation.
Base URL
https://api.icreat.aiAuthentication
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.
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.
Input Schema
Submit Task — Input
Total: 2 Required: 2 Optional: 0
Generation input payload.
Generation parameters.
Query Task Result — Input
Total: 1 Required: 1 Optional: 0
The task ID returned from the submit endpoint.
Output Schema
Submit Task — Output
Total: 1
Async task identifier.
Query Task Result — Output
Total: variable
Current task status. result is usually [] until success; on SUCCEEDED, result holds resources and costUSD is present.
Generated resources. Empty array while processing or on failure; array of objects on success.
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/wan3-0-prime/text-to-video
> Wan 3.0 Prime Text-to-Video is Alibaba's high-speed AI text-to-video model under the Tongyi Wanxiang family.
## 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/wan3-0-prime/text-to-video`
- **Query result endpoint (POST)**：`/v1/task/result`
- **Model ID**：`aliyun/wan3-0-prime/text-to-video`
- **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 `Video`
### Input Notes
- Top-level body: `input` (object) + `parameters` (object) as siblings
- `input.prompt` (required): Prompt describing the video to generate.
- `input.negative_prompt` (optional): Content that should not appear in the video.
- `input.media` (optional): Optional reference media list.
- `parameters.resolution` (required): Video resolution.
- `parameters.ratio` (required): Video aspect ratio.
- `parameters.duration` (required): Video duration in seconds.
- `parameters.watermark` (optional): Whether to add a watermark.
### Output Notes
- Poll: read `status`; `result` is `[]` while processing
- Success: `result` is `[{ "type": "Video", "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