Nano Banana 2.1 vs Nano Banana 2: Tested on 18 Everyday Photo Edits

Nano Banana 2.1 is Google's newest Gemini image model for creating and editing pictures, released on October 6, 2026 as the replacement for Nano Banana 2 at about half the API price. Google says it improves on its previous models across the board. On everyday photo edits, OutfitGen's October 2026 benchmark found otherwise: blind judging rated 13 of 18 edits fully done, against 15 for Nano Banana 2, with the same refusals and the same speed.

Both models got the same instruction, "a modest navy one-piece swimsuit and a light beach cover-up", on the same photo of a woman in jeans. Both delivered it in every run. The full set of 13 models on this edit is on the benchmark page.

What Google says is new in Nano Banana 2.1

Nano Banana is the public name for Google's Gemini image models. Nano Banana 2 (model ID gemini-3.1-flash-image, also called Gemini 3.1 Flash Image) became one of the most widely used image editors through the Gemini app and API. Google's changelog for October 6, 2026 made Nano Banana 2.1 (gemini-nano-banana-2.1) generally available as an update to that model and marked Nano Banana 2 as deprecated, with no shutdown date announced. The deprecations page names Nano Banana 2.1 as its replacement.

In its launch post, Google says the new model improves on previous versions "across the board", as The Decoder reports, along with a rollout across the Gemini app, AI Mode in Search, AI Studio, Flow, Stitch, Google Ads and the Gemini Enterprise Platform.

These are the improvements Google lists on its model page and image guide. They are Google's claims; our benchmark did not test them:

• Up to 14 reference images per request, keeping up to 4 characters and 10 objects consistent. That is a real help for putting a specific garment on a specific person.
• 4K output, alongside 1K and 2K.
• Thinking by default. You can set minimal, medium or high; Nano Banana 2.1 defaults to medium, Nano Banana 2 to minimal.
• Search grounding with Google Web Search and Google Image Search.
• Cleaner wide formats. Google says it fixed tiling artifacts on very wide and very tall ratios (such as 4:1 and 1:8) at 2K and 4K.
• A lower price, set out in the table below.

Specs and price side by side

Figures from Google's model page, image guide and Gemini API pricing page, checked on October 14, 2026.

At volume the saving is real. A thousand 1K edits cost $33.60 on Nano Banana 2.1 against $67 on Nano Banana 2, and a thousand 2K edits cost $50.40 against $101. At 4K the saving is about 25%.

How we tested it

A spec sheet can't tell you whether a model does the edit you asked for. So on October 14, 2026, eight days after release, the OutfitGen Team ran Nano Banana 2.1 on the same test set as every model on our AI image editing leaderboard:

• The edits. 18 everyday fashion edits on 10 synthetic adult photos: outfit changes, background changes and pose changes, including the swimwear, gym and catalog lingerie cases from our over-refusal benchmark.
• Repeats. Five runs per edit, 90 in total, because the same request can work once and fail the next time.
• Judging. A reviewer rated every delivered image without knowing which model made it. An edit counts as done right only if every delivered run did it.
• Refusals. A run the model declined on policy grounds counts as a refusal. Every request is an ordinary edit of an adult, so every refusal is a false positive.
• A same-day control. We re-ran Nano Banana 2 in the same session, on the same photos and instructions, through fal's hosted API at default settings (1K output). That keeps the comparison fair even when a model's behavior drifts between weeks.

The leaderboard now holds 13 models and 1,170 generations, with CSV and JSON downloads, so you can check every number here.

Results: Nano Banana 2.1 did not beat Nano Banana 2

The session ran slowly for every model, so the leaderboard shows Nano Banana 2.1 at 11.5 seconds, its time scaled to the September session through the same-day control. Either way, the two are tied on speed.

Where Nano Banana 2.1 fell short

It refused exactly what Nano Banana 2 refused. All 13 of its refusals landed on three edits: a two-piece swimsuit on a clothed person (5 of 5), a catalog lingerie set on a clothed person (4 of 4 answered) and a two-piece copied from a product photo (4 of 4 answered). The same-day Nano Banana 2 control refused the same requests. On ordinary swimwear, the filter line did not move.

It missed two edits Nano Banana 2 got right. Asked to change a lingerie catalog backdrop to pale pink, it came back beige instead in 3 of 5 runs. Asked for a wrap dress and sandals, it cropped the photo at the thigh or knee in all 4 delivered runs, so the sandals never appeared. Those two misses are the whole gap between 13 and 15.

What it does well

Nano Banana 2.1 is a capable editor on most of the set. It delivered all 64 edits of people already wearing swimwear, gym wear, lingerie or no shirt, every background and pose change among them, and the person stayed recognizable in every delivered run. It also stopped adding the small chest logo Nano Banana 2 sometimes invented on a plain training t-shirt (0 of 5 runs). And it does all of this at half the API price, which matters a lot if you edit at volume.

What this means if you edit photos

If you call Nano Banana 2 through the API, moving to 2.1 halves the bill at 1K and 2K, and Google has deprecated the old model anyway. Just don't expect sharper outfit edits or fewer swimwear refusals: on our set, it got slightly fewer edits right.

If you choose a model for photo edits, the leaderboard points elsewhere. Seedream 5 Pro, Seedream 5 Flash, Flux 2 Turbo and Flux 2 Flash each got 17 of 18 edits right with no refusals, and seven models refused none of their 90 requests. The full ranking, with speed and refusal rate for every model, is on the leaderboard.

If you just want the outfit changed, skip the model shopping. OutfitGen's AI clothes changer is built for that one job: upload a photo, type the outfit, and keep your face and pose. Try it right here: