minimax/h3-max/text-to-video

text-to-video

MiniMax H3 Max Text-to-Video API by MiniMax

minimax/h3-max/text-to-video

Text-to-video

MiniMax H3 Max text-to-video: generate a cinematic video from a text prompt. Supports 480P、768P, 5-15s., and 16:9/9:16/1:1/adaptive aspect ratios.

Compare models

MiniMax H3 Max Text-to-Video is developed by MiniMax. 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 *

Duration *

Aspect Ratio

Prompt expansion

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.048 per run. For $10 you can run this model approximately 208 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": "minimax/h3-max/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the video to generate
    "resolution": "768P",  # Required. Native 480P or 768P output, or SR-enhanced 1440P or 4K short edge. options: 480P | 768P | 1440p-sr | 4k-sr
    "duration": 8,  # Required. The duration of the generated video in seconds
    "ratio": "1:1",  # The aspect ratio of the generated video. options: 21:9 | 16:9 | 4:3 | 1:1 | 3:4 | 9:16
    "prompt_expansion": False,  # Whether to expand the prompt for better results
}

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": "minimax/h3-max/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: 6Required: 4Optional: 2

modelstringrequired

model name

Default: "minimax/h3-max/text-to-video"

promptstringrequired

The text prompt describing the video to generate.

resolutionstringrequired

Native 480P or 768P output, or SR-enhanced 1440P or 4K short edge.

Default: "768P"

480P768P1440p-sr4k-sr

durationnumberrequired

The duration of the generated video in seconds.

Default: 8

56789101112131415

ratiostring

The aspect ratio of the generated video.

Default: "1:1"

21:916:94:31:13:49:16

prompt_expansionboolean

Whether to expand the prompt for better results.

Default: false

Example Request Body

{
  "model": "minimax/h3-max/text-to-video",
  "prompt": "A beautiful landscape",
  "resolution": "768P",
  "duration": 8,
  "ratio": "1:1",
  "prompt_expansion": false
}

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": "minimax/h3-max/text-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the video to generate.",
            "type": "string"
          },
          "resolution": {
            "default": "768P",
            "description": "Native 480P or 768P output, or SR-enhanced 1440P or 4K short edge.",
            "enum": [
              "480P",
              "768P",
              "1440p-sr",
              "4k-sr"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "duration": {
            "default": 8,
            "description": "The duration of the generated video in seconds.",
            "enum": [
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15
            ],
            "type": "number",
            "x-ui-component": "select"
          },
          "ratio": {
            "default": "1:1",
            "description": "The aspect ratio of the generated video.",
            "enum": [
              "21:9",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "prompt_expansion": {
            "default": false,
            "description": "Whether to expand the prompt for better results.",
            "type": "boolean",
            "x-ui-component": "switch"
          }
        },
        "required": [
          "model",
          "prompt",
          "resolution",
          "duration"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "ratio",
          "prompt_expansion"
        ]
      },
      "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

# minimax/h3-max/text-to-video

> MiniMax H3 Max text-to-video: generate a cinematic video from a text prompt. Supports 480P、768P, 5-15s., and 16:9/9:16/1:1/adaptive aspect ratios.


## 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**: `minimax/h3-max/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: `"minimax/h3-max/text-to-video"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the video to generate.

- **`resolution`** (`string`, _required_):
  Native 480P or 768P output, or SR-enhanced 1440P or 4K short edge.
  - Default: `"768P"`
  - Options: "480P", "768P", "1440p-sr", "4k-sr"

- **`duration`** (`number`, _required_):
  The duration of the generated video in seconds.
  - Default: `8`
  - Options: 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

- **`ratio`** (`string`, _optional_):
  The aspect ratio of the generated video.
  - Default: `"1:1"`
  - Options: "21:9", "16:9", "4:3", "1:1", "3:4", "9:16"

- **`prompt_expansion`** (`boolean`, _optional_):
  Whether to expand the prompt for better results.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "minimax/h3-max/text-to-video",
  "prompt": "",
  "resolution": "768P",
  "duration": 8
}
```


**Full Example**:

```json
{
  "model": "minimax/h3-max/text-to-video",
  "prompt": "",
  "resolution": "768P",
  "duration": 8,
  "ratio": "1:1",
  "prompt_expansion": false
}
```


### 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": "minimax/h3-max/text-to-video",
  "prompt": "",
  "resolution": "768P",
  "duration": 8,
  "ratio": "1:1",
  "prompt_expansion": false
}'

# 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/minimax/h3-max/text-to-video)

A tiny hamster wearing a miniature explorer's outfit walks through a vast golden wheat field at sunset, carrying a small red gift box. A gentle breeze moves the wheat, warm sunlight shines through the clouds, and the hamster suddenly stops as a flock of birds flies across the glowing sky. The camera slowly tracks behind the hamster, then moves into a cinematic close-up as it looks toward a distant cozy cottage. Emotional storytelling, cinematic composition, warm golden-hour lighting, shallow depth of field, subtle film grain, realistic fur details, natural movement, atmospheric, magical yet believable, 4K, cinematic film style.

MiniMax H3 Max Text-to-Video

MiniMax H3 Max Text-to-Video is a speed-optimized AI video generation model that turns a text prompt into a complete 24fps clip with synchronized audio. A 5-second 768P video renders in under 3 seconds, and even a full 15-second clip finishes in roughly 15 seconds — fast enough to iterate on an idea in real time rather than waiting on a queue.

Why Choose This?

Near-instant generation A 5s 768P clip in under 3 seconds; 15s clips in about 15 seconds.
Audio included Every clip is generated as complete audio-video at 24fps — no separate scoring or sound pass.
Cinematic motion Fluid camera work and lifelike movement from a plain-text description.
Flexible aspect ratios 21:9, 16:9, 4:3, 1:1, 3:4 or 9:16 to fit any platform.
Selectable duration Produce 5–15s clips at 480P or 768P.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the scene, subject, and action
resolution	Yes	Video resolution. Available options: `480P`, `768P`
duration	Yes	Duration of the generated video in seconds. Integer between 4 and 15. Available options: 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
ratio	Yes	Aspect ratio of the generated video. For text-to-video, `ratio` is required and cannot be `adaptive`. Available options: `21:9`, `16:9`, `4:3`, `1:1`, `3:4`, `9:16`
prompt_expansion	No	Enable AI prompt expansion via H3 Context-IR. When `true`, your prompt is first expanded into a rich, structured description (shot, soundscape, music) before generation, and an additional per-token Context-IR fee applies. Default `false`: the prompt is used as-is with no extra charge.
callback_url	No	HTTPS URL notified whenever the task status changes, so you can react to completion without polling. Requires a one-time verification handshake — see Task Status Callbacks below.

Task Status Callbacks (`callback_url`)

When you supply a callback_url, the MiniMax server notifies it on every task status change, so you don't have to poll for the result.

Verification handshake — right after the task is created, the server first sends a verification request to your URL containing a challenge field. Your endpoint must return the challenge value unchanged, within 3 seconds, to complete verification.
Status pushes — once verification succeeds, the server sends a POST to your URL every time the task status changes. The push body has the same structure as the Query Task (task status) response.

Callback status values: queued, running, succeeded, failed, cancelled.

The callback_url must be a publicly reachable HTTPS endpoint.

How to Use

Write your prompt — describe the scene, subject, lighting, and motion in detail.
Set resolution — higher for quality, lower for faster generation.
Choose an aspect ratio — pick one of the supported ratios; adaptive is not available for text-to-video.
Adjust duration — pick any length from 5s to 15s.
Run — submit and download your video.

Best Use Cases

Social Media Content — Short-form clips for TikTok, Reels, and Stories.
Concept Visualization — Bring ideas to life without filming.
Marketing Videos — Produce promotional content from text.
Storytelling — Create narrative scenes for creative projects.

Pro Tips

Be specific about camera angle, lighting, mood, and motion.
For text-to-video, ratio must be an explicit value (e.g. 16:9); adaptive is not supported for this mode.
Describe environmental effects (wind, smoke, golden-hour light) for richer results.

Notes

A prompt is required.
Supported durations between 5s and 15s, at 24fps with audio.
Resolution is limited to 480P and 768P — 2K is not available on H3 Max.
Keyframe interpolation and multimodal references (reference image / video / audio) are not supported.
Generation is asynchronous — submit, then poll for the finished video.

MiniMax H3 Max Image-to-Video — Animate a first-frame image at the same speed.
MiniMax H3 Text-to-Video — The standard H3 model, with 2K output and reference-to-video support.