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Atlas Cloud API Integration Guide

Atlas Cloud API 集成指南

Atlas Cloud is an AI API aggregation platform that provides access to 300+ image, video, and LLM models through a unified interface. This skill helps you quickly integrate Atlas Cloud API into any project.
Atlas Cloud是一个AI API聚合平台，通过统一接口提供300+图像、视频及LLM模型的访问能力。本技能可帮助你快速将Atlas Cloud API集成到任意项目中。

Quick Start

快速开始

1. Get an API Key

1. 获取API密钥

Create an API Key at Atlas Cloud Console.
在Atlas Cloud控制台创建API密钥。

2. Set Environment Variable

2. 设置环境变量

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

API Architecture

API架构

Atlas Cloud has the following API endpoints:
EndpointBase URLPurpose
Media Generation API
https://api.atlascloud.ai/api/v1
Image generation, video generation, poll results, upload media
LLM API
https://api.atlascloud.ai/v1
Chat completions (OpenAI-compatible)
All requests require the following headers:
Authorization: Bearer $ATLASCLOUD_API_KEY
Content-Type: application/json
Atlas Cloud包含以下API端点：
端点基础URL用途
媒体生成API
https://api.atlascloud.ai/api/v1
图像生成、视频生成、查询结果、上传媒体
LLM API
https://api.atlascloud.ai/v1
对话补全（兼容OpenAI）
所有请求需包含以下请求头：
Authorization: Bearer $ATLASCLOUD_API_KEY
Content-Type: application/json

Full Endpoint List

完整端点列表

MethodEndpointDescription
POST
/api/v1/model/generateImage
Submit image generation task
POST
/api/v1/model/generateVideo
Submit video generation task
GET
/api/v1/model/prediction/{id}
Check generation task status and result
POST
/api/v1/model/uploadMedia
Upload local media file to get a public URL
POST
/v1/chat/completions
LLM chat (OpenAI-compatible format)
GET
api.atlascloud.ai/api/v1/models
List all available models (no auth required)
方法端点描述
POST
/api/v1/model/generateImage
提交图像生成任务
POST
/api/v1/model/generateVideo
提交视频生成任务
GET
/api/v1/model/prediction/{id}
检查生成任务状态及结果
POST
/api/v1/model/uploadMedia
上传本地媒体文件以获取公共URL
POST
/v1/chat/completions
LLM对话（兼容OpenAI格式）
GET
api.atlascloud.ai/api/v1/models
列出所有可用模型（无需认证）

MCP Tools (9 Tools)

MCP工具（9个工具）

If the user has installed the Atlas Cloud MCP Server (
npx atlascloud-mcp
), the following 9 tools are available for direct invocation:
若用户已安装Atlas Cloud MCP Server（
npx atlascloud-mcp
），则可直接调用以下9个工具：

Model Discovery Tools

模型发现工具

atlas_list_models
— List All Models

atlas_list_models
— 列出所有模型

  • Params: 
    type
    (optional): 
    "Text"
    | 
    "Image"
    | 
    "Video"
  • Purpose: List all available models, optionally filtered by type
  • Examples: No params to list all; 
    type="Image"
    for image models only
  • 参数：
    type
    （可选）：
    "Text"
    | 
    "Image"
    | 
    "Video"
  • 用途：列出所有可用模型，可按类型筛选
  • 示例：无参数则列出全部；
    type="Image"
    仅列出图像模型

atlas_search_docs
— Search Models & Docs

atlas_search_docs
— 搜索模型与文档

  • Params: 
    query
    (required): Search keyword matching model names, types, providers, tags
  • Purpose: Fuzzy search models by keyword. Returns detailed API schema info when there's only one match
  • Examples: 
    "video generation"
    , 
    "deepseek"
    , 
    "image edit"
    , 
    "qwen"
  • 参数：
    query
    （必填）：匹配模型名称、类型、提供商、标签的搜索关键词
  • 用途：按关键词模糊搜索模型。若仅匹配到一个结果，返回详细的API schema信息
  • 示例：
    "video generation"
    , 
    "deepseek"
    , 
    "image edit"
    , 
    "qwen"

atlas_get_model_info
— Get Model Details

atlas_get_model_info
— 获取模型详情

  • Params: 
    model
    (required): Model ID, e.g. 
    "deepseek-ai/deepseek-v3.2"
  • Purpose: Get full model info including API docs, input/output schema, pricing, cURL examples, Playground link
  • Examples: 
    model="deepseek-ai/deepseek-v3.2"
  • 参数：
    model
    （必填）：模型ID，例如
    "deepseek-ai/deepseek-v3.2"
  • 用途：获取完整模型信息，包括API文档、输入输出schema、定价、cURL示例、Playground链接
  • 示例：
    model="deepseek-ai/deepseek-v3.2"

Generation Tools

生成工具

atlas_generate_image
— Generate Image

atlas_generate_image
— 生成图像

  • Params: 
    • model
      (required): Exact image model ID
    • params
      (required): Model-specific parameter JSON object (e.g. 
      prompt
      , 
      image_size
      , etc.)
  • Purpose: Submit image generation task, returns prediction ID. Must verify model ID first via 
    atlas_list_models
    or 
    atlas_search_docs
  • Returns: prediction ID — use 
    atlas_get_prediction
    to check result
  • 参数： 
    • model
      （必填）：准确的图像模型ID
    • params
      （必填）：模型专属参数JSON对象（如
      prompt
      、
      image_size
      等）
  • 用途：提交图像生成任务，返回预测ID。必须先通过
    atlas_list_models
    或
    atlas_search_docs
    验证模型ID
  • 返回值：预测ID — 使用
    atlas_get_prediction
    检查结果

atlas_generate_video
— Generate Video

atlas_generate_video
— 生成视频

  • Params: 
    • model
      (required): Exact video model ID
    • params
      (required): Model-specific parameter JSON object (e.g. 
      prompt
      , 
      duration
      , 
      aspect_ratio
      , 
      image_url
      , etc.)
  • Purpose: Submit video generation task, returns prediction ID
  • Returns: prediction ID — video generation typically takes 1-5 minutes
  • 参数： 
    • model
      （必填）：准确的视频模型ID
    • params
      （必填）：模型专属参数JSON对象（如
      prompt
      、
      duration
      、
      aspect_ratio
      、
      image_url
      等）
  • 用途：提交视频生成任务，返回预测ID
  • 返回值：预测ID — 视频生成通常需要1-5分钟

atlas_quick_generate
— Quick Generate (One-Step)

atlas_quick_generate
— 一键快速生成

  • Params: 
    • model_keyword
      (required): Model search keyword, e.g. 
      "nano banana"
      , 
      "seedream"
      , 
      "kling v3"
    • type
      (required): 
      "Image"
      | 
      "Video"
    • prompt
      (required): Text description of what to generate
    • image_url
      (optional): Source image URL for image-to-video or image editing models
    • extra_params
      (optional): Additional model-specific parameters to override defaults
  • Purpose: One-step generation — automatically searches model → fetches schema → builds params → submits task. No need to know exact model IDs
  • Examples: 
    model_keyword="seedream v5", type="Image", prompt="a cute cat"
  • 参数： 
    • model_keyword
      （必填）：模型搜索关键词，例如
      "nano banana"
      , 
      "seedream"
      , 
      "kling v3"
    • type
      （必填）：
      "Image"
      | 
      "Video"
    • prompt
      （必填）：生成内容的文本描述
    • image_url
      （可选）：图生视频或图像编辑模型的源图像URL
    • extra_params
      （可选）：覆盖默认值的额外模型专属参数
  • 用途：一键生成 — 自动搜索模型→获取schema→构建参数→提交任务。无需知晓准确模型ID
  • 示例：
    model_keyword="seedream v5", type="Image", prompt="a cute cat"

atlas_chat
— LLM Chat

atlas_chat
— LLM对话

  • Params: 
    • model
      (required): LLM model ID
    • messages
      (required): Array of message objects with 
      role
      and 
      content
    • temperature
      (optional): Sampling temperature 0-2
    • max_tokens
      (optional): Maximum response tokens
    • top_p
      (optional): Nucleus sampling parameter 0-1
  • Purpose: Send OpenAI-compatible chat completion request
  • 参数： 
    • model
      （必填）：LLM模型ID
    • messages
      （必填）：包含
      role
      和
      content
      的消息对象数组
    • temperature
      （可选）：采样温度0-2
    • max_tokens
      （可选）：最大响应令牌数
    • top_p
      （可选）：核采样参数0-1
  • 用途：发送兼容OpenAI格式的对话补全请求

Utility Tools

实用工具

atlas_get_prediction
— Check Generation Result

atlas_get_prediction
— 检查生成结果

  • Params: 
    prediction_id
    (required): Prediction ID returned from a generation request
  • Purpose: Check image/video generation task status and result
  • Status values: 
    starting
    → 
    processing
    → 
    completed
    /
    succeeded
    /
    failed
  • On completion: Returns output URL list — can download locally via curl/wget
  • 参数：
    prediction_id
    （必填）：生成请求返回的预测ID
  • 用途：检查图像/视频生成任务的状态及结果
  • 状态值：
    starting
    → 
    processing
    → 
    completed
    /
    succeeded
    /
    failed
  • 完成后：返回输出URL列表 — 可通过curl/wget本地下载

atlas_upload_media
— Upload Media File

atlas_upload_media
— 上传媒体文件

  • Params: 
    file_path
    (required): Absolute path to the local file
  • Purpose: Upload local image/media file to Atlas Cloud and get a publicly accessible URL. Use this to provide 
    image_url
    for image editing or image-to-video models
  • Workflow:
    1. Upload local file with this tool to get a URL
    2. Use the returned URL as the 
      image_url
      parameter for 
      atlas_generate_image
      , 
      atlas_generate_video
      , or 
      atlas_quick_generate
  • Note: Only for Atlas Cloud generation tasks. Uploaded files are temporary and will be cleaned up periodically. Uploading content unrelated to generation tasks (e.g., bulk hosting, illegal content, or abuse) may result in API key suspension
  • 参数：
    file_path
    （必填）：本地文件的绝对路径
  • 用途：将本地图像/媒体文件上传至Atlas Cloud并获取可公开访问的URL。用于为图像编辑或图生视频模型提供
    image_url
    参数
  • 流程：
    1. 使用本工具上传本地文件以获取URL
    2. 将返回的URL作为
      image_url
      参数传入
      atlas_generate_image
      、
      atlas_generate_video
      或
      atlas_quick_generate
  • 注意：仅适用于Atlas Cloud生成任务。上传的文件为临时文件，会定期清理。上传与生成任务无关的内容（如批量托管、非法内容或滥用）可能导致API密钥被暂停使用

Image Generation

图像生成

Image generation is an asynchronous two-step process: submit task → poll result.
图像生成是异步两步流程：提交任务 → 查询结果。

Submit Image Generation Task

提交图像生成任务

POST https://api.atlascloud.ai/api/v1/model/generateImage
Request body:
json
{
  "model": "bytedance/seedream-v5.0-lite",
  "prompt": "A beautiful sunset over mountains",
  "image_size": "1024x1024"
}
Response:
json
{
  "code": 200,
  "data": {
    "id": "prediction_abc123",
    "status": "starting"
  }
}
Different models accept different parameters. Common parameters include:
  • prompt
    (required): Image description
  • image_size
    / 
    width
    + 
    height
    : Dimensions
  • num_inference_steps
    : Inference steps
  • guidance_scale
    : Guidance scale
  • image_url
    : Input image (for image-to-image models)
POST https://api.atlascloud.ai/api/v1/model/generateImage
请求体：
json
{
  "model": "bytedance/seedream-v5.0-lite",
  "prompt": "A beautiful sunset over mountains",
  "image_size": "1024x1024"
}
响应：
json
{
  "code": 200,
  "data": {
    "id": "prediction_abc123",
    "status": "starting"
  }
}
不同模型接受不同参数，常见参数包括：
  • prompt
    （必填）：图像描述
  • image_size
    / 
    width
    + 
    height
    ：尺寸
  • num_inference_steps
    ：推理步数
  • guidance_scale
    ：引导系数
  • image_url
    ：输入图像（适用于图生图模型）

Poll Generation Result

查询生成结果

GET https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}
Response:
json
{
  "code": 200,
  "data": {
    "id": "prediction_abc123",
    "status": "completed",
    "outputs": ["https://cdn.atlascloud.ai/generated/xxx.png"]
  }
}
Possible 
status
values: 
starting
→ 
processing
→ 
completed
/ 
failed
Image generation typically takes 10-30 seconds. Poll every 3 seconds.
GET https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}
响应：
json
{
  "code": 200,
  "data": {
    "id": "prediction_abc123",
    "status": "completed",
    "outputs": ["https://cdn.atlascloud.ai/generated/xxx.png"]
  }
}
可能的
status
值：
starting
→ 
processing
→ 
completed
/ 
failed
图像生成通常需要10-30秒，建议每3秒查询一次。

Video Generation

视频生成

Video generation follows the exact same flow as image generation, just with a different endpoint.
视频生成流程与图像生成完全一致，仅端点不同。

Submit Video Generation Task

提交视频生成任务

POST https://api.atlascloud.ai/api/v1/model/generateVideo
Request body:
json
{
  "model": "kwaivgi/kling-v3.0-std/text-to-video",
  "prompt": "A rocket launching into space",
  "duration": 5,
  "aspect_ratio": "16:9"
}
Common video model parameters:
  • prompt
    (required): Video description
  • image_url
    : Input image (for image-to-video models)
  • duration
    : Video duration in seconds
  • aspect_ratio
    : Aspect ratio (e.g., 
    "16:9"
    , 
    "9:16"
    , 
    "1:1"
    )
Poll results using the same prediction endpoint. Video generation typically takes 1-5 minutes.
POST https://api.atlascloud.ai/api/v1/model/generateVideo
请求体：
json
{
  "model": "kwaivgi/kling-v3.0-std/text-to-video",
  "prompt": "A rocket launching into space",
  "duration": 5,
  "aspect_ratio": "16:9"
}
常见视频模型参数：
  • prompt
    （必填）：视频描述
  • image_url
    ：输入图像（适用于图生视频模型）
  • duration
    ：视频时长（秒）
  • aspect_ratio
    ：宽高比（如
    "16:9"
    , 
    "9:16"
    , 
    "1:1"
    ）
使用相同的预测端点查询结果。视频生成通常需要1-5分钟。

Upload Media

上传媒体

Upload a local file to Atlas Cloud to get a publicly accessible URL. This is required when you need to provide an 
image_url
to image-editing or image-to-video models but only have a local file.
将本地文件上传至Atlas Cloud以获取可公开访问的URL。当你需要为图像编辑或图生视频模型提供
image_url
但仅有本地文件时，需使用此功能。

Upload Endpoint

上传端点

POST https://api.atlascloud.ai/api/v1/model/uploadMedia
Content-Type: multipart/form-data
Authorization: Bearer $ATLASCLOUD_API_KEY
Request: multipart form data with a 
file
field containing the file binary.
Response:
json
{
  "code": 200,
  "data": {
    "download_url": "https://atlas-img.oss-accelerate-overseas.aliyuncs.com/media/xxx.jpg",
    "filename": "photo.jpg",
    "size": 123456
  }
}
POST https://api.atlascloud.ai/api/v1/model/uploadMedia
Content-Type: multipart/form-data
Authorization: Bearer $ATLASCLOUD_API_KEY
请求：包含
file
字段（文件二进制数据）的multipart表单数据。
响应：
json
{
  "code": 200,
  "data": {
    "download_url": "https://atlas-img.oss-accelerate-overseas.aliyuncs.com/media/xxx.jpg",
    "filename": "photo.jpg",
    "size": 123456
  }
}

Workflow: Local Image → Image-to-Video

流程：本地图像 → 图生视频

  1. Upload local image → get URL
  2. Use URL as 
    image_url
    parameter in generation request
Important: This upload endpoint is strictly for temporary use with Atlas Cloud generation tasks. Uploaded files will be cleaned up periodically. Do NOT use this as permanent file hosting, CDN, or for any purpose unrelated to Atlas Cloud image/video generation. Abuse (e.g., bulk uploads, hosting illegal or unrelated content) may result in immediate API key suspension.
  1. 上传本地图像 → 获取URL
  2. 将URL作为生成请求中的
    image_url
    参数
重要提示：此上传端点仅可临时用于Atlas Cloud生成任务。上传的文件会定期清理。请勿将其用作永久文件托管、CDN或任何与Atlas Cloud图像/视频生成无关的用途。滥用行为（如批量上传、托管非法或无关内容）可能导致API密钥立即被暂停使用。

LLM Chat API (OpenAI-Compatible)

LLM对话API（兼容OpenAI）

The LLM API is fully compatible with the OpenAI format. You can use the OpenAI SDK directly.
POST https://api.atlascloud.ai/v1/chat/completions
Request body:
json
{
  "model": "qwen/qwen3.5-397b-a17b",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Hello!"}
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}
Response (standard OpenAI format):
json
{
  "id": "chatcmpl-xxx",
  "model": "qwen/qwen3.5-397b-a17b",
  "choices": [{
    "index": 0,
    "message": {"role": "assistant", "content": "Hello! How can I help?"},
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 8,
    "total_tokens": 28
  }
}
LLM API完全兼容OpenAI格式，你可直接使用OpenAI SDK。
POST https://api.atlascloud.ai/v1/chat/completions
请求体：
json
{
  "model": "qwen/qwen3.5-397b-a17b",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Hello!"}
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}
响应（标准OpenAI格式）：
json
{
  "id": "chatcmpl-xxx",
  "model": "qwen/qwen3.5-397b-a17b",
  "choices": [{
    "index": 0,
    "message": {"role": "assistant", "content": "Hello! How can I help?"},
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 8,
    "total_tokens": 28
  }
}

Using OpenAI SDK

使用OpenAI SDK

Since Atlas Cloud LLM API is fully OpenAI-compatible, you can use the official SDKs directly:
Python:
python
from openai import OpenAI

client = OpenAI(
    api_key="your-atlascloud-api-key",
    base_url="https://api.atlascloud.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.5-397b-a17b",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=1024
)
print(response.choices[0].message.content)
Node.js / TypeScript:
typescript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-atlascloud-api-key',
  baseURL: 'https://api.atlascloud.ai/v1',
});

const response = await client.chat.completions.create({
  model: 'qwen/qwen3.5-397b-a17b',
  messages: [{ role: 'user', content: 'Hello!' }],
  max_tokens: 1024,
});
console.log(response.choices[0].message.content);
由于Atlas Cloud LLM API完全兼容OpenAI，你可直接使用官方SDK：
Python：
python
from openai import OpenAI

client = OpenAI(
    api_key="your-atlascloud-api-key",
    base_url="https://api.atlascloud.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.5-397b-a17b",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=1024
)
print(response.choices[0].message.content)
Node.js / TypeScript：
typescript
import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-atlascloud-api-key',
  baseURL: 'https://api.atlascloud.ai/v1',
});

const response = await client.chat.completions.create({
  model: 'qwen/qwen3.5-397b-a17b',
  messages: [{ role: 'user', content: 'Hello!' }],
  max_tokens: 1024,
});
console.log(response.choices[0].message.content);

Code Templates

代码模板

For full implementation code with polling logic, error handling, and streaming support, read the reference files:
  • references/image-gen.md
     — Complete image generation implementation (Python / Node.js / cURL)
  • references/video-gen.md
     — Complete video generation implementation, including image-to-video
  • references/llm-chat.md
     — LLM chat implementation with streaming support
  • references/upload.md
     — Media file upload implementation (Python / Node.js / cURL)
  • references/quick-generate.md
     — Quick generation with auto model search (Python / Node.js)
  • references/models.md
     — Popular model ID quick reference
Read the corresponding reference file when you need to write specific integration code.
如需包含查询逻辑、错误处理和流式支持的完整实现代码，请参考以下参考文件：
  • references/image-gen.md
     — 完整图像生成实现（Python / Node.js / cURL）
  • references/video-gen.md
     — 完整视频生成实现，包括图生视频
  • references/llm-chat.md
     — 支持流式的LLM对话实现
  • references/upload.md
     — 媒体文件上传实现（Python / Node.js / cURL）
  • references/quick-generate.md
     — 自动模型搜索的快速生成实现（Python / Node.js）
  • references/models.md
     — 热门模型ID速查
当你需要编写特定集成代码时，请阅读对应的参考文件。

IMPORTANT: Always Verify Model IDs

重要提示：始终验证模型ID

Model IDs change frequently as new versions are released and old ones are deprecated. Unless you are 100% certain of an exact model ID, always fetch the real model list first before writing any integration code:
GET https://api.atlascloud.ai/api/v1/models
This endpoint requires no authentication and returns all currently available models with their exact IDs, types, and pricing. Never guess or fabricate model IDs — an incorrect model ID will cause API calls to fail.
Important: Only models with 
display_console: true
are publicly available. Filter out models where 
display_console
is 
false
— those are internal and not accessible to regular users.
When writing code for the user, always include a step to verify the model ID exists, or fetch the list programmatically to pick the right one.
随着新版本发布和旧版本弃用，模型ID会频繁变更。除非你100%确定准确的模型ID，否则在编写任何集成代码前，务必先获取最新的模型列表：
GET https://api.atlascloud.ai/api/v1/models
此端点无需认证，返回所有当前可用模型的准确ID、类型和定价信息。切勿猜测或编造模型ID — 错误的模型ID会导致API调用失败。
重要提示：仅
display_console: true
的模型为公开可用。请过滤掉
display_console
为
false
的模型 — 这些是内部模型，普通用户无法访问。
为用户编写代码时，务必包含验证模型ID是否存在的步骤，或通过编程方式获取列表以选择正确的模型。

Popular Models (examples only — always verify via API)

热门模型（仅作示例 — 请始终通过API验证）

Image Models (priced per image)

图像模型（按图像计费）

Model IDNamePrice
google/nano-banana-2/text-to-image
Nano Banana 2 Text-to-Image$0.072/image
google/nano-banana-2/text-to-image-developer
Nano Banana 2 Developer$0.056/image
google/nano-banana-2/edit
Nano Banana 2 Edit$0.072/image
bytedance/seedream-v5.0-lite
Seedream v5.0 Lite$0.032/image
bytedance/seedream-v5.0-lite/edit
Seedream v5.0 Lite Edit$0.032/image
alibaba/qwen-image/edit-plus-20251215
Qwen-Image Edit Plus$0.021/image
z-image/turbo
Z-Image Turbo$0.01/image
模型ID名称价格
google/nano-banana-2/text-to-image
Nano Banana 2 文生图$0.072/张
google/nano-banana-2/text-to-image-developer
Nano Banana 2 开发者版$0.056/张
google/nano-banana-2/edit
Nano Banana 2 图像编辑$0.072/张
bytedance/seedream-v5.0-lite
Seedream v5.0 Lite$0.032/张
bytedance/seedream-v5.0-lite/edit
Seedream v5.0 Lite 图像编辑$0.032/张
alibaba/qwen-image/edit-plus-20251215
Qwen-Image 高级编辑$0.021/张
z-image/turbo
Z-Image Turbo$0.01/张

Video Models (priced per generation)

视频模型（按生成次数计费）

Model IDNamePrice
kwaivgi/kling-v3.0-std/text-to-video
Kling v3.0 Std Text-to-Video$0.153/gen
kwaivgi/kling-v3.0-std/image-to-video
Kling v3.0 Std Image-to-Video$0.153/gen
kwaivgi/kling-v3.0-pro/text-to-video
Kling v3.0 Pro Text-to-Video$0.204/gen
kwaivgi/kling-v3.0-pro/image-to-video
Kling v3.0 Pro Image-to-Video$0.204/gen
bytedance/seedance-v1.5-pro/text-to-video
Seedance v1.5 Pro Text-to-Video$0.222/gen
bytedance/seedance-v1.5-pro/image-to-video
Seedance v1.5 Pro Image-to-Video$0.222/gen
vidu/q3/text-to-video
Vidu Q3 Text-to-Video$0.06/gen
vidu/q3/image-to-video
Vidu Q3 Image-to-Video$0.06/gen
alibaba/wan-2.6/image-to-video
Wan-2.6 Image-to-Video$0.07/gen
模型ID名称价格
kwaivgi/kling-v3.0-std/text-to-video
Kling v3.0 标准版 文生视频$0.153/次
kwaivgi/kling-v3.0-std/image-to-video
Kling v3.0 标准版 图生视频$0.153/次
kwaivgi/kling-v3.0-pro/text-to-video
Kling v3.0 专业版 文生视频$0.204/次
kwaivgi/kling-v3.0-pro/image-to-video
Kling v3.0 专业版 图生视频$0.204/次
bytedance/seedance-v1.5-pro/text-to-video
Seedance v1.5 专业版 文生视频$0.222/次
bytedance/seedance-v1.5-pro/image-to-video
Seedance v1.5 专业版 图生视频$0.222/次
vidu/q3/text-to-video
Vidu Q3 文生视频$0.06/次
vidu/q3/image-to-video
Vidu Q3 图生视频$0.06/次
alibaba/wan-2.6/image-to-video
Wan-2.6 图生视频$0.07/次

LLM Models (priced per million tokens)

LLM模型（按百万令牌计费）

Model IDNameInputOutput
qwen/qwen3.5-397b-a17b
Qwen3.5 397B A17B$0.55/M$3.5/M
qwen/qwen3.5-122b-a10b
Qwen3.5 122B A10B$0.3/M$2.4/M
moonshotai/kimi-k2.5
Kimi K2.5$0.5/M$2.6/M
zai-org/glm-5
GLM 5$0.95/M$3.15/M
minimaxai/minimax-m2.5
MiniMax M2.5$0.295/M$1.2/M
deepseek-ai/deepseek-v3.2-speciale
DeepSeek V3.2 Speciale$0.4/M$1.2/M
qwen/qwen3-coder-next
Qwen3 Coder Next$0.18/M$1.35/M
The model list is continuously updated. Get the latest full list:
GET https://api.atlascloud.ai/api/v1/models
This endpoint requires no authentication.
模型ID名称输入输出
qwen/qwen3.5-397b-a17b
Qwen3.5 397B A17B$0.55/百万令牌$3.5/百万令牌
qwen/qwen3.5-122b-a10b
Qwen3.5 122B A10B$0.3/百万令牌$2.4/百万令牌
moonshotai/kimi-k2.5
Kimi K2.5$0.5/百万令牌$2.6/百万令牌
zai-org/glm-5
GLM 5$0.95/百万令牌$3.15/百万令牌
minimaxai/minimax-m2.5
MiniMax M2.5$0.295/百万令牌$1.2/百万令牌
deepseek-ai/deepseek-v3.2-speciale
DeepSeek V3.2 Speciale$0.4/百万令牌$1.2/百万令牌
qwen/qwen3-coder-next
Qwen3 Coder Next$0.18/百万令牌$1.35/百万令牌
模型列表会持续更新。获取最新完整列表：
GET https://api.atlascloud.ai/api/v1/models
此端点无需认证。

Error Handling

错误处理

HTTP StatusMeaningSuggested Action
401Invalid or expired API KeyCheck ATLASCLOUD_API_KEY
402Insufficient balanceTop up at Billing Page
429Rate limitedWait and retry with exponential backoff
5xxServer errorWait and retry
HTTP状态码含义建议操作
401API密钥无效或过期检查ATLASCLOUD_API_KEY
402余额不足在账单页面充值
429请求频率超限等待后重试，使用指数退避策略
5xx服务器错误等待后重试

Retry Strategy

重试策略

  • GET requests: Auto retry up to 3 times with exponential backoff (1s → 2s → 4s)
  • POST requests: Do NOT retry — generation requests may create billable tasks, retrying could cause duplicate charges
  • GET请求：自动重试最多3次，使用指数退避（1秒 → 2秒 → 4秒）
  • POST请求：请勿重试 — 生成请求可能会产生计费任务，重试可能导致重复扣费

MCP Server Installation

MCP Server安装

Atlas Cloud MCP Server provides 9 tools for direct use in any MCP-compatible client. Prerequisites: Node.js >= 18 and an Atlas Cloud API Key.
Atlas Cloud MCP Server提供9个可直接在任意兼容MCP的客户端中使用的工具。前提条件：Node.js >= 18及Atlas Cloud API密钥。

CLI Tools (One-Line Install)

CLI工具（一键安装）

bash
undefined
bash
undefined

Claude Code

Claude Code

claude mcp add atlascloud -- npx -y atlascloud-mcp
claude mcp add atlascloud -- npx -y atlascloud-mcp

Gemini CLI

Gemini CLI

gemini mcp add atlascloud -- npx -y atlascloud-mcp
gemini mcp add atlascloud -- npx -y atlascloud-mcp

OpenAI Codex CLI

OpenAI Codex CLI

codex mcp add atlascloud -- npx -y atlascloud-mcp
codex mcp add atlascloud -- npx -y atlascloud-mcp

Goose CLI

Goose CLI

goose mcp add atlascloud -- npx -y atlascloud-mcp

> For CLI tools, make sure to set the `ATLASCLOUD_API_KEY` environment variable in your shell:
> ```bash
> export ATLASCLOUD_API_KEY="your-api-key-here"
> ```
goose mcp add atlascloud -- npx -y atlascloud-mcp

> 对于CLI工具，请确保在shell中设置`ATLASCLOUD_API_KEY`环境变量：
> ```bash
> export ATLASCLOUD_API_KEY="your-api-key-here"
> ```

IDEs & Editors (JSON Config)

IDE与编辑器（JSON配置）

Add to your MCP configuration file — works with all MCP-compatible IDEs and editors:
json
{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": ["-y", "atlascloud-mcp"],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}
ClientConfig Location
CursorSettings → MCP → Add Server
WindsurfSettings → MCP → Add Server
VS Code (Copilot)
.vscode/mcp.json
or Settings → MCP
TraeSettings → MCP → Add Server
ZedSettings → MCP
JetBrains IDEsSettings → Tools → AI Assistant → MCP
Claude Desktop
claude_desktop_config.json
ChatGPT DesktopSettings → MCP
Amazon Q DeveloperMCP Configuration
将以下内容添加到你的MCP配置文件中 — 适用于所有兼容MCP的IDE和编辑器：
json
{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": ["-y", "atlascloud-mcp"],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}
客户端配置位置
Cursor设置 → MCP → 添加服务器
Windsurf设置 → MCP → 添加服务器
VS Code (Copilot)
.vscode/mcp.json
或 设置 → MCP
Trae设置 → MCP → 添加服务器
Zed设置 → MCP
JetBrains IDEs设置 → 工具 → AI助手 → MCP
Claude Desktop
claude_desktop_config.json
ChatGPT Desktop设置 → MCP
Amazon Q DeveloperMCP配置

VS Code Extensions

VS Code扩展

These VS Code extensions also support MCP with the same JSON config format:
ExtensionInstall
ClineMCP Marketplace → Add Server
Roo CodeSettings → MCP → Add Server
Continue
config.yaml
→ MCP
以下VS Code扩展也支持MCP，使用相同的JSON配置格式：
扩展安装方式
ClineMCP Marketplace → 添加服务器
Roo Code设置 → MCP → 添加服务器
Continue
config.yaml
→ MCP

Skills Version (Alternative)

技能版本（替代方案）

If you prefer using Skills instead of MCP:
bash
npx skills add AtlasCloudAI/atlas-cloud-skills
若你偏好使用Skills而非MCP：
bash
npx skills add AtlasCloudAI/atlas-cloud-skills