Nano Banana 2 Lite vs Nano Banana 2: Pricing Compared

Choose Nano Banana 2 Lite when your product needs many 1K images quickly and cheaply. Choose Nano Banana 2 when output resolution, search grounding, or reference-heavy composition matters enough to justify roughly twice the 1K output price.

That is the practical answer to Nano Banana 2 Lite vs Nano Banana 2 pricing. As of 2026-07-20, Google’s standard Gemini Developer API price is $0.0336 for a 1K Lite image and $0.067 for a 1K Nano Banana 2 image. The decision is not simply cheap versus good: Lite has a narrower output specification, while the standard model provides more production controls.

We run image tools daily, but date-sensitive claims belong to the documentation. Every price and limit below was checked on 2026-07-20.

First, the naming

Google’s product names are easy to blur together. The two models compared here are:

• Nano Banana 2 Lite, formally Gemini 3.1 Flash Lite Image, with model ID gemini-3.1-flash-lite-image.
• Nano Banana 2, formally Gemini 3.1 Flash Image, with model ID gemini-3.1-flash-image.

Nano Banana Pro is a third, separate model called Gemini 3 Pro Image. It is not another name for Nano Banana 2. Google’s image-generation guide positions Lite as the fastest, cheapest option, Nano Banana 2 as the generalist model, and Pro as the premium model for more complex work.

Nano Banana 2 Lite vs Nano Banana 2 at a glance

The prices in this table are Gemini Developer API paid-tier rates in USD, as of 2026-07-20. The official Gemini API pricing page lists no free tier for either image model.

Decision factor	Nano Banana 2 Lite	Nano Banana 2
Model ID	gemini-3.1-flash-lite-image	gemini-3.1-flash-image
Standard 1K image output	$0.0336	$0.067
Batch 1K image output	$0.0168	$0.034
Standard input, per 1M tokens	$0.25 pricing category; model input is text/image	$0.50, text/image
Standard text/thinking output, per 1M tokens	$1.50	$3.00
Output sizes	1K only	0.5K, 1K, 2K, 4K
Published latency detail	Sub-2-second target; Google launch post reports 4-second text-to-image output	Described as low latency; no single numeric figure in the model page reviewed
Search grounding	Not supported	Google Web and Image Search supported
Best operational fit	Interactive drafts and high-volume 1K generation	General production, higher resolution, grounded or reference-heavy work

At 1,000 standard 1K outputs, the listed image-output charge is about $33.60 with Lite versus $67 with Nano Banana 2. At batch rates, 1,000 outputs cost about $16.80 versus $34. These estimates exclude input tokens, text or thinking output, retries, and search queries. Google says batch jobs can take up to 24 hours.

What the per-image prices include

Google bills generated images through image-output tokens. A 1K image uses 1,120 output tokens on both models. Lite charges $30 per million image-output tokens, producing the published $0.0336 equivalent. Nano Banana 2 charges $60 per million, producing the published $0.067 equivalent after rounding.

Nano Banana 2 also exposes more resolution choices. At standard rates as of 2026-07-20, Google lists $0.045 for 0.5K, $0.067 for 1K, $0.101 for 2K, and $0.151 for 4K. Lite is optimized for 1K and its official model page explicitly says that 2K and 4K are unsupported.

Do not budget only from the headline image price. Prompts and reference media create input-token charges, and responses may include billed text or thinking tokens. Nano Banana 2 can also use Google Web and Image Search grounding. The official pricing page lists 5,000 grounded prompts per month at no search charge, shared across Gemini 3, then $14 per 1,000 search queries as of 2026-07-20. A request may trigger more than one query, so grounded-request count and billed-query count are not necessarily equal.

Speed: Lite has the clearer latency case

Lite is the speed-first model, but Google’s own pages use two figures that need context. The Lite model page says it targets sub-two-second end-to-end latency and lists sub-two-second latency as a capability. Google’s June 30 launch post says Lite delivers text-to-image outputs in four seconds. Those statements may reflect different test paths or definitions; Google does not explain the difference on those pages.

Treat both as vendor-reported figures, not an application-level service guarantee. Network distance, prompt complexity, editing inputs, safety checks, load, and retries can change observed latency. Before routing a user-facing feature to either model, log p50 and p95 latency from your own prompts and region.

Google describes Nano Banana 2 as low latency and suitable for quick interactive responses and high throughput, but its model page does not publish a comparable end-to-end number.

Quality and control differences

Lite is not merely an older model sold more cheaply. It is a newer efficiency specialist with 1K output, local edits, text rendering, character alignment, and multiple aspect ratios. Google’s launch material says it retains prompt adherence, character consistency, and legible in-image text while prioritizing speed.

The limits still matter. The main image-generation guide says Lite is not optimized for multiple reference inputs or multi-turn sequential editing. The same guide documents support for up to 14 high-fidelity object images, so “not optimized” does not mean “unsupported.” It means you should not assume that accepting many inputs makes Lite the stronger choice for a long, consistency-sensitive editing chain.

Nano Banana 2 is the safer default for those chains. Google’s guide describes it as the generalist workhorse, strong at multiple-reference processing and consistency. It supports up to ten high-fidelity object references plus up to four character references in one workflow. Its model page lists a 131,072-token input limit and 32,768-token output limit, compared with Lite’s 65,536 input and 4,096 output limits.

Standard also adds controls Lite lacks: 0.5K through 4K output, 1:4, 4:1, 1:8, and 8:1 aspect ratios, PDF input, and search grounding. These are concrete reasons to pay more, not proof that every output will look better.

Both models can still misspell text, distort fine details, mishandle complex edits, or generate inaccurate data graphics. Google advises checking generated images, including embedded text, before use. Keep human or automated review in the pipeline regardless of price tier.

Pick Lite if…

Choose Nano Banana 2 Lite when most of these describe the workload:

• Every deliverable can stay at 1024px output.
• Users are waiting in an interactive interface.
• You generate many candidates, thumbnails, stickers, backgrounds, or concept frames.
• A low cost per attempt matters because prompts often need several iterations.
• Edits are local and short rather than long, reference-heavy conversations.
• You can promote selected outputs to another model for final production.

A sensible pipeline is Lite for exploration, then Nano Banana 2 only for selected assets needing higher resolution or tighter consistency. If ten Lite attempts replace ten standard attempts, the listed 1K output cost falls from about $0.67 to $0.336 before input and text charges.

Pick Nano Banana 2 if…

Choose the standard Nano Banana 2 model when one or more of these are requirements:

• You need 0.5K, 2K, or 4K output rather than 1K only.
• The image must incorporate several objects and recurring characters consistently.
• Your generation depends on current Web or Image Search context.
• You need PDF input or the model’s larger context limits.
• You are producing a smaller number of assets where review time costs more than the model-price difference.
• You need extreme horizontal or vertical aspect ratios supported by the standard model.

For 1K work, the premium over Lite is $0.0334 per generated image at the listed standard rates. If the standard model prevents even one manual reconstruction across a batch, that difference may be reasonable. That outcome is workload-dependent, so test it rather than assuming the more expensive route wins.

A practical evaluation before switching

Build a small prompt set from real work: simple generation, text-heavy graphics, a local edit, a character-consistency task, and a multi-reference composition. Run each prompt several times on both models. Record output cost, input and thinking charges, latency percentiles, acceptance rate, and reviewer minutes.

Compare cost per accepted image, not cost per API response. A $0.0336 output rejected twice costs more in model charges and attention than a $0.067 output accepted once. Conversely, paying for 4K, grounding, or larger context adds no value when a 1K draft is the final requirement.

Our default routing rule is straightforward: start 1K exploratory and latency-sensitive work on Lite; route final, grounded, high-resolution, or reference-sensitive work to Nano Banana 2. Recheck the official pricing and model pages before deployment, because this comparison is a dated snapshot, not a billing commitment.