minimax/h3/text-to-video

Texto a Video

MiniMax H3 Text-to-Video API by MiniMax

minimax/h3/text-to-video

Text-to-video

MiniMax H3 text-to-video: generate a cinematic video from a text prompt. Supports 2K, 5-15s., and 16:9/9:16/1:1/adaptive aspect ratios.

Entrada

Prompt *

Resolución

Duración

Relación de aspecto

Salida

Inactivo

Los videos generados se mostrarán aquí

Configura los parámetros y haz clic en ejecutar para comenzar a generar

Cada ejecución costará $0.14. Con $10 puedes ejecutar aproximadamente 71 veces.

Puedes continuar con:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parámetros

Ejemplo de código
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/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": "2K",  # The resolution of the generated video. options: 2K
    "duration": 8,  # 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
}

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()

Instalar

Instala el paquete de dependencias necesario.

pip install requests

Autenticación

Todas las solicitudes de API requieren autenticación mediante una clave de API. Puedes obtener tu clave de API desde el panel de Atlas Cloud.

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

Encabezados HTTP

import os

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

Mantén tu clave de API segura

Nunca expongas tu clave de API en código del lado del cliente ni en repositorios públicos. Usa variables de entorno o un proxy de backend en su lugar.

Enviar una solicitud

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())

Enviar una solicitud

Envía una solicitud de generación asíncrona. La API devuelve un ID de predicción que puedes usar para verificar el estado y obtener el resultado.

POST/api/v1/model/generateVideo

Cuerpo de la solicitud

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/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']}")

Respuesta

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

Verificar estado

Consulta el endpoint de predicción para verificar el estado actual de tu solicitud.

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

Ejemplo de polling

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)

Valores de estado

processingLa solicitud aún se está procesando.

completedLa generación está completa. Las salidas están disponibles.

succeededLa generación fue exitosa. Las salidas están disponibles.

failedLa generación falló. Verifica el campo de error.

Respuesta completada

{
  "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"
  }
}

Subir archivos

Sube archivos al almacenamiento de Atlas Cloud y obtén una URL que puedes usar en tus solicitudes de API. Usa multipart/form-data para subir.

POST/api/v1/model/uploadMedia

Ejemplo de carga

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}")

Respuesta

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

Schema de entrada

Los siguientes parámetros se aceptan en el cuerpo de la solicitud.

Total: 5Obligatorio: 2Opcional: 3

modelstringrequired

model name

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

promptstringrequired

The text prompt describing the video to generate.

resolutionstring

The resolution of the generated video.

Default: "2K"

durationnumber

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

Ejemplo de cuerpo de solicitud

{
  "model": "minimax/h3/text-to-video",
  "prompt": "A beautiful landscape",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}

Schema de salida

La API devuelve una respuesta de predicción con las URL de salida generadas.

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.

Ejemplo de respuesta

{
  "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 integra más de 400 modelos de IA directamente en tu asistente de codificación con IA. Un solo comando para instalar y luego usa lenguaje natural para generar imágenes, videos y chatear con LLM.

Clientes compatibles

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clientes compatibles

Instalar

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurar clave de API

Obtén tu clave de API desde el panel de Atlas Cloud y configúrala como variable de entorno.

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

Funcionalidades

Una vez instalado, puedes usar lenguaje natural en tu asistente de IA para acceder a todos los modelos de Atlas Cloud.

Generación de imágenesGenera imágenes con modelos como Nano Banana 2, Z-Image y más.

Creación de videosCrea videos a partir de texto o imágenes con Kling, Vidu, Veo, etc.

Chat con LLMChatea con Qwen, DeepSeek y otros modelos de lenguaje de gran escala.

Carga de mediosSube archivos locales para flujos de trabajo de edición de imágenes e imagen a video.

Más información

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server conecta tu IDE con más de 400 modelos de IA a través del Model Context Protocol. Funciona con cualquier cliente compatible con MCP.

Clientes compatibles

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clientes compatibles

Instalar

npx -y atlascloud-mcp

Configuración

Agrega la siguiente configuración al archivo de configuración de MCP de tu IDE.

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

Herramientas disponibles

atlas_generate_imageGenera imágenes a partir de indicaciones de texto.

atlas_generate_videoCrea videos a partir de texto o imágenes.

atlas_chatChatea con modelos de lenguaje de gran escala.

atlas_list_modelsExplora más de 400 modelos de IA disponibles.

atlas_quick_generateCreación de contenido en un solo paso con selección automática de modelo.

atlas_upload_mediaSube archivos locales para flujos de trabajo de API.

Más información

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "minimax/h3/text-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the video to generate.",
            "type": "string"
          },
          "resolution": {
            "default": "2K",
            "description": "The resolution of the generated video.",
            "enum": [
              "2K"
            ],
            "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"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "ratio"
        ]
      },
      "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"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "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"
    }
  ]
}

Plantilla de Prompt Compatible con LLM

# minimax/h3/text-to-video

> MiniMax H3 text-to-video: generate a cinematic video from a text prompt. Supports 2K, 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/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/text-to-video"`

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

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"2K"`
  - Options: "2K"

- **`duration`** (`number`, _optional_):
  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"



**Required Parameters Example**:

```json
{
  "model": "minimax/h3/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "minimax/h3/text-to-video",
  "prompt": "",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}
```


### 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”).

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`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": "",
  "has_nsfw_contents": [],
  "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/text-to-video",
  "prompt": "",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}'

# 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/text-to-video)

Cinematic medium close-up of a desert warrior wearing a high-tech dust mask and stillsuit, glowing piercing bright blue eyes (eyes of Ibad). The wind blows fine dust across their face. Warm desert sunlight casting sharp side shadows. Cinematic focus, shallow depth of field, slow-motion film grain, photorealistic, Dune style, 8k.

Cargando...

MiniMax H3 Text-to-Video

MiniMax H3 Text-to-Video is a state-of-the-art AI video generation model that creates cinematic, high-fidelity videos directly from a text prompt. With crisp detail up to 2K, smooth natural motion, and flexible aspect ratios, it turns a single description into a polished clip.

Why Choose This?

High resolution output Generate videos in 2K quality.
Cinematic motion Fluid camera work and lifelike movement from a plain-text description.
Flexible aspect ratios 16:9, 9:16, 1:1, or adaptive to fit any platform.
Selectable duration Produce 5s or 10s clips.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the scene, subject, and action
resolution	No	Output quality: 2K (default)
duration	No	Video length in seconds: 8 (default), 5-10
ratio	No	Aspect ratio: adaptive (default), 9:16, 1:1, 4:3

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 — match your target platform, or use adaptive.
Adjust duration — pick 5s or 10s.
Run — submit and download your video.

Pricing

Billed per second of generated video, by resolution:

Resolution	Cost per second
2K	$0.14
768p	$0.10

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.
Use "adaptive" ratio to let the model choose the best framing for the scene.
Higher resolution (2K) suits hero shots; 768P is great for quick iteration.
Describe environmental effects (wind, smoke, golden-hour light) for richer results.

Notes

A prompt is required.
Supported durations are 5s and 10s.
Generation is asynchronous — submit, then poll for the finished video.

MiniMax H3 Image-to-Video — Animate a first-frame image (optionally with a last frame).
MiniMax H3 Reference-to-Video — Keep a subject consistent from a reference image.

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MiniMax H3 Text-to-Video API by MiniMax

Entrada

Salida

Parámetros

Ejemplo de código

Instalar

Autenticación

Encabezados HTTP

Enviar una solicitud

Enviar una solicitud

Cuerpo de la solicitud

Respuesta

Verificar estado

Ejemplo de polling

Valores de estado

Respuesta completada

Subir archivos

Ejemplo de carga

Respuesta

Schema de entrada

Ejemplo de cuerpo de solicitud

Schema de salida

Ejemplo de respuesta

Atlas Cloud Skills

Clientes compatibles

Instalar

Configurar clave de API

Funcionalidades

MCP Server

Clientes compatibles

Instalar

Configuración

Herramientas disponibles

API Schema

Plantilla de Prompt Compatible con LLM

MiniMax H3 Text-to-Video

Why Choose This?

Parameters

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

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Hailuo-2.3 t2v Standard

Hailuo-2.3 t2v Pro

Hailuo-2.3 i2v Standard

Hailuo-2.3 i2v Pro

Hailuo-2.3 Fast

Hailuo-02 t2v Pro

Hailuo-02 Fast

Hailuo 02 Pro

Hailuo 02 t2v Standard

Hailuo 02 i2v Standard

Hailuo 02 i2v Pro

Hailuo 02 Standard

Sync.so Lipsync v3

VEED Lipsync

Una sola API para toda la IA multimedia.

Entrada

Salida

Parámetros

Ejemplo de código

Instalar

Autenticación

Encabezados HTTP

Enviar una solicitud

Enviar una solicitud

Cuerpo de la solicitud

Respuesta

Verificar estado

Ejemplo de polling

Valores de estado

Respuesta completada

Subir archivos

Ejemplo de carga

Respuesta