google/gemini-omni-flash/text-to-video

Texto para Vídeo

Gemini Omni Flash Text-to-Video API by Google

google/gemini-omni-flash/text-to-video

Text-to-video

A natively multimodal Google DeepMind model that generates cinematic videos with synchronized native audio from a text prompt alone, grounded in real-world physics for controllable, high-speed video generation.

Entrada

Prompt *

Duração

Proporção

Thinking level

Resolução

Semente

Saída

Inativo

Os vídeos gerados serão exibidos aqui

Configure os parâmetros e clique em executar para começar a gerar

Cada execução custará $0.125. Com $10 você pode executar aproximadamente 80 vezes.

Você pode continuar com:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parâmetros

Exemplo 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": "google/gemini-omni-flash/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. Text prompt for generation
    "duration": 10,  # The duration of the generated video in seconds. (min: 3, max: 10)
    "aspect_ratio": "16:9",  # The aspect ratio of the generated video. options: 16:9 | 9:16
    "resolution": "720p",  # The resolution of the generated video. options: 720p
    "thinking_level": "default",  # Controls the amount of internal reasoning the model performs before generating a response. options: default | high | low
    "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()

Instalar

Instale o pacote necessário para a sua linguagem de programação.

pip install requests

Autenticação

Todas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do Atlas Cloud.

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

Cabeçalhos HTTP

import os

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

Mantenha sua chave de API segura

Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de backend.

Enviar uma solicitação

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 uma solicitação

Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.

POST/api/v1/model/generateVideo

Corpo da solicitação

import requests

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

data = {
    "model": "google/gemini-omni-flash/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']}")

Resposta

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

Verificar status

Consulte o endpoint de predição para verificar o status atual da sua solicitação.

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

Exemplo 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 status

processingA solicitação ainda está sendo processada.

completedA geração está completa. As saídas estão disponíveis.

succeededA geração foi bem-sucedida. As saídas estão disponíveis.

failedA geração falhou. Verifique o campo de erro.

Resposta concluída

{
  "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"
  }
}

Enviar arquivos

Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.

POST/api/v1/model/uploadMedia

Exemplo de upload

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}")

Resposta

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

Schema de entrada

Os seguintes parâmetros são aceitos no corpo da solicitação.

Total: 7Obrigatório: 2Opcional: 5

modelstringrequired

model name

Default: "google/gemini-omni-flash/text-to-video"

promptstringrequired

Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.

durationinteger

The duration of the generated video in seconds.

Default: 10Min: 3Max: 10

aspect_ratiostring

The aspect ratio of the generated video.

Default: "16:9"

16:99:16

resolutionstring

The resolution of the generated video.

Default: "720p"

720p

thinking_levelstring

Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.

Default: "default"

defaulthighlow

seedinteger

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

Default: -1

Exemplo de corpo da solicitação

{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": "A beautiful landscape",
  "duration": 10,
  "aspect_ratio": "16:9",
  "resolution": "720p",
  "thinking_level": "default",
  "seed": -1
}

Schema de saída

A API retorna uma resposta de predição com as URLs de saída geradas.

codeinteger

HTTP status code of the response.

messagestring

Human-readable message; non-empty on failure.

dataobject

Exemplo de resposta

{
  "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

O Atlas Cloud Skills integra mais de 400 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.

Clientes compatíveis

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clientes compatíveis

Instalar

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurar chave de API

Obtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.

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

Funcionalidades

Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.

Geração de imagensGere imagens com modelos como Nano Banana 2, Z-Image e mais.

Criação de vídeosCrie vídeos a partir de texto ou imagens com Kling, Vidu, Veo, etc.

Chat com LLMConverse com Qwen, DeepSeek e outros grandes modelos de linguagem.

Upload de mídiaEnvie arquivos locais para fluxos de trabalho de edição de imagens e imagem para vídeo.

Saiba mais

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

O Atlas Cloud MCP Server conecta seu IDE com mais de 400 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.

Clientes compatíveis

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clientes compatíveis

Instalar

npx -y atlascloud-mcp

Configuração

Adicione a seguinte configuração ao arquivo de configuração de MCP do seu IDE.

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

Ferramentas disponíveis

atlas_generate_imageGere imagens a partir de prompts de texto.

atlas_generate_videoCrie vídeos a partir de texto ou imagens.

atlas_chatConverse com grandes modelos de linguagem.

atlas_list_modelsExplore mais de 400 modelos de IA disponíveis.

atlas_quick_generateCriação de conteúdo em uma etapa com seleção automática de modelo.

atlas_upload_mediaEnvie arquivos locais para fluxos de trabalho de API.

Saiba mais

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "openapi": "3.0.0",
  "paths": {
    "/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"
    },
    "/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"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "google/gemini-omni-flash/text-to-video"
          },
          "prompt": {
            "description": "Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.",
            "type": "string"
          },
          "duration": {
            "default": 10,
            "description": "The duration of the generated video in seconds.",
            "type": "integer",
            "maximum": 10,
            "minimum": 3,
            "x-ui-component": "select"
          },
          "aspect_ratio": {
            "default": "16:9",
            "description": "The aspect ratio of the generated video.",
            "enum": [
              "16:9",
              "9:16"
            ],
            "type": "string",
            "x-placeholder": "Select one",
            "x-ui-component": "select"
          },
          "resolution": {
            "default": "720p",
            "description": "The resolution of the generated video.",
            "enum": [
              "720p"
            ],
            "type": "string",
            "x-placeholder": "Select one",
            "x-ui-component": "select"
          },
          "thinking_level": {
            "description": "Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.",
            "default": "default",
            "enum": [
              "default",
              "high",
              "low"
            ],
            "type": "string"
          },
          "seed": {
            "default": -1,
            "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",
          "duration",
          "aspect_ratio",
          "thinking_level",
          "resolution",
          "seed"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "code": {
            "description": "HTTP status code of the response.",
            "type": "integer"
          },
          "message": {
            "description": "Human-readable message; non-empty on failure.",
            "type": "string"
          },
          "data": {
            "type": "object",
            "properties": {
              "id": {
                "description": "Unique identifier for the prediction.",
                "type": "string"
              },
              "model": {
                "description": "Model ID used for the prediction.",
                "type": "string"
              },
              "outputs": {
                "description": "Array of URLs to the generated content. Null when status is not completed.",
                "type": "array",
                "items": {
                  "type": "string"
                },
                "nullable": true
              },
              "urls": {
                "description": "Object containing related API endpoints.",
                "type": "object",
                "properties": {
                  "get": {
                    "description": "URL to poll for the prediction result.",
                    "type": "string",
                    "format": "uri"
                  }
                }
              },
              "has_nsfw_contents": {
                "description": "Array of boolean values indicating NSFW detection for each output. Null if not applicable.",
                "type": "array",
                "items": {
                  "type": "boolean"
                },
                "nullable": true
              },
              "status": {
                "description": "Status of the task: created, processing, completed, timeout, or failed.",
                "type": "string"
              },
              "created_at": {
                "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
                "format": "date-time",
                "type": "string"
              },
              "error": {
                "description": "Error message if the task failed, empty string otherwise.",
                "type": "string"
              },
              "error_code": {
                "description": "Error code if the task failed.",
                "type": "integer"
              },
              "executionTime": {
                "description": "Total execution time in milliseconds.",
                "type": "number"
              },
              "timings": {
                "description": "Detailed timing breakdown.",
                "type": "object",
                "properties": {
                  "inference": {
                    "description": "Inference time in milliseconds.",
                    "type": "number"
                  }
                }
              }
            }
          }
        }
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

Template de Prompt Compatível com LLM

# google/gemini-omni-flash/text-to-video

> A natively multimodal Google DeepMind model that generates cinematic videos with synchronized native audio from a text prompt alone, grounded in real-world physics for controllable, high-speed video generation.


## 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**: `google/gemini-omni-flash/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: `"google/gemini-omni-flash/text-to-video"`

- **`prompt`** (`string`, _required_):
  Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.

- **`duration`** (`integer`, _optional_):
  The duration of the generated video in seconds.
  - Default: `10`
  - Min: 3
  - Max: 10

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

- **`thinking_level`** (`string`, _optional_):
  Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.
  - Default: `"default"`
  - Options: "default", "high", "low"

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"720p"`
  - Options: "720p"

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



**Required Parameters Example**:

```json
{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": "",
  "duration": 10,
  "aspect_ratio": "16:9",
  "thinking_level": "default",
  "resolution": "720p",
  "seed": -1
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):
  HTTP status code of the response.

- **`message`** (`string`, _optional_):
  Human-readable message; non-empty on failure.

- **`data`** (`object`, _optional_):
  - Properties:
    - **`id`** (`string`, _optional_):
      Unique identifier for the prediction.

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

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

    - **`urls`** (`object`, _optional_):
      Object containing related API endpoints.
      - Properties:
        - **`get`** (`string`, _optional_):
          URL to poll for the prediction result.


    - **`has_nsfw_contents`** (`array[boolean]`, _optional_):
      Array of boolean values indicating NSFW detection for each output. Null if not applicable.

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

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

    - **`error`** (`string`, _optional_):
      Error message if the task failed, empty string otherwise.

    - **`error_code`** (`integer`, _optional_):
      Error code if the task failed.

    - **`executionTime`** (`number`, _optional_):
      Total execution time in milliseconds.

    - **`timings`** (`object`, _optional_):
      Detailed timing breakdown.
      - Properties:
        - **`inference`** (`number`, _optional_):
          Inference time in milliseconds.





**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": {
    "id": "",
    "model": "",
    "outputs": [
      ""
    ],
    "urls": {
      "get": ""
    },
    "has_nsfw_contents": [],
    "status": "",
    "created_at": "",
    "error": "",
    "error_code": 0,
    "executionTime": 0,
    "timings": {
      "inference": 0
    }
  }
}
```


## 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": "google/gemini-omni-flash/text-to-video",
  "prompt": "",
  "duration": 10,
  "aspect_ratio": "16:9",
  "thinking_level": "default",
  "resolution": "720p",
  "seed": -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/google/gemini-omni-flash/text-to-video)

A solitary man leans against a vintage red coupe on an empty seaside road at dusk. The wind gently moves the grass and his coat while a lonely cloud hangs motionless above him. The camera slowly tracks sideways, capturing reflections on the car body and the endless blue ocean beyond.

Carregando...

Gemini Omni Flash — Text to Video

Model ID: google/gemini-omni-flash/text-to-video

Gemini Omni Flash is Google DeepMind's high-performance, natively multimodal model built for high-speed video generation, editing, and cinematic control. This variant accepts a text prompt only, making it ideal for pure creative generation where you describe the entire scene through language.

Overview

Gemini Omni Flash (gemini-omni-flash-preview) was introduced by Google alongside Nano Banana 2 Lite as a new generation of multimodal media models. Unlike traditional pipelines that stitch modalities together, Omni Flash is a single transformer that processes text, images, audio, and video simultaneously, producing output that is more cohesive, consistent, and controllable.

What sets it apart from earlier video models (such as the Veo family) is that it natively generates audio with every video — dialogue, ambience, music, and sound design are produced together with the picture rather than added afterward. The model is grounded in Gemini's real-world knowledge, so it reasons about physics, narrative logic, culture, and visual composition to produce results that feel intentional and cinematic. Generated media carries an invisible SynthID watermark.

AtlasCloud exposes Gemini Omni Flash through four endpoints — text-to-video, image-to-video, reference-to-video, and video-edit. All four route to the same gemini-omni-flash-preview model and differ only by the input modality they accept, corresponding to the model's task parameter (text_to_video, image_to_video, reference_to_video, edit). This endpoint maps to text_to_video.

Inputs

This variant takes a text prompt as its only content input. You describe the subjects, actions, camera language, lighting, mood, style, and any dialogue or sound design entirely in natural language, and the model synthesizes a video (with audio) from scratch.

Key Capabilities

Rich prompt understanding — Describe subjects, actions, camera movements, lighting, mood, style, and audio in a single prompt of up to 20,000 characters.
Native audio generation — Every clip is rendered with a synchronized soundtrack (speech, music, effects) driven by your description.
World-grounded realism — Physics, motion, and scene dynamics informed by Gemini's real-world knowledge.
Cinematic control — Camera framing, pacing, and single-scene composition guided directly from the prompt.
Adjustable reasoning — The thinking_level control trades latency for quality on complex prompts.
Reproducible results — Set a fixed seed to reproduce or iterate on a specific generation.

Input Parameters

Parameter	Type	Required	Default	Description
`model`	string	Yes	`google/gemini-omni-flash/text-to-video`	Model identifier
`prompt`	string	Yes	—	Text description of the video. Max 20,000 characters.
`duration`	integer	No	`10`	Video length in seconds. Range: `3`–`10`.
`aspect_ratio`	string	No	`16:9`	Output aspect ratio. Enum: `16:9`, `9:16`.
`thinking_level`	string	No	`default`	Internal reasoning effort. Enum: `default`, `high`, `low`.
`resolution`	string	No	`720p`	Output resolution. Enum: `720p`.
`seed`	integer	No	`-1`	Random seed for reproducibility. `-1` uses a random seed.

Use Cases

Creative storytelling — Generate cinematic scenes with sound from narrative descriptions.
Concept visualization — Quickly visualize ideas, moods, or environments.
Storyboard prototyping — Turn scene descriptions into video drafts before full production.
Marketing assets — Produce short-form video content directly from copy briefs.
Educational content — Illustrate concepts, processes, or historical scenes through natural language.

Pricing

Billing is based on the duration of the generated video, charged at a flat per-second rate.

SKU	Rate
Per second of output	$0.125

Formula: max(3, duration) × $0.125

Billing is per second, with a 3-second minimum — durations below 3s are billed as 3s.
Example: a 10-second video costs 10 × $0.125 = $1.25.
Example: a 3-second video costs 3 × $0.125 = $0.375.

Gemini Omni Flash Text-to-Video API by Google

Entrada

Saída

Parâmetros

Exemplo de código

Instalar

Autenticação

Cabeçalhos HTTP

Enviar uma solicitação

Enviar uma solicitação

Corpo da solicitação

Resposta

Verificar status

Exemplo de polling

Valores de status

Resposta concluída

Enviar arquivos

Exemplo de upload

Resposta

Schema de entrada

Exemplo de corpo da solicitação

Schema de saída

Exemplo de resposta

Atlas Cloud Skills

Clientes compatíveis

Instalar

Configurar chave de API

Funcionalidades

MCP Server

Clientes compatíveis

Instalar

Configuração

Ferramentas disponíveis

API Schema

Template de Prompt Compatível com LLM

Gemini Omni Flash — Text to Video

Overview

Inputs

Key Capabilities

Input Parameters

Use Cases

Pricing

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Entrada

Saída

Parâmetros

Exemplo de código

Instalar

Autenticação

Cabeçalhos HTTP

Enviar uma solicitação

Enviar uma solicitação

Corpo da solicitação

Resposta

Verificar status

Exemplo de polling

Valores de status

Resposta concluída

Enviar arquivos

Exemplo de upload

Resposta

Schema de entrada