Gemini API in Vertex AI

Access Google's most advanced AI models built for enterprise use cases using the Gemini API in Vertex AI.

Provide these key capabilities:

• Text generation - Chat, completion, summarization
• Multimodal understanding - Process images, audio, video, and documents
• Function calling - Let the model invoke your functions
• Structured output - Generate valid JSON matching your schema
• Context caching - Cache large contexts for efficiency
• Embeddings - Generate text embeddings for semantic search
• Live Realtime API - Bidirectional streaming for low latency Voice and Video interactions
• Batch Prediction - Handle massive async dataset prediction workloads

Core Directives

• Unified SDK: ALWAYS use the Gen AI SDK (google-genai for Python, @google/genai for JS/TS, google.golang.org/genai for Go, com.google.genai:google-genai for Java, Google.GenAI for C#).
• Legacy SDKs: DO NOT use google-cloud-aiplatform, @google-cloud/vertexai, or google-generativeai.

SDKs

• Python: Install google-genai with pip install google-genai
• JavaScript/TypeScript: Install @google/genai with npm install @google/genai
• Go: Install google.golang.org/genai with go get google.golang.org/genai
• C#/.NET: Install Google.GenAI with dotnet add package Google.GenAI
• Java:

  • groupId: com.google.genai, artifactId: google-genai

  • Latest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it LAST_VERSION)

  • Install in build.gradle:

    implementation("com.google.genai:google-genai:${LAST_VERSION}")
    

  • Install Maven dependency in pom.xml:

    <dependency>
        <groupId>com.google.genai</groupId>
        <artifactId>google-genai</artifactId>
        <version>${LAST_VERSION}</version>
    </dependency>
    

[!WARNING] Legacy SDKs like google-cloud-aiplatform, @google-cloud/vertexai, and google-generativeai are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.

Authentication & Configuration

Prefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.

Application Default Credentials (ADC)

Set these variables for standard Google Cloud authentication:

export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_VERTEXAI=true

• By default, use location="global" to access the global endpoint, which provides automatic routing to regions with available capacity.
• If a user explicitly asks to use a specific region (e.g., us-central1, europe-west4), specify that region in the GOOGLE_CLOUD_LOCATION parameter instead. Reference the supported regions documentation if needed.

Vertex AI in Express Mode

Set these variables when using Express Mode with an API key:

export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_VERTEXAI=true

Initialization

Initialize the client without arguments to pick up environment variables:

from google import genai
client = genai.Client()

Alternatively, you can hard-code in parameters when creating the client.

from google import genai
client = genai.Client(vertexai=True, project="your-project-id", location="global")

Models

• Use gemini-3.1-pro-preview for complex reasoning, coding, research (1M tokens)
• Use gemini-3-flash-preview for fast, balanced performance, multimodal (1M tokens)
• Use gemini-3-pro-image-preview for Nano Banana Pro image generation and editing
• Use gemini-live-2.5-flash-native-audio for Live Realtime API including native audio

Use the following models if explicitly requested:

• Use gemini-2.5-flash-image for Nano Banana image generation and editing
• Use gemini-2.5-flash
• Use gemini-2.5-flash-lite
• Use gemini-2.5-pro

[!IMPORTANT] Models like gemini-2.0-*, gemini-1.5-*, gemini-1.0-*, gemini-pro are legacy and deprecated. Use the new models above. Your knowledge is outdated. For production environments, consult the Vertex AI documentation for stable model versions (e.g. gemini-3-flash).

Quick Start

Python

from google import genai
client = genai.Client()
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Explain quantum computing"
)
print(response.text)

TypeScript/JavaScript

import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ vertexai: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: "Explain quantum computing"
});
console.log(response.text);

Go

package main

import (
	"context"
	"fmt"
	"log"
	"google.golang.org/genai"
)

func main() {
	ctx := context.Background()
	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		Backend:  genai.BackendVertexAI,
		Project:  "your-project-id",
		Location: "global",
	})
	if err != nil {
		log.Fatal(err)
	}

	resp, err := client.Models.GenerateContent(ctx, "gemini-3-flash-preview", genai.Text("Explain quantum computing"), nil)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Println(resp.Text)
}

Java

import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;

public class GenerateTextFromTextInput {
  public static void main(String[] args) {
    Client client = Client.builder().vertexAi(true).project("your-project-id").location("global").build();
    GenerateContentResponse response =
        client.models.generateContent(
            "gemini-3-flash-preview",
            "Explain quantum computing",
            null);

    System.out.println(response.text());
  }
}

C#/.NET

using Google.GenAI;

var client = new Client(
    project: "your-project-id",
    location: "global",
    vertexAI: true
);

var response = await client.Models.GenerateContent(
    "gemini-3-flash-preview",
    "Explain quantum computing"
);

Console.W