How to Use Gemini 3.1 Pro API?

Getting Started with Gemini 3.1 Pro API: A Developer's Guide

Google recently released Gemini 3.1 Pro, its most capable model yet, offering advanced reasoning, multimodal understanding, and agentic workflows. Developers can access this preview model via the Gemini API, enabling state-of-the-art performance with support for up to 1 million input tokens and 64k output tokens, all while maintaining low latency for production systems.

Try Apidog today

To streamline testing your Gemini 3.1 Pro API integrations, tools like Apidog can simplify the process. Apidog lets you visually construct requests, upload images or PDFs for multimodal testing, inspect streamed responses, and automatically generate SDK code in multiple languages. Its intuitive interface reduces debugging time by handling authentication headers, JSON schemas, and file encoding seamlessly.

Understanding Gemini 3.1 Pro: Capabilities That Redefine AI Integration

Gemini 3.1 Pro introduces native dynamic thinking, improved tool use, and superior multimodal fusion. It processes text, high-resolution images, video frames, PDFs up to 1000 pages, and code simultaneously within the same context window, enabling coherent multi-step reasoning without extensive prompt engineering.

Additionally, the model introduces the thinking_level configuration:
-[ high for deep analysis tasks

• low for high-throughput scenarios

The default high level activates internal chain-of-thought mechanisms automatically, reducing the need for explicit reasoning instructions.

Thought signatures are another powerful feature. These encrypted strings maintain conversation state across turns, especially when combining function calling with image generation or editing. Including the exact thoughtSignature in subsequent requests ensures deterministic behavior in long-running agent loops.

The knowledge cutoff is January 2025. Pairing the model with the built-in Google Search tool allows for grounded, up-to-date responses without manual retrieval-augmented generation pipelines.

Prerequisites for Working with the Gemini 3.1 Pro API

Before writing any code, ensure your environment is ready:

1. Have a Google account with access to Google AI Studio.
2. Enable billing on the associated Google Cloud project (preview models enforce strict rate limits on free tiers).
3. Install Python 3.9+ or Node.js 18+, depending on your preferred stack.

Allocate storage for large multimodal payloads, as video files and high-resolution images consume tokens quickly. Monitor usage through the AI Studio dashboard to avoid quota errors.

Obtaining and Securing Your Gemini API Key

Navigate to Google AI Studio, click "Get API key," and copy the newly created key immediately. Store it as an environment variable to keep credentials out of source code.

Linux/macOS:

export GEMINI_API_KEY=your_actual_key_here

Windows:

set GEMINI_API_KEY=your_actual_key_here

For production deployments, rotate keys regularly and restrict them through Google Cloud IAM policies.

Installing the Official Google GenAI SDK

The SDK abstracts HTTP details and provides type-safe interfaces. Install it with:

Python:

pip install -U google-genai

Node.js:

npm install @google/genai

The package automatically reads GEMINI_API_KEY from the environment, but you can also pass the key during client instantiation for explicit configuration.

Making Your First Call to the Gemini 3.1 Pro API

Initialize the client and send a simple text prompt to verify connectivity.

Python Example:

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Explain the differences between Gemini 3.1 Pro and previous models in technical terms.",
    config=types.GenerateContentConfig(
        thinking_level="high"
    )
)

print(response.text)

cURL Equivalent (Useful for Apidog Testing):

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-pro-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{"text": "Explain the differences between Gemini 3.1 Pro and previous models in technical terms."}]
    }],
    "generationConfig": {
      "thinking_level": "high"
    }
  }'

JavaScript Example:

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-3.1-pro-preview",
    contents: "Explain the differences between Gemini 3.1 Pro and previous models in technical terms.",
    config: { thinking_level: "high" }
  });
  console.log(response.text);
}

main();

Exploring Core Endpoints and Request Anatomy

The Gemini API revolves around three primary methods:

• generateContent: Synchronous responses
• streamGenerateContent: Partial output streaming
• countTokens: Token estimation

The request body follows a consistent structure:

• contents: Array of role-based messages (user/model/function)
• tools: Array of Google Search, code_execution, or custom function declarations
• generationConfig: Controls thinking_level, temperature, maxOutputTokens, etc.
• safetySettings: Optional content filter overrides

Custom functions are defined with JSON schemas. The model emits functionCall parts, which you execute locally and return as functionResponse parts. This closed loop powers autonomous agents interacting with external APIs or databases.

Configuring Generation Parameters for Production Reliability

Fine-tune behavior through the generationConfig object:

• thinking_level: "low" | "high" (default high)
• maxOutputTokens: 64k maximum
• stopSequences: Array of strings that halt generation
• responseMimeType: "application/json" for structured output
• responseJsonSchema: Pydantic or Zod schema for type-safe parsing

Combining structured outputs with tools extracts clean JSON from web searches or code execution, streamlining backend logic integration.

Harnessing Multimodal Capabilities

Gemini 3.1 Pro processes images, videos, and documents natively. Include file data as base64 inline or via the File API for larger uploads.

Python Multimodal Example:

import base64
from google import genai
from google.genai import types

client = genai.Client()

# Read image
with open("diagram.png", "rb") as f:
    image_bytes = f.read()

response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents=[
        types.Content(
            role="user",
            parts=[
                types.Part(text="Analyze this system architecture diagram and suggest optimizations."),
                types.Part(
                    inline_data=types.Blob(
                        mime_type="image/png",
                        data=image_bytes
                    )
                )
            ]
        )
    ],
    config=types.GenerateContentConfig(
        media_resolution="media_resolution_high"  # v1alpha endpoint if needed
    )
)

print(response.text)

Testing and Debugging Efficiently with Apidog

Open Apidog and create a new project named "Gemini 3.1 Pro Integration." Add a global variable for your API key and set the base URL to the generative language endpoint.

Create collections for different scenarios: text-only, multimodal, function-calling, and streaming. Apidog auto-generates cURL, Python, and JavaScript snippets for saved requests, maintaining living documentation for your team.

When errors occur, Apidog highlights the exact header or payload field causing the issue. It records request history with timestamps and token usage, helping build accurate cost models before production deployment.

Conclusion

With this guide, you’re ready to integrate Gemini 3.1 Pro into your applications confidently. Start coding today, test thoroughly with Apidog, and push the boundaries of what AI can achieve. Bookmark the official documentation and revisit your Apidog project regularly to incorporate new features.

Begin building intelligent applications with the Gemini 3.1 Pro API now!