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Free AI Visibility AuditNano Banana 2 Lite, also listed as Gemini 3.1 Flash Lite Image, is Google's fastest and most cost-efficient Gemini image model, built for high-volume developer pipelines and rapid visual exploration.
GoogleVerified 24/09/2026Released 30/06/202601 / 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
$0.25
field median $0.43
Output / 1M tokens
$1.50
field median $1.75
Context
66K
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 2 Lite is an image generation model that takes text and images as input and produces images. It was built for text-to-image work at speed and low cost, the kind of job where you generate many variants and throw most of them away. It is not a general reasoning model and not the one to reach for when a single image has to be right first time.
Where it sits in its line, and when a sibling is the better pick.
Nano Banana 2 Lite first appeared on 30 June 2026 as the light, cheap tier of Google's Gemini image line. Pick it when volume and turnaround matter more than the last increment of fidelity, such as thumbnail batches, placeholder art, moodboards or an in-product generate button used thousands of times a day. When the output is the deliverable and a person will scrutinise it, a heavier Gemini image model is the better choice.
The API, the apps it powers, and what its limits let you do.
Nano Banana 2 Lite is reached through the Gemini API, where it accepts text and image input and returns images. The context window is modest by Gemini standards but sized for image prompting rather than long documents, so a prompt, a few reference images and style instructions fit comfortably in one call. That makes it straightforward to drop into a queue or a loop rather than a single interactive session.
What changes because this exists, or why it does not.
Nano Banana 2 Lite makes generate-many-then-filter workflows affordable. Teams that previously rationed image calls because each one cost real money can now generate a spread of options per request and pick, which changes the shape of the product rather than just the bill. It is an incremental release, the value is in the price and latency, not in new capability.
Follows Nano Banana 2 (Gemini 3.1 Flash Image), and is the newest in its line. See the whole line.
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 | $0.25 | per 1M tokens | 2026-09-24 | Check |
| Output | $1.50 | per 1M tokens | 2026-09-24 | Check |
Nano Banana 2 Lite sits at the bottom of Google's image model pricing, which is the point of it, with output costing several times input as is normal for generation work. Against the wider field of image-capable models it is among the cheapest routes to a generated image at scale, and the saving is large enough to justify routing bulk work here and reserving a stronger model for final assets.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $12.50 |
| A busy support assistant | 200M tokens | 40M tokens | $110.00 |
| A document pipeline | 1000M tokens | 100M tokens | $400.00 |
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 | 65,536 tokens |
|---|---|
| Maximum output | 58,982 tokens |
| Modalities | image, text |
| Released | 30/06/2026 |
| Status | Current |
| Catalogue identifier | google/gemini-3.1-flash-lite-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.
Came before
← Nano Banana 2 (Gemini 3.1 Flash Image)Came after
Nothing newer in this line yet.
Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / 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
A cheap, fast image model does not change how your brand gets cited in AI answers, that is still driven by text retrieval. What it does change is the competitive floor: image generation inside a product is now cheap enough that it stops being a differentiator worth writing about, so the pages that win citations are the ones explaining the workflow around the images, not the fact that they exist.
Most buyers never see the Nano Banana 2 Lite name directly. They meet it as the image generation sitting inside a SaaS product their team already uses, or through Google's own Gemini surfaces, and developers meet it as a model string in the Gemini API when they are costing out a pipeline.
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.