alibaba/wan-3.0-prime/text-to-video

text-to-video

Wan 3.0 Prime Text-to-Video API by Alibaba

alibaba/wan-3.0-prime/text-to-video

Text-to-video

All-in-one Wan3.0 renderer: cinematic, hyper-real video from a text prompt, up to 30s with smart-duration and adaptive aspect ratio.

Compare models

Wan 3.0 Prime Text-to-Video is developed by Alibaba. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Resolution

[ Original ] keeps the model’s native render. [ SR ] is cost-efficient upscaling. [ ESR ] is Atlas’s enhanced pass — more fine detail and steadier motion, still below the cost of rendering natively. Learn More

Duration

Aspect Ratio

Audio

Seed

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.061 per run. For $10 you can run this model approximately 163 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "alibaba/wan-3.0-prime/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the scene, subject, and action (up to 20000 characters)
    "resolution": "1080p",  # Output resolution
    "duration": 5,  # Video length in seconds (2-30)
    "ratio": "adaptive",  # The aspect ratio of the generated video. options: adaptive | 16:9 | 4:3 | 1:1 | 3:4 | 9:16
    "audio": True,  # Whether the output video includes an audio track
    "seed": 1,  # The random seed to use for the generation
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

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

Keep your API key secure

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

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "alibaba/wan-3.0-prime/text-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 7Required: 2Optional: 5

modelstringrequired

model name

Default: "alibaba/wan-3.0-prime/text-to-video"

promptstringrequired

The text prompt describing the scene, subject, and action (up to 20000 characters).

resolutionstring

Output resolution. Native tiers: 480p, 720p, 1080p. ESR tiers: 720p-esr, 1080p-esr, 1440p-esr, 4k-esr.

Default: "1080p"

1080p720p480p720p-esr1080p-esr1440p-esr4k-esr

durationnumber

Video length in seconds (2-30). Pass -1 for smart-duration (the model picks the best length).

Default: 5

-123456789101112131415161718192021222324252627282930

ratiostring

The aspect ratio of the generated video. Use 'adaptive' to let the model choose.

Default: "adaptive"

adaptive16:94:31:13:49:16

audioboolean

Whether the output video includes an audio track. Same price either way.

Default: true

seedinteger

The random seed to use for the generation. -1 means a random seed will be used.

Example Request Body

{
  "model": "alibaba/wan-3.0-prime/text-to-video",
  "prompt": "A beautiful landscape",
  "resolution": "1080p",
  "duration": 5,
  "ratio": "adaptive",
  "audio": true
}

Output Schema

The API returns a prediction response with the generated output URLs.

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

statusstring

Status of the task: created, processing, completed, or failed.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "alibaba/wan-3.0-prime/text-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the scene, subject, and action (up to 20000 characters).",
            "type": "string"
          },
          "resolution": {
            "default": "1080p",
            "description": "Output resolution. Native tiers: 480p, 720p, 1080p. ESR tiers: 720p-esr, 1080p-esr, 1440p-esr, 4k-esr.",
            "enum": [
              "1080p",
              "720p",
              "480p",
              "720p-esr",
              "1080p-esr",
              "1440p-esr",
              "4k-esr"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "duration": {
            "default": 5,
            "description": "Video length in seconds (2-30). Pass -1 for smart-duration (the model picks the best length).",
            "enum": [
              -1,
              2,
              3,
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15,
              16,
              17,
              18,
              19,
              20,
              21,
              22,
              23,
              24,
              25,
              26,
              27,
              28,
              29,
              30
            ],
            "x-enum-labels": {
              "-1": "Smart"
            },
            "type": "number",
            "x-ui-component": "select"
          },
          "ratio": {
            "default": "adaptive",
            "description": "The aspect ratio of the generated video. Use 'adaptive' to let the model choose.",
            "enum": [
              "adaptive",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "audio": {
            "default": true,
            "description": "Whether the output video includes an audio track. Same price either way.",
            "type": "boolean",
            "x-ui-component": "switch"
          },
          "seed": {
            "description": "The random seed to use for the generation. -1 means a random seed will be used.",
            "type": "integer"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "ratio",
          "audio",
          "seed"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).",
            "format": "date-time",
            "type": "string"
          },
          "id": {
            "description": "Unique identifier for the prediction, the ID of the prediction to get.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated content (empty when status is not completed).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    },
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM-Friendly Prompt Template

# alibaba/wan-3.0-prime/text-to-video

> All-in-one Wan3.0 renderer: cinematic, hyper-real video from a text prompt, up to 30s with smart-duration and adaptive aspect ratio.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `alibaba/wan-3.0-prime/text-to-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"alibaba/wan-3.0-prime/text-to-video"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the scene, subject, and action (up to 20000 characters).

- **`resolution`** (`string`, _optional_):
  Output resolution. Native tiers: 480p, 720p, 1080p. ESR tiers: 720p-esr, 1080p-esr, 1440p-esr, 4k-esr.
  - Default: `"1080p"`
  - Options: "1080p", "720p", "480p", "720p-esr", "1080p-esr", "1440p-esr", "4k-esr"

- **`duration`** (`number`, _optional_):
  Video length in seconds (2-30). Pass -1 for smart-duration (the model picks the best length).
  - Default: `5`
  - Options: -1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30

- **`ratio`** (`string`, _optional_):
  The aspect ratio of the generated video. Use 'adaptive' to let the model choose.
  - Default: `"adaptive"`
  - Options: "adaptive", "16:9", "4:3", "1:1", "3:4", "9:16"

- **`audio`** (`boolean`, _optional_):
  Whether the output video includes an audio track. Same price either way.
  - Default: `true`

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation. -1 means a random seed will be used.



**Required Parameters Example**:

```json
{
  "model": "alibaba/wan-3.0-prime/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "alibaba/wan-3.0-prime/text-to-video",
  "prompt": "",
  "resolution": "1080p",
  "duration": 5,
  "ratio": "adaptive",
  "audio": true,
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "alibaba/wan-3.0-prime/text-to-video",
  "prompt": "",
  "resolution": "1080p",
  "duration": 5,
  "ratio": "adaptive",
  "audio": true,
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/alibaba/wan-3.0-prime/text-to-video)

A damaged spacecraft descends through the atmosphere of an alien planet, surrounded by glowing clouds and fragments of burning debris. The camera follows the spacecraft from a distant perspective before rapidly moving closer as it begins to lose control. The planet's enormous surface slowly emerges beneath the clouds. The spacecraft shakes violently, sparks and smoke trailing behind it. Epic scale, dramatic lighting, realistic atmospheric scattering, cinematic camera shake, detailed spacecraft surfaces, volumetric clouds, intense motion, high-contrast lighting, subtle film grain, large-scale science-fiction blockbuster cinematography.