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Google · language model

Nano Banana Pro (Gemini 3 Pro Image): pricing, context and benchmarks

Nano Banana Pro (Gemini 3 Pro Image) is Google's model released on June 18, 2026. It costs $2 per million input tokens and $12 per million output tokens, with a 131.1K-token context window. No independent score has been published for it so far.

Score
32/100
Price 4/30
Context 4/15
Features 11/15
High costReasoningTool calling

What you need to know about Nano Banana Pro (Gemini 3 Pro Image)

Google, based in United States, put Nano Banana Pro (Gemini 3 Pro Image) online on June 18, 2026. It accepts images and text as input and produces images and text. Its context window reaches 131,072 tokens, larger than 29 % of the models in the registry, and it can write up to 32,768 tokens in one go.

It has been available for about 4 months, long enough for its prices and scores to have settled.

Nano Banana Pro (Gemini 3 Pro Image) pricing

On OpenRouter, Nano Banana Pro (Gemini 3 Pro Image) costs $2 per million input tokens and $12 per million output tokens. Counting three tokens read for every token written, its average price is $4.5 per million, more expensive than 88 % of paid models (5.9 times the median of $0.76).

A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.01. At 1,000 tasks a day, budget $300 a month. The price calculator redoes this with your own volumes.

Tokens already sent and cached are billed at $0.2 per million.

Performance and benchmarks

Epoch AI does not yet publish a capabilities index for Nano Banana Pro (Gemini 3 Pro Image). We therefore show no made-up performance rating: only price, context and features count towards its score.

Features, privacy and local use

  • It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
  • It can answer in an enforced JSON format (structured outputs).
  • It is a reasoning model: it "thinks" before answering, which helps on hard tasks but uses more output tokens.

Its weights are not published: it can only be used through the Google API or a broker such as OpenRouter, which means sending your data off your machines.

Other Google models

Average price per million tokens (3 input for 1 output).
ModelReleasedAvg. priceECI
Gemini 4 ArgonOct 13, 2026$4166.5
Nano Banana 2.1Oct 6, 2026$3—
Gemini 3.8 FlashSep 2, 2026$1.65157.4
Gemini 3.7 FlashAug 13, 2026$1.5158
Gemini 3.6 FlashJul 21, 2026$1.5154.5
Gemini 3.5 Flash LiteJul 21, 2026$0.85144.9
Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image)Jun 30, 2026$0.56—
Nano Banana 2 (Gemini 3.1 Flash Image)Jun 18, 2026$1.13—

Verdict

Computed score: 32/100, from public data (performance, price, context, features).

Strengths

  • Agent-ready: tool calling and structured JSON output.

Limitations

  • Expensive: costlier than 88 % of paid models.
  • No independent score published: performance cannot be verified.

Avoid if…

  • you process large volumes on a tight budget;
  • your data must not leave your servers;
  • your tasks are mostly simple: a non-reasoning model will cost less.

Frequently asked questions

How much does Nano Banana Pro (Gemini 3 Pro Image) cost?

$2 per million input tokens and $12 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.01.

How big is Nano Banana Pro (Gemini 3 Pro Image)'s context window?

131,072 tokens, roughly 98,000 words, with a maximum of 32,768 output tokens.

Can Nano Banana Pro (Gemini 3 Pro Image) run locally?

No, Google does not publish its weights: it is API-only.

Is Nano Banana Pro (Gemini 3 Pro Image) suitable for an AI agent?

Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.

What are the alternatives to Nano Banana Pro (Gemini 3 Pro Image)?

The closest models on price and performance are Aion-3.0, GPT-5.2 Chat, and Sonar Pro Search.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score