LLM Reference
Google AI Studio

Nano Banana Pro (Gemini 3 Pro Image) on Google AI Studio

Gemini 3 · Google DeepMind

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Last refreshed 2026-06-15. Next refresh: weekly.

Why use Nano Banana Pro (Gemini 3 Pro Image) on Google AI Studio?

Google AI Studio offers Nano Banana Pro (Gemini 3 Pro Image) with pay-as-you-go pricing at $2.00/1M input tokens. Google AI Studio is a model prototyping environment and API access point for Gemini models, offering an inference playground for developers to test and build AI applications.

Compare Nano Banana Pro (Gemini 3 Pro Image) across 2 providers to find the best fit for your use case
Input / 1M
$2.00
Output / 1M
$120.00
Cache
Not sourced
Batch
-50% · in $1.00

Setup recipe

Python + curl
Install
pip install google-genai
Auth
export GOOGLE_API_KEY=...
Call
import os
from google import genai
client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
response = client.models.generate_content(
Model ID
gemini-3-pro-image

Request example

import os
from google import genai

client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
response = client.models.generate_content(
    model="gemini-3-pro-image",
    contents="Hello"
)
print(response.text)

Gotchas

  • Use the model name directly, e.g. "gemini-2.0-flash", "gemini-1.5-pro", or "gemini-2.5-pro-preview-05-06".
  • The examples expect GOOGLE_API_KEY; rename it only if your application config maps the new variable.

Compare Nano Banana Pro (Gemini 3 Pro Image) Across Providers

ProviderInput (per 1M)Output (per 1M)
Google AI Studio$2.00$120.00
Prodia

Pricing

TypePrice (per 1M)
Input tokens$2.00
Output tokens$120.00

Capabilities

VisionMultimodalReasoningJSON / Tool useStructured OutputsBatch API

About Nano Banana Pro (Gemini 3 Pro Image)

Nano Banana Pro is the GA Gemini 3 Pro Image model for high-fidelity image creation through the Gemini API. It is aimed at complex graphic design, product mockups, data visualizations, and accurate text rendering, and replaces gemini-3-pro-image-preview retiring on 2026-06-25.

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Model Specs

Released2026-05-28
Context66k
ArchitectureDecoder Only
Knowledge cutoff2025-01