Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.xingchaoyiqing.com/llms.txt

Use this file to discover all available pages before exploring further.

The Gemini Native API lets you interact with Gemini models using Google’s native request format, including the contents and generationConfig structure. All Gemini models accessible through this endpoint support both image and video analysis, making it the right choice whenever you need multimodal capabilities with the full flexibility of the native Gemini protocol. Base URL: http://apillm.globalaiopc.com/gw_llm_power Endpoints:
  • POST /v1/models/{model}:generateContent — Standard (non-streaming) response
  • POST /v1/models/{model}:streamGenerateContent — Streaming response

​
Authentication

Authenticate every request using the Authorization header with your API key: 
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

​
Supported Models

All Gemini models available through this endpoint support image and video analysis.
ModelVariants
gemini-2.5-flash-liteStandard only
gemini-2.5-proStandard, -official, -low
gemini-3-flash-previewStandard, -official, -low
gemini-3.1-flash-lite-previewStandard only
gemini-3.1-pro-previewStandard, -official, -low

​
Model Suffix Reference

SuffixDescription
(none)Standard / stable version
-officialOfficial version
-lowBudget version

​
Request Parameters

​
contents
array
required
The array of message content objects that make up the conversation. Each object typically contains a role and a parts array.
​
contents[].role
string
The role of the message author. Use user for human turns and model for prior model turns in multi-turn conversations.
​
contents[].parts
array
required
An array of content parts for the message. Typically contains text objects for plain text input. The native Gemini multimodal structure (inline images, video, etc.) is also supported.
​
contents[].parts[].text
string
The text content of the part.
​
generationConfig.temperature
number
Controls the randomness of the model’s output. Lower values produce more focused, deterministic responses; higher values produce more creative output.
​
generationConfig.topP
number
Nucleus sampling parameter. The model considers only the tokens comprising the top topP probability mass.
​
generationConfig.maxOutputTokens
integer
The maximum number of tokens the model may generate in its response.
​
systemInstruction.parts[].text
string
An optional system prompt that sets the context and behavior for the model. Provide this as a text part within the systemInstruction object.

​
Response Fields

​
candidates[].content.parts[].text
string
The text content generated by the model.
​
candidates[].finishReason
string
The reason the model stopped generating. Common values include STOP (natural end) and MAX_TOKENS (token limit reached).
​
usageMetadata.promptTokenCount
integer
The number of tokens in the input contents and system instruction.
​
usageMetadata.candidatesTokenCount
integer
The number of tokens in the generated response candidates.
​
usageMetadata.totalTokenCount
integer
The total number of tokens used in the request (prompt + candidates).

​
Code Examples

curl -X POST "http://apillm.globalaiopc.com/gw_llm_power/v1/models/gemini-3.1-pro-preview:generateContent" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [{"text": "Explain the history of artificial intelligence"}]
      }
    ],
    "generationConfig": {
      "temperature": 0.7,
      "maxOutputTokens": 1024
    }
  }'

​
Example Response

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "The history of artificial intelligence spans decades, progressing through three major phases: early rule-based systems in the 1950s–1980s, the rise of machine learning in the 1990s–2010s, and the current era of large language models and deep learning."
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 52,
    "totalTokenCount": 62
  }
}
To receive a streaming response, replace :generateContent with :streamGenerateContent in the request URL. The API will return a series of incremental response chunks in the native Gemini streaming format.
This endpoint follows the native Google Gemini protocol. For complete details on multimodal input, function calling, safety settings, and advanced generation configuration, refer to the Google Gemini generateContent documentation.