Introducing Wan 3.0 on DeepInfra

Wan3.0 is a video generation model from Alibaba’s Wan team that combines text-to-video, image-to-video, reference-based generation, video editing, and video extension in a single model. The media you send with a request selects the workflow, so one model handles every input type.

A request can combine a text prompt with up to 20 reference assets, including images, video clips, and audio. It can also include a document or a public web page. Wan3.0 turns that input into up to 30 seconds of 1080P video with synchronized audio.

Artificial Analysis ranks Wan3.0 first on its text-to-video leaderboard with audio, at 1,173 Elo. Wan3.0 is available on DeepInfra at $0.20 per second of generated video.

What Makes This Model Different

The most distinctive design decision in Wan3.0 is its unified model. Earlier releases split these tasks across separate models, such as the Wan2.7 reference-to-video model and the Wan2.6 text-to-video model. Wan3.0 handles all of them behind one model identifier, so you manage a single endpoint as your use case changes.

Wan3.0 also doubles the maximum clip length. Alibaba lists Wan2.7 at 2 to 15 seconds per generation, while Wan3.0 reaches 30 seconds in one pass. Both earlier generations remain available on DeepInfra, including Wan2.7 reference-to-video and Wan2.6 text-to-video, each at $0.10 per second.

The supported task types are:

The reference system accepts up to 10 reference images, 5 video clips, and 5 audio clips in one request. A request can also include one document, such as a PDF, DOCX, PPTX, or Markdown file, or one public web page. The model parses it and turns it into video.

You reference assets directly in the prompt with positional names such as Image 1 or Video 2. The numbers follow the order of the media array, and images and videos are counted separately.

Audio is on by default. The model generates dialogue, background music, and sound effects alongside the video. Set audio to false if you handle sound separately.

Output tops out at 1080P and 30 fps, with 480P and 720P tiers available. Duration can be set from 2 to 30 seconds, or set to -1 to let the model choose a length from the prompt and media.

The prompt_extend parameter defaults to true and rewrites your prompt before generation. Sparse prompts benefit most, so leave it on unless you need exact control over the wording.

Getting Started on DeepInfra

Wan3.0-Video is available on DeepInfra under the model identifier Wan-AI/Wan3.0-Video. Pricing is $0.20 per second of generated video, so a 10-second clip costs $2.00 and a full 30-second generation costs $6.00.

Billing is usage-based through a REST API that fits existing tooling. DeepInfra runs a zero-retention policy for inputs, outputs, and user data, and the platform is SOC 2 and ISO 27001 certified.

Quickstart

Create a key in the DeepInfra dashboard, then submit a text-to-video request. The response includes a video_url that points to the finished MP4.

curl -X POST "https://api.deepinfra.com/v1/inference/Wan-AI/Wan3.0-Video" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -d '{
    "prompt": "A red fox running through a snowy pine forest at dusk, cinematic lighting",
    "resolution": "1080P",
    "ratio": "16:9",
    "duration": 5,
    "audio": true,
    "prompt_extend": true
  }'
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import os
import requests
 
response = requests.post(
    "https://api.deepinfra.com/v1/inference/Wan-AI/Wan3.0-Video",
    headers={
        "Authorization": f"Bearer {os.environ['DEEPINFRA_TOKEN']}",
        "Content-Type": "application/json",
    },
    json={
        "prompt": "A red fox running through a snowy pine forest at dusk, cinematic lighting",
        "resolution": "1080P",
        "ratio": "16:9",
        "duration": 5,
        "audio": True,
        "prompt_extend": True,
    },
)
 
print(response.json()["video_url"])
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Key parameters

• duration accepts integers from 2 to 30, or -1 to let the model pick a length from your prompt and media.
• ratio defaults to adaptive, which derives the aspect ratio from your input media. This helps most in image-to-video workflows.
• audio defaults to true. Pass false for a silent output.
• prompt_extend defaults to true and rewrites the prompt to improve output quality.
• watermark defaults to false. Set it to true to add an AI-generated watermark.
• seed accepts integers from 0 to 2,147,483,647 for reproducible outputs.
• Reference media types cannot be combined with first_frame or last_frame in the same request.

The model page also lets you run a first generation in the browser and inspect the full parameter reference.

Conclusion

Wan3.0 removes the need to stitch together separate models for each video task. One endpoint accepts text, images, video clips, audio, documents, and web pages, and it generates synchronized audio in the same pass.

This range suits workflows that were hard to automate before. Examples include product demo videos built from a URL, story-driven clips built from a PDF script, and short-form content with consistent characters.

Start with a short 480P or 720P clip to check motion and composition, then render the final version at 1080P.