name: nano-banana-pro
description: Generate images with Google's Nano Banana Pro (Gemini 3 Pro Image). Use when generating AI images via Gemini API, creating professional visuals, or building image generation features. Triggers on Nano Banana Pro, Gemini 3 Pro Image, gemini-3-pro-image-preview, Google image generation.

Nano Banana Pro (Gemini 3 Pro Image)

Generate high-quality images with Google's Gemini 3 Pro Image API.

Overview

Nano Banana Pro is the marketing name for Gemini 3 Pro Image (gemini-3-pro-image-preview), Google's state-of-the-art image generation and editing model built on Gemini 3 Pro.

Quick Start

Get API Key

1. Go to Google AI Studio
2. Click "Get API Key"
3. Store securely as environment variable

Basic Image Generation (Python)

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="A serene Japanese garden with cherry blossoms and a koi pond",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

# Process response
for part in response.candidates[0].content.parts:
    if hasattr(part, 'text'):
        print(f"Description: {part.text}")
    elif hasattr(part, 'inline_data'):
        # Save image
        image_data = part.inline_data.data  # Base64 encoded
        mime_type = part.inline_data.mime_type  # image/png

        import base64
        with open("output.png", "wb") as f:
            f.write(base64.b64decode(image_data))

REST API (cURL)

curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "role": "user",
      "parts": [{"text": "Create a vibrant infographic about photosynthesis"}]
    }],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"]
    }
  }'

TypeScript/JavaScript

const GEMINI_API_KEY = process.env.GEMINI_API_KEY;

async function generateImage(prompt: string) {
  const response = await fetch(
    'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
    {
      method: 'POST',
      headers: {
        'x-goog-api-key': GEMINI_API_KEY!,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        contents: [{ 
          role: 'user', 
          parts: [{ text: prompt }] 
        }],
        generationConfig: {
          responseModalities: ['TEXT', 'IMAGE'],
        },
      }),
    }
  );

  const data = await response.json();
  return data;
}

Configuration Options

Image Configuration

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Professional product photo of a coffee mug",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Options: 1:1, 3:2, 16:9, 9:16, 21:9
            image_size="2K"       # Options: 1K, 2K, 4K
        )
    )
)

With Google Search Grounding

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Create an infographic showing today's stock market trends",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        tools=[{"google_search": {}}]  # Enable search grounding
    )
)

Multi-Turn Conversations (Iterative Editing)

# Create a chat session
chat = client.chats.create(
    model="gemini-3-pro-image-preview",
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        tools=[{"google_search": {}}]
    )
)

# Initial generation
response1 = chat.send_message(
    "Create a vibrant infographic explaining photosynthesis"
)

# Edit the image
response2 = chat.send_message(
    "Update this infographic to be in Spanish. Keep all other elements the same."
)

Key Capabilities

1. Superior Text Rendering

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="""Create a professional poster with:
    - Title: "Annual Tech Summit 2025"
    - Date: March 15-17, 2025
    - Location: San Francisco Convention Center
    """,
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

2. Character Consistency (Up to 5 Subjects)

import base64

def load_image(path: str) -> str:
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode()

character_ref = load_image("character.png")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[
        {"text": "Generate an image of this person at a tech conference"},
        {"inline_data": {"mime_type": "image/png", "data": character_ref}}
    ],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']
    )
)

Next.js API Route

// app/api/generate-image/route.ts
import { NextRequest, NextResponse } from 'next/server';

export async function POST(request: NextRequest) {
  const { prompt, aspectRatio = '1:1', imageSize = '2K' } = await request.json();

  try {
    const response = await fetch(
      'https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent',
      {
        method: 'POST',
        headers: {
          'x-goog-api-key': process.env.GEMINI_API_KEY!,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          contents: [{ role: 'user', parts: [{ text: prompt }] }],
          generationConfig: {
            responseModalities: ['TEXT', 'IMAGE'],
            imageConfig: { aspectRatio, imageSize },
          },
        }),
      }
    );

    const data = await response.json();
    const parts = data.candidates?.[0]?.content?.parts || [];
    const imagePart = parts.find((p: any) => p.inline_data);

    return NextResponse.json({
      image: imagePart ? {
        data: imagePart.inline_data.data,
        mimeType: imagePart.inline_data.mime_type,
        url: `data:${imagePart.inline_data.mime_type};base64,${imagePart.inline_data.data}`,
      } : null,
    });
  } catch (error) {
    return NextResponse.json({ error: 'Generation failed' }, { status: 500 });
  }
}

Model Comparison

Resources

• Documentation: https://ai.google.dev/gemini-api/docs/image-generation
• Google AI Studio: https://aistudio.google.com
• Prompt Guide: https://ai.google.dev/gemini-api/docs/prompting-intro