google/gemini-3.5-flash

LLM

Gemini 3.5 Flash API by Google

google/gemini-3.5-flash

Gemini-3.5-flash

Fast and cost-efficient multimodal model designed for high-throughput applications, real-time interactions, and everyday AI tasks.

Gemini 3.5 Flash 由 Google 开发。Atlas Cloud（由 Atlas Cloud AI LLC 运营）仅提供接入服务，并不拥有该模型。所有商标均归其各自所有者所有。

参数

代码示例
import os
import requests

# Multimodal Understanding Example
# Image: inline_data with base64 bytes, or file_data with a public URL
# Video: file_data with a public URL (recommended for large files)

url = "https://api.atlascloud.ai/v1/models/google/gemini-3.5-flash:generateContent"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.environ['ATLASCLOUD_API_KEY']}"
}
payload = {
    "contents": [
        {
            "role": "user",
            "parts": [
                {
                    "inline_data": {
                        "mime_type": "image/png",
                        "data": "<BASE64_IMAGE_DATA>"
                    }
                },
                {
                    "file_data": {
                        "mime_type": "video/mp4",
                        "file_uri": "https://example.com/your-video.mp4"
                    }
                },
                {
                    "text": "Please describe the content of this image/video"
                }
            ]
        }
    ],
    "generationConfig": {
        "maxOutputTokens": 32768,
        "temperature": 0.7
    }
}

response = requests.post(url, headers=headers, json=payload)
print(response.json()["candidates"][0]["content"]["parts"][0]["text"])

安装

安装所需的依赖包。

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

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

HTTP 请求头

import os

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

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

import requests

url = "https://api.atlascloud.ai/v1/models/google/gemini-3.5-flash:generateContent"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "contents": [
        {
            "role": "user",
            "parts": [
                {
                    "text": "what is difference between http and https"
                }
            ]
        }
    ],
    "generationConfig": {
        "maxOutputTokens": 32768,
        "temperature": 0.7
    }
}

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

Input Schema

以下参数在请求体中被接受。

总计: 3必填: 1可选: 2

contentsarray[object]required

The conversation so far. Note: the model ID goes in the URL path, not in the body.

rolestringrequired

Who produced this turn. Use "user" for input and "model" for previous replies.

usermodel

partsarray[object]required

Ordered content parts. Combine a media part with a text part to ask a question about the media. Accepts image (png, jpeg, webp, gif), video (mp4, mpeg, mov, avi, flv, mpg, webm, wmv, 3gp).

systemInstructionobject

System-level instruction. Gemini keeps this outside contents, unlike the OpenAI system role.

partsarray[object]required

Instruction text, wrapped in the same part shape.

generationConfigobject

Sampling options. Gemini nests these instead of putting them at the top level.

maxOutputTokensinteger

The maximum number of tokens to generate in the completion. The maximum value is the model's context length minus the prompt length.

Min: 1

temperaturenumber

Controls the variety of the model's responses. Lower values make output more predictable and deterministic, higher values more diverse and random.

Default: 1Min: 0Max: 2

请求体示例

{
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "inline_data": {
            "mime_type": "image/png",
            "data": "<BASE64_IMAGE_DATA>"
          }
        },
        {
          "file_data": {
            "mime_type": "video/mp4",
            "file_uri": "https://example.com/your-video.mp4"
          }
        },
        {
          "text": "Please describe the content of this image/video"
        }
      ]
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 32768,
    "temperature": 0.7
  }
}

Output Schema

API 返回兼容 ChatCompletion 的响应格式。

idstringrequired

Unique identifier for the completion.

objectstringrequired

Object type, always "chat.completion".

Default: "chat.completion"

createdintegerrequired

Unix timestamp of when the completion was created.

modelstringrequired

The model used for the completion.

choicesarray[object]required

List of completion choices.

indexintegerrequired

Index of the choice.

messageobjectrequired

The generated message.

finish_reasonstringrequired

The reason generation stopped.

stoplengthcontent_filter

usageobjectrequired

Token usage statistics.

prompt_tokensintegerrequired

Number of tokens in the prompt.

completion_tokensintegerrequired

Number of tokens in the completion.

total_tokensintegerrequired

Total tokens used.

响应示例

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "model-name",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 20,
    "total_tokens": 30
  }
}

Atlas Cloud Skills

Atlas Cloud Skills 将 400+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

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

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 400+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

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

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 400+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

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