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Free AI Visibility AuditNano Banana Pro is Google's image generation and editing model, built on Gemini 3 Pro and sold through the Gemini API. It takes both images and text as input and returns images, so it handles generation and editing of existing assets in the same call.
GoogleVerified 24/09/2026Released 18/06/202601 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 151 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$2.00
field median $0.43
Output / 1M tokens
$12.00
field median $1.75
Context
131K
field median 524K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Nano Banana Pro is an image model, not a general assistant. It is built for generating pictures from a prompt and for editing images you supply, with Google claiming stronger multimodal reasoning and real-world grounding than the original Nano Banana. It is the wrong tool for long text generation, code or chat, even though it reads text as input.
Where it sits in its line, and when a sibling is the better pick.
Nano Banana Pro appeared in June 2026 as the Gemini 3 Pro branch of the Nano Banana line, sitting above the original Nano Banana. Pick it when the image depends on getting details right, following a complicated instruction, editing an existing asset faithfully, or reflecting something true about the world. For high volume, low stakes image work, the smaller Nano Banana is the more sensible choice.
The API, the apps it powers, and what its limits let you do.
Nano Banana Pro is reached through Google's Gemini API and surfaces in the Gemini app. Its context window is large enough to carry several reference images plus detailed written direction in one request, which is what makes multi-image editing and consistent character or product work practical. Input can be image or text, or both together, and output comes back as images.
What changes because this exists, or why it does not.
The useful change is the grounding and reasoning Google has put behind the image work. Prompts that describe a real thing, a real layout or a long list of constraints have a better chance of coming back correct, which reduces the round trips designers normally spend nudging an image model towards what they asked for. For teams already on the Gemini API, it also means image work sits behind the same key and the same billing as everything else.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $2.00 | per 1M tokens | 2026-09-24 | Check |
| Output | $12.00 | per 1M tokens | 2026-09-24 | Check |
Nano Banana Pro is priced as a premium image model, with output costing several times what input does, so the spend sits in what you generate rather than what you feed it. That puts it above the lighter Nano Banana for routine work, and makes it worth reserving for images where accuracy matters more than volume.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $100.00 |
| A busy support assistant | 200M tokens | 40M tokens | $880.00 |
| A document pipeline | 1000M tokens | 100M tokens | $3,200.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 131,072 tokens |
|---|---|
| Maximum output | 32,768 tokens |
| Modalities | image, text |
| Released | 18/06/2026 |
| Status | Current |
| Catalogue identifier | google/gemini-3-pro-image |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Google
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.
How we decide what counts as evidence10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Nano Banana Pro does not change how your brand gets retrieved or cited, it changes what gets made once someone has an answer. The practical consequence for a SaaS brand is that screenshots, diagrams and product imagery are now cheap for anyone to produce and edit, including competitors and third parties describing your product. Keep your own visual assets accurate, labelled and easy to find, so the grounded version is the one that circulates.
Most buyers meet Nano Banana Pro inside the Gemini app, where it is the engine behind image generation and editing rather than a name they choose. Developers meet it directly through the Gemini API. It is an image surface, so it is not where your brand gets cited in an AI answer, but it is increasingly where the visuals in those experiences come from.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.