Nano Banana 2 Lite vs Nano Banana 2 vs Pro vs Original

Google currently documents four Nano Banana API models, not a single model with quality tiers. Nano Banana 2 Lite is the low-cost 1K option, Nano Banana 2 is Google’s general-purpose image model, Nano Banana Pro is positioned for complex professional assets, and the original Nano Banana is a legacy model approaching shutdown.

For most Photoshop production work, Bielfor starts with Nano Banana 2 at 2K and changes models only when the task gives a reason. That is a Bielfor workflow judgment, not a Google benchmark. The tables below separate provider facts from our routing advice.

What are the four current Nano Banana models?

Google’s Nano Banana image guide names these four stable API IDs:

The official pricing table presents those amounts as image-output equivalents. Text, image input, thinking output, and Search grounding can add to the total request cost.

Two corrections matter for old tutorials. First, 2K and 4K size controls belong to the current Gemini 3 image models. The original output is at most 1024px-class. Second, Lite is now the least expensive current Google image API route at 1K.

Is the original Nano Banana still current?

It remains callable as gemini-2.5-flash-image, but Google labels it legacy. The deprecations table lists October 2, 2026 as its shutdown date. Google’s image guide recommends that customers move from the legacy original to Nano Banana 2 Lite for lower price, higher quality, and faster generation.

That makes the original a migration concern, not a sensible foundation for a new long-lived workflow. If a production tool still depends on it, test Lite and Nano Banana 2 before the cutoff and verify that presets do not silently request unavailable native 2K output.

Which resolutions does each model support?

• Nano Banana 2 Lite: 1K only.
• Nano Banana 2: 0.5K, 1K, 2K, and 4K.
• Nano Banana Pro: 1K, 2K, and 4K.
• Original: output up to 1024px, without a native 2K or 4K setting.

For a large Photoshop canvas, a 2K or 4K generation avoids starting from an unnecessarily small plate. Resolution alone does not prove visual quality, however. Prompt, reference quality, task complexity, and post-production still matter.

How many reference images does each model accept?

There is no universal Google reference cap that accurately describes every model.

• The original works best with up to three input images.
• Nano Banana 2 supports high-fidelity handling for up to 10 objects plus four character references in one workflow.
• Pro supports up to 14 total references, with Google’s table separating as many as six objects, five characters, and three style references. A separate limitation notes five high-fidelity inputs, so 14 total should not be described as 14 high-fidelity references.
• Lite documents up to 14 object images, but Google explicitly says the model is not optimized for multiple-reference or sequential-editing workflows.

These limits come from the current generation guide. A Photoshop panel may impose a smaller interface cap. If it does, call that a product limit rather than a Google model limit.

What thinking controls are available?

Google’s GenerateContent image guide documents minimal and high for Nano Banana 2 and Lite. minimal is the default and lowest-latency setting, but minimal does not turn thinking off. There is no documented none level for these image models.

Pro uses a thinking process but does not expose the same level switch in Google’s image guide. The original does not support thinking. The listed image-output charge stays the same when the Flash thinking level changes, but text and thinking output is billed separately. Total request cost can therefore vary.

Which models support Search grounding?

The same GenerateContent image guide documents Google Web and Image Search grounding for Nano Banana 2 and Google Search grounding for Pro. Lite and the original do not support Search grounding. Search calls can create separate charges after the included allowance, and grounded output still needs human verification.

Use Search only when the image genuinely depends on current facts or real-world context. It is unnecessary overhead for an abstract backdrop, a controlled product scene, or a local inpaint.

Which model should you choose for Photoshop?

The following is Bielfor workflow judgment based on task shape, not an official Google ranking:

Google’s Pro model card supports the provider-side statement that Pro targets complex professional asset production and accurate text. It does not establish that Pro wins every Photoshop task or every cross-provider comparison.

How different are Nano Banana 2 and Pro prices at 2K?

At 2K, the displayed image-output equivalents are $0.101 for Nano Banana 2 and $0.134 for Pro. Pro is 32.7 percent more expensive relative to Nano Banana 2. In the other direction, Nano Banana 2 is 24.6 percent cheaper relative to Pro. State the denominator when quoting a percentage.

At 1K, $0.134 is exactly twice $0.067. That 2x statement does not apply at 2K. Three original-model outputs cost $0.117, slightly less than one Pro 1K or 2K output; four cost $0.156, which is more.

How do you use these models inside Photoshop?

The Gemini API produces and edits images; the Photoshop integration is supplied by Adobe, a plugin, a script, or a manual transfer. See all Nano Banana Photoshop methods and the Google API key setup.

Disclosure: Bielfor builds Creator MAX. We therefore label product-routing advice as our own judgment and link Google facts to Google sources. Regardless of interface, preserve the original layer, inspect masks at 100 percent, and verify generated text manually.

FAQ

Is Nano Banana Pro better than Nano Banana 2?

Not for every task. Google positions Pro for complex professional assets and accurate text. Nano Banana 2 is its general-purpose model and costs less at 2K and 4K. Choose with a representative test file.

Does the original Nano Banana support native 2K?

No. Google prices and documents output up to 1024px for gemini-2.5-flash-image. A 2K result shown by a third-party interface needs to be identified as tool-side resizing or upscaling.

Do the current Google image models share a 14-reference limit?

No. The documented guidance is model-specific, and total inputs are not the same as high-fidelity object or character references.

What are the Nano Banana 2 thinking settings?

The documented controls are minimal and high. Minimal does not mean zero thinking.