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 開發。Atlas Cloud（由 Atlas Cloud AI LLC 營運）僅提供接取服務，並不擁有該模型。所有商標均歸其各自所有者所有。

參數

程式碼範例
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"])

安裝

安裝所需的相依套件。

pip install requests

驗證

所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。

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

HTTP 標頭

import os

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

保護好您的 API Key

切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使用環境變數或後端代理。

提交請求

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

以下參數在請求主體中被接受。

總計: 3必填: 1選填: 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

範例請求主體

{
  "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 傳回相容 ChatCompletion 的回應格式。

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.

範例回應

{
  "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 將 400+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API Key

從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。

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

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案用於圖片編輯和圖生影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 400+ AI 模型連接。支援任何相容 MCP 的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

npx -y atlascloud-mcp

設定

將以下設定新增到您的 IDE 的 MCP 設定檔中。

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

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 400+ 可用 AI 模型。

atlas_quick_generate一步式內容創建，自動選擇最佳模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

了解更多

github.com/AtlasCloudAI/mcp-server

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