All models
Google

Gemini 3 Pro Image

−30% vs Google
✓ Official API· served byGoogle Vertex AIVertex AI
Try in Playground

Gemini 3 Pro Image, which Google calls Nano Banana Pro, is a reasoning-driven engine for professional-grade image editing and generation with studio-quality precision and advanced creative control. Google recommends it for complex graphic design, high-fidelity product mockups, and factual data visualizations that need accurate text rendering and grounding in Google Search.

The real Google model

Same weights, same API, same answers. We change only how you pay for it: one key for every maker, one bill, no separate account to open. Every figure on this page comes from Google’s own page — check it.

Maker's own page

Price

input / output · per 1M tokens

Google Vertex AIVertex AI

−30%Only on FlintBeam

−30% on every request

Direct on Vertex AI: $2.00 / $120.00

Daily discount until 2026-11-30; it can change or end earlier.

$2.00 / $120.00$1.40 / $84.00

One image: $0.09 to $0.22

Context

65,536

up to 32,768 out

32,768 by default here

Modalities

Reasoning

Released

Nov 2025

The prices above are per 1M tokens, the unit every model is billed in. A picture is billed as the tokens it weighs, so a larger one costs more.

What it would cost you

1000 tokens is roughly 750 English words.

Estimated total$44.80

How it is served

Economy

Requests are retried automatically if a provider is unavailable.

Similar models

Models built for the same kind of work, at a similar price.

Eligible models may have a daily discount below the maker's price. Daily discounts apply to eligible models and can change from day to day.

Use it from your code

Model:google/gemini-3-pro-image
main.py
OpenAI Specification v1.0
from openai import OpenAI

# Works with the official openai package (pip install openai)
client = OpenAI(
    base_url="https://api.flintbeam.com/v1",
    api_key="sk-live-your-api-key"
)

response = client.chat.completions.create(
    model="google/gemini-3-pro-image",
    messages=[
        {"role": "system", "content": "You are an experienced software engineer."},
        {"role": "user", "content": "Describe the architecture of a distributed cache."}
    ],
    temperature=0.7,
    max_tokens=1500
)

print(response.choices[0].message.content)