Native Gemini Format

curl --request POST \
  --url https://api.gravitex.ai/v1beta/models/{model}:generateContent \
  --header 'Content-Type: application/json' \
  --data '
{
  "contents": [
    {}
  ],
  "generationConfig": {},
  "systemInstruction": {},
  "safetySettings": [
    {}
  ],
  "tools": [
    {}
  ],
  "toolConfig": {},
  "cachedContent": "<string>"
}
'

import requests

url = "https://api.gravitex.ai/v1beta/models/{model}:generateContent"

payload = {
    "contents": [{}],
    "generationConfig": {},
    "systemInstruction": {},
    "safetySettings": [{}],
    "tools": [{}],
    "toolConfig": {},
    "cachedContent": "<string>"
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    contents: [{}],
    generationConfig: {},
    systemInstruction: {},
    safetySettings: [{}],
    tools: [{}],
    toolConfig: {},
    cachedContent: '<string>'
  })
};

fetch('https://api.gravitex.ai/v1beta/models/{model}:generateContent', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.gravitex.ai/v1beta/models/{model}:generateContent",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'contents' => [
        [
                
        ]
    ],
    'generationConfig' => [
        
    ],
    'systemInstruction' => [
        
    ],
    'safetySettings' => [
        [
                
        ]
    ],
    'tools' => [
        [
                
        ]
    ],
    'toolConfig' => [
        
    ],
    'cachedContent' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.gravitex.ai/v1beta/models/{model}:generateContent"

	payload := strings.NewReader("{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.gravitex.ai/v1beta/models/{model}:generateContent")
  .header("Content-Type", "application/json")
  .body("{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.gravitex.ai/v1beta/models/{model}:generateContent")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "candidates": [
    {
      "content": {
        "parts": [{"text": "Response text"}],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0,
      "safetyRatings": []
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 20,
    "totalTokenCount": 30,
    "thoughtsTokenCount": 0,
    "cachedContentTokenCount": 0
  },
  "modelVersion": "gemini-2.5-pro",
  "createTime": "2025-01-01T00:00:00Z"
}

POST

v1beta

models

{model}

:generateContent

Native Gemini Format

curl --request POST \
  --url https://api.gravitex.ai/v1beta/models/{model}:generateContent \
  --header 'Content-Type: application/json' \
  --data '
{
  "contents": [
    {}
  ],
  "generationConfig": {},
  "systemInstruction": {},
  "safetySettings": [
    {}
  ],
  "tools": [
    {}
  ],
  "toolConfig": {},
  "cachedContent": "<string>"
}
'

import requests

url = "https://api.gravitex.ai/v1beta/models/{model}:generateContent"

payload = {
    "contents": [{}],
    "generationConfig": {},
    "systemInstruction": {},
    "safetySettings": [{}],
    "tools": [{}],
    "toolConfig": {},
    "cachedContent": "<string>"
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    contents: [{}],
    generationConfig: {},
    systemInstruction: {},
    safetySettings: [{}],
    tools: [{}],
    toolConfig: {},
    cachedContent: '<string>'
  })
};

fetch('https://api.gravitex.ai/v1beta/models/{model}:generateContent', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.gravitex.ai/v1beta/models/{model}:generateContent",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'contents' => [
        [
                
        ]
    ],
    'generationConfig' => [
        
    ],
    'systemInstruction' => [
        
    ],
    'safetySettings' => [
        [
                
        ]
    ],
    'tools' => [
        [
                
        ]
    ],
    'toolConfig' => [
        
    ],
    'cachedContent' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.gravitex.ai/v1beta/models/{model}:generateContent"

	payload := strings.NewReader("{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.gravitex.ai/v1beta/models/{model}:generateContent")
  .header("Content-Type", "application/json")
  .body("{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.gravitex.ai/v1beta/models/{model}:generateContent")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"contents\": [\n    {}\n  ],\n  \"generationConfig\": {},\n  \"systemInstruction\": {},\n  \"safetySettings\": [\n    {}\n  ],\n  \"tools\": [\n    {}\n  ],\n  \"toolConfig\": {},\n  \"cachedContent\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "candidates": [
    {
      "content": {
        "parts": [{"text": "Response text"}],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0,
      "safetyRatings": []
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 20,
    "totalTokenCount": 30,
    "thoughtsTokenCount": 0,
    "cachedContentTokenCount": 0
  },
  "modelVersion": "gemini-2.5-pro",
  "createTime": "2025-01-01T00:00:00Z"
}

Introduction

The Gemini Native API uses Google Gemini’s request and response format. It is suitable for Google official clients (e.g. the google-generativeai SDK) or when you need to work directly with Gemini data structures. The API follows the Gemini specification and supports thinking mode, multimodal input, tool calling, Google Search (Grounding), context caching, image generation, and other full capabilities.

If you use an OpenAI-compatible client (e.g. OpenAI SDK) for Gemini, see Gemini OpenAI format (Chat). For other models, use OpenAI Chat Completions.

Difference from OpenAI format

Aspect	Gemini Native	OpenAI-compatible (/v1/chat/completions)
Message structure	`contents[].parts[]` (text / inlineData / fileData)	`messages[].content`
Roles	`user` / `model`	`user` / `assistant` / `system`
System prompt	`systemInstruction.parts`	`messages` with role=system
Streaming	`streamGenerateContent?alt=sse`	`stream: true`
Thinking mode	`generationConfig.thinkingConfig` or model suffix	Model suffix (e.g. `-thinking`)

API endpoints

Feature	Method	Path
Text generation (non-streaming)	POST	`/v1beta/models/{model}:generateContent`
Text generation (streaming)	POST	`/v1beta/models/{model}:streamGenerateContent?alt=sse`
Single Embedding	POST	`/v1beta/models/{model}:embedContent`
Batch Embedding	POST	`/v1beta/models/{model}:batchEmbedContents`

Replace {model} in the path with the actual model ID, e.g. gemini-2.5-pro, gemini-3-pro-preview.

Authentication

Any of the following is supported:

string

Bearer token: Bearer sk-xxxxxxxxxx (recommended, consistent with other GravitexAI endpoints)

string

Google-style API key: x-goog-api-key: sk-xxxxxxxxxx

You can also pass the key in the URL: ?key=sk-xxxxxxxxxx.

Request parameters

generateContent / streamGenerateContent

array

required

List of conversation contents. Each item has role (user or model) and parts. Each part can be: {"text": "..."}, {"inlineData": {"mimeType": "...", "data": "base64..."}}, or {"fileData": {"mimeType": "...", "fileUri": "gs://..."}}.

object

Generation config.

temperature: 0–2, randomness
topP: nucleus sampling
topK: top-K sampling
maxOutputTokens: max output tokens
stopSequences: stop sequences
responseMimeType: e.g. text/plain
responseModalities: e.g. ["TEXT"] or ["IMAGE"]
thinkingConfig: thinking mode (see below)
imageConfig: image generation config (see below)

object

System instruction: {"parts": [{"text": "..."}]}.

array

Safety levels, e.g. [{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"}].

array

Tool declarations (function calling), see advanced features.

object

Tool config, e.g. functionCallingConfig.mode: AUTO / ANY / NONE.

string

Context caching ID returned by the API; used to reuse cached context.

Response format

Non-streaming generateContent returns JSON:

{
  "candidates": [
    {
      "content": {
        "parts": [{"text": "Response text"}],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0,
      "safetyRatings": []
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 10,
    "candidatesTokenCount": 20,
    "totalTokenCount": 30,
    "thoughtsTokenCount": 0,
    "cachedContentTokenCount": 0
  },
  "modelVersion": "gemini-2.5-pro",
  "createTime": "2025-01-01T00:00:00Z"
}

The streaming endpoint returns SSE; each line starts with data: and contains a JSON fragment (e.g. candidates[].content.parts).

Basic examples

cURL (non-streaming)
cURL (streaming)
Python (google-generativeai)
Node.js

curl -X POST "https://api.gravitex.ai/v1beta/models/gemini-2.5-pro:generateContent" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "Describe AI in one sentence"}]}
    ],
    "generationConfig": {
      "temperature": 0.7,
      "maxOutputTokens": 1024
    }
  }'

curl -N -X POST "https://api.gravitex.ai/v1beta/models/gemini-2.5-pro:streamGenerateContent?alt=sse" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "Describe AI in one sentence"}]}
    ],
    "generationConfig": {"maxOutputTokens": 1024}
  }'

import google.generativeai as genai

genai.configure(
    api_key="sk-xxxxxxxxxx",
    transport="rest",
    client_options={"api_endpoint": "https://api.gravitex.ai"}
)

model = genai.GenerativeModel("gemini-2.5-pro")
response = model.generate_content("Describe AI in one sentence")
print(response.text)

const { GoogleGenerativeAI } = require("@google/generative-ai");

const genAI = new GoogleGenerativeAI("sk-xxxxxxxxxx");
genAI.apiKey = "sk-xxxxxxxxxx";
// If the SDK supports a custom baseUrl, set it to https://api.gravitex.ai
const model = genAI.getGenerativeModel({ model: "gemini-2.5-pro" });

const result = await model.generateContent("Describe AI in one sentence");
const text = result.response.text();
console.log(text);

By default, google-generativeai calls Google’s API. To use GravitexAI, set api_endpoint to https://api.gravitex.ai via client_options or environment variables. See your SDK docs for details.

Advanced features

Thinking mode

Supported in three ways:

generationConfig.thinkingConfig (Gemini 2.5 Pro): use thinkingBudget (token count)
thinkingConfig.thinkingLevel (Gemini 3 Pro): use LOW / HIGH
Model suffix: -thinking, -thinking-8192, -nothinking, -thinking-low, -thinking-high

thinkingBudget (2.5 Pro)
thinkingLevel (3 Pro)

{
  "contents": [{"role": "user", "parts": [{"text": "Give a geometry problem and solve it step by step"}]}],
  "generationConfig": {
    "maxOutputTokens": 8192,
    "thinkingConfig": {
      "includeThoughts": true,
      "thinkingBudget": 8192
    }
  }
}

{
  "contents": [{"role": "user", "parts": [{"text": "Give a geometry problem and solve it step by step"}]}],
  "generationConfig": {
    "maxOutputTokens": 8192,
    "thinkingConfig": {
      "includeThoughts": true,
      "thinkingLevel": "HIGH"
    }
  }
}

Multimodal input

Mix text and media in contents[].parts:

{
  "contents": [
    {
      "role": "user",
      "parts": [
        {"text": "Describe this image"},
        {
          "inlineData": {
            "mimeType": "image/jpeg",
            "data": "/9j/4AAQSkZJRg..."
          }
        }
      ]
    }
  ]
}

Image: inlineData with base64 data, or fileData with fileUri (e.g. gs://...)
Audio: inlineData with mimeType such as audio/mp3

Tool calling (Function Calling)

{
  "contents": [{"role": "user", "parts": [{"text": "What is the weather in Shanghai today?"}]}],
  "tools": [
    {
      "functionDeclarations": [
        {
          "name": "get_weather",
          "description": "Get weather for a city",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string"}
            },
            "required": ["location"]
          }
        }
      ]
    }
  ],
  "toolConfig": {
    "functionCallingConfig": {
      "mode": "AUTO",
      "allowedFunctionNames": []
    }
  }
}

The model may return a functionCall part; include the corresponding functionResponse in the next contents and send another request.

Google Search (Grounding)

When enabled, the model can use real-time web search to improve answers (e.g. weather, news). Add googleSearch to tools:

{
  "contents": [{"role": "user", "parts": [{"text": "What is the weather in Beijing today?"}]}],
  "tools": [
    {
      "googleSearch": {}
    }
  ],
  "toolConfig": {
    "functionCallingConfig": {
      "mode": "AUTO"
    }
  }
}

To use both function calling and Google Search, include googleSearch: {} and functionDeclarations as separate elements in the same tools array. Responses may include retrieval metadata (e.g. groundingMetadata).

Streaming

Use: POST /v1beta/models/{model}:streamGenerateContent?alt=sse. Request body is the same as generateContent. Response is SSE; each data: line is a JSON chunk.

Context caching

First request does not include cachedContent. If the server returns a cache ID, subsequent requests can send:

{
  "cachedContent": "cached-content-id",
  "contents": [{"role": "user", "parts": [{"text": "Continue from the context above"}]}]
}

This reduces cost and latency for long repeated context.

Image generation (e.g. Gemini 2.5 Flash)

When the model supports image output, set in generationConfig:

{
  "contents": [{"role": "user", "parts": [{"text": "Draw a cat"}]}],
  "generationConfig": {
    "responseModalities": ["IMAGE"],
    "imageConfig": {
      "aspectRatio": "1:1",
      "imageSize": "1K",
      "imageOutputOptions": {"mimeType": "image/png"}
    }
  }
}

Response candidates[].content.parts may include inlineData (e.g. base64 image).

Embedding API

Single: embedContent

Endpoint: POST https://api.gravitex.ai/v1beta/models/{model}:embedContent Request body example:

{
  "model": "text-embedding-004",
  "content": {
    "parts": [{"text": "Text to embed"}]
  }
}

Or put model in the path: /v1beta/models/text-embedding-004:embedContent, with body containing only content.

Batch: batchEmbedContents

Endpoint: POST https://api.gravitex.ai/v1beta/models/{model}:batchEmbedContents Request body example:

{
  "requests": [
    {"content": {"parts": [{"text": "First text"}]}},
    {"content": {"parts": [{"text": "Second text"}]}}
  ]
}

Response is an array, one embedding per request.

Error handling

Errors are returned as HTTP status codes and JSON body, for example:

{
  "error": {
    "code": 400,
    "message": "Invalid request: ...",
    "status": "INVALID_ARGUMENT"
  }
}

Common cases:

Status	Meaning
400	Invalid request (e.g. missing `contents`, unsupported parameter)
401	Authentication failed (invalid or missing API key)
404	Model not found or wrong path
429	Rate limited; retry later
500	Server error

Parse error.message in your client and handle retries or user messaging accordingly.

Comparison with OpenAI format

Item	Gemini Native	OpenAI (/v1/chat/completions)
Base path	`/v1beta/models/{model}:generateContent`	`/v1/chat/completions`
Auth	`Authorization: Bearer sk-xxx` or `x-goog-api-key`	`Authorization: Bearer sk-xxx`
Message format	`contents[].parts[]` (text/inlineData/fileData)	`messages[].content` (string or array)
System prompt	`systemInstruction.parts`	`messages` with `role: "system"`
Streaming	`streamGenerateContent?alt=sse`	`stream: true`
Thinking	`thinkingConfig` or model suffix	Model suffix (e.g. `-thinking`)
Tools	`tools[].functionDeclarations`	`tools[].function` (OpenAI shape)
Typical clients	Google SDK, custom HTTP client	OpenAI SDK, OpenAI-compatible clients

Use the native endpoint when you rely on Google Gemini tooling or need Gemini-specific fields (e.g. thinkingConfig, native multimodal parts). Use /v1/chat/completions when you want to stay within the OpenAI ecosystem.

Native Claude Format Native OpenAI Format (Responses)

API documentation

Chat & text

Safety & audio

Image Series

Video Series

Completions & Embeddings

Interface module

Native Gemini Format

Introduction

Difference from OpenAI format

API endpoints

Authentication

Request parameters

generateContent / streamGenerateContent

Response format

Basic examples

Advanced features

Thinking mode

Multimodal input

Tool calling (Function Calling)

Google Search (Grounding)

Streaming

Context caching

Image generation (e.g. Gemini 2.5 Flash)

Embedding API

Single: embedContent

Batch: batchEmbedContents

Error handling

Comparison with OpenAI format

​Introduction

​Difference from OpenAI format

​API endpoints

​Authentication

​Request parameters

​generateContent / streamGenerateContent

​Response format

​Basic examples

​Advanced features

​Thinking mode

​Multimodal input

​Tool calling (Function Calling)

​Google Search (Grounding)

​Streaming

​Context caching

​Image generation (e.g. Gemini 2.5 Flash)

​Embedding API

​Single: embedContent

​Batch: batchEmbedContents

​Error handling

​Comparison with OpenAI format

Introduction

Difference from OpenAI format

API endpoints

Authentication

Request parameters

generateContent / streamGenerateContent

Response format

Basic examples

Advanced features

Thinking mode

Multimodal input

Tool calling (Function Calling)

Google Search (Grounding)

Streaming

Context caching

Image generation (e.g. Gemini 2.5 Flash)

Embedding API

Single: embedContent

Batch: batchEmbedContents

Error handling

Comparison with OpenAI format