Puw vs Nano Banana
Short answer
Nano Banana is a general-purpose image model you prompt; Puw is a narrow tool for business photos. If you want to invent and restyle images, use Nano Banana. If you need the room, the plate or the label to come back exactly as photographed with better light, that constraint is what Puw is built around.
This is less a competition than a category difference, and pretending otherwise would waste your time. Google's image models are extraordinary at making a picture that did not exist. A listing photo is the opposite problem: it already exists, it has to stay true, and the only thing that should change is the quality of the light falling on it.
Side by side.
| Nano Banana | Puw | |
|---|---|---|
| What it is | A general image generation and editing model | A renderer for business photos |
| How you use it | Prompt it in the Gemini app or via the API | Drop photos in, pick a mode, optional note |
| Small faces | Google lists this as something it can still struggle with | Held fixed through the render |
| Accurate spelling in-image | Google lists this as something it can still struggle with | Text preserved character-for-character |
| Character consistency | A stated strength — same subject across edits and angles | Not a goal; the subject is the photo you gave it |
| Batch of 25 listing photos | Prompt each one, or build against the API | One drop, all render at once |
| Output | Depends on the model and surface you use | 4K native, 8K and 16K upscale |
| Price | Free tier in the Gemini app; API billed per image | $0.07–$0.30 a photo, first render free |
When Nano Banana is the better choice
- You want to generate something that was never photographed — a concept, an illustration, a product in a place it has never been.
- You want to restyle heavily: change the season, swap the outfit, move the subject into a different scene. That is exactly what it is built for.
- You want the same character or product held consistent across many generated images. Google calls this out as a strength, and it is not something Puw does at all.
- You are already building on the Gemini API and one more image call is cheaper than one more vendor.
- You want to experiment for free in a chat window without thinking about credits.
When Puw is
- The photo has to remain a truthful record — a room a guest will sleep in, a dish that will arrive at a table, a property someone will buy.
- There is printed text in frame. Menu prices, model numbers, signage and allergen notes come back unchanged rather than approximated.
- You have twenty-five photos and twenty minutes, and prompting each one individually is not a plan.
- You want a consistent look across a whole set without engineering a prompt that reproduces itself.
- You do not want to learn prompting at all. Pick Precise, press Generate.
Real outputs
A feed, not a pitch deck.
Every photo below started as a phone snapshot. Tap one to compare before / after.
Drop one or many — they all render at the same time, each with its own mode and notes.
Mode and size below apply to every photo — set a different mode or note on any row to override it.
Questions people ask.
Is Puw built on Nano Banana?
No. Puw runs its own render pipeline with two models, Aria and Symphony, and a mode system that controls how much freedom the render is given. Nano Banana is a Google model you reach through the Gemini app or API.
Can I just use Nano Banana for my hotel photos?
You can, and for a single atmospheric shot it may well be the better answer. The two things to watch are the ones Google names itself — small faces and in-image spelling — plus the practical cost of prompting twenty-five photos one at a time.
Which one is cheaper?
For occasional single images, the free tier in the Gemini app is hard to beat. For a recurring batch — a listing a day, a menu a month — Puw is $0.07 to $0.30 a photo with no prompt engineering in between.
Does Puw let me prompt like Nano Banana does?
Partly. Each photo takes a plain-language note of up to 300 characters, and Magic Edit handles targeted changes. It is deliberately narrower than an open prompt box, because the narrowness is what keeps faces and text intact.
The Gemini image models keep changing. Is this comparison current?
It was checked on 27 August 2026 against Google's own documentation, and the model family has moved more than once since it launched. Treat the source links as the authority and this page as a snapshot.
Where these claims come from
- Small faces, accurate spelling and fine details listed as current limitations of Gemini 2.5 Flash Image (Nano Banana) — developers.googleblog.com, checked 27 August 2026
- Character and subject consistency across edits described as a core capability — deepmind.google, checked 27 August 2026
- Model family, surfaces and API availability — ai.google.dev, checked 27 August 2026
Competitors change their products and their prices. If something here has gone out of date, the source link is the authority, not this page. All other names and trademarks belong to their owners.
