Two jobs, same client, both due Friday.
The first is a gig poster. Band name in a heavy display face, four support acts set at roughly 9pt, a venue address, a date, a ticket URL, and a QR code — all of it sitting on a duotone photograph. Twelve separate pieces of type, and every one of them has to be readable and correctly spelled.
The second is a lifestyle shot of the same band's tour hoodie. Folded on an unmade bed, morning light coming in sideways through a window, fabric weave visible in the fold shadows, one loose thread near the cuff. No text at all.
Run both jobs through a single image model and you will almost certainly ship one of them and re-roll the other four times. That split is the whole reason this comparison exists.
This is written on September 10, 2026, two days after OpenAI shipped ChatGPT Images 2.5 and the API models gpt-image-2.5-flare and gpt-image-2.5-sunburst. On the other side is Google DeepMind's Nano Banana Pro — officially Gemini 3 Pro Image — which has been the default answer to "which model renders text properly" since November 2025. One model is two days old, one is a known quantity, and that asymmetry matters.
A note on what follows: I have not run a private 200-image bake-off and won't pretend otherwise. Everything below comes from published specs, vendor claims (attributed as claims), LM Arena standings, third-party coverage, and the credit prices on this site. Where the evidence is thin — and on a two-day-old model, a lot of it is — I say so instead of filling the gap with adjectives.
Nano Banana Pro vs GPT Image 2.5: The Scoreboard
| Dimension |
Winner |
One-line why |
| Typography & layout |
Nano Banana Pro |
Ten months of shipped evidence on dense, multilingual type; 2.5's claim is two days old |
| Photorealism & lighting |
Draw |
2.5 claims sharper faces and product detail; NBP gives you actual lighting and camera controls |
| Instruction following & editing |
GPT Image 2.5 |
16 reference images, surgical edits, and multi-turn changes that don't drift |
| Speed & iteration |
GPT Image 2.5 |
Flare targets up to 50% lower latency; NBP's thinking pass costs it seconds per image |
| Resolution & formats |
Nano Banana Pro |
True 4K, 21:9 ultrawide, no 3:1 ratio ceiling — but no transparent backgrounds |
| Cost per image |
Split |
2.5 is cheaper below 2K; NBP is three times cheaper for top-quality 4K |
Final tally: two rounds to Nano Banana Pro, two to GPT Image 2.5, one draw, one split by resolution. If that reads like a fence-sit, keep going — the rounds themselves are decisive even where the aggregate isn't, and the last section explains why a single winner was always the wrong thing to want here.
You can test any of this yourself on the GPT Image 2.5 and Nano Banana Pro tool pages, which run the same prompt box and sit one dropdown apart.
Round 1: Typography & Layout
Text rendering is the single dimension where Nano Banana Pro built its reputation, and Google leaned into it. The launch materials promise "more accurate, legible text directly in the image in multiple languages," with a wider range of textures, fonts and calligraphy than the previous generation managed. That claim survived contact with the internet. Ten months of infographics, posters, mock magazine spreads and non-Latin script tests later, Nano Banana Pro is still what people reach for when the type has to be right.
Two things make it hold up beyond the headline. First, multilingual coverage — it handles scripts most models mangle, which matters if your poster has a Japanese subtitle or a Cyrillic band name. Second, layout reasoning: it doesn't just spell words correctly, it places them in a hierarchy that reads like design rather than like text that landed where it fell.
GPT Image 2.5 is going straight at this. OpenAI's pitch specifically calls out sharper small print and typography, and on the LM Arena leaderboards at launch, Sunburst took first at roughly 1421 with Flare second at about 1399, with the edit arenas showing the same order. That is a real signal. It was also, by the leaderboard's own labeling at the time, preliminary — those scores rested on something like 3,000 votes, a small fraction of what an established model accumulates. Elo on 3,000 votes moves, sometimes a lot.
So the honest position: OpenAI has made a credible claim to have closed the typography gap and there is early leaderboard evidence for it, but no body of shipped work yet confirming it holds on the hard cases — 9pt support-act names, a URL that must be character-perfect, a QR code drawn rather than pasted. Nano Banana Pro has that body of work. Until 2.5 accumulates one, the poster job goes to Google.
Point: Nano Banana Pro — for anything where a typo costs you a reprint, ten months of evidence beats two days of it.
Running score: Nano Banana Pro 1 — GPT Image 2.5 0
Round 2: Photorealism & Lighting
Here the two models are good at different halves of the same problem, which is why this round doesn't have a winner.
Nano Banana Pro's photoreal output has a well-documented character: strong skin texture, believable lens bokeh, fabric that folds like fabric. Reviewers consistently rate it at or near the top of mainstream models for material rendering. It also ships the thing most models don't — actual direction. You can specify camera angle, shift focus, apply a color grade, convert a daylight scene to night, add bokeh. Those aren't prompt tricks you hope land; they're controls, and they're why art directors reach for it on commercial work.
The documented flip side is that it skews clean. The "plastic skin" complaint follows Nano Banana Pro around — subsurface scattering pushed slightly too far, pores smoothed out, everything a half-stop too polished. Prompting explicitly for soft key light, natural pores and a gentle grade fixes it, but you have to know to ask. Out of the box it errs toward glossy, and glossy is the wrong default for a hoodie on an unmade bed.
GPT Image 2.5 aims squarely at that gap: OpenAI says the release improves sharpness on faces and products specifically, on top of already strong photographic polish, and the launch LM Arena standing supports a general quality lead. What it doesn't give you is Nano Banana Pro's control surface. No camera-angle parameter, no color-grade toggle — you describe what you want in prose and the model interprets.
Which you'd rather have depends on how you work. Want a hero shot right on the first try from a well-written prompt? 2.5's raw output quality is the better bet. Matching an existing brand look and dialing lighting until it fits? Nano Banana Pro gets you there in fewer attempts, even if its baseline render is slicker than you asked for.
Point: Draw — 2.5 has the better default render, Nano Banana Pro has the better steering wheel, and neither advantage cancels the other.
Running score: Nano Banana Pro 1 — GPT Image 2.5 0 (1 draw)
Round 3: Instruction Following & Editing
Editing is where GPT Image 2.5 makes its strongest case, and it's a genuinely different pitch from "our images look nicer."
Two specifics matter. The first is reference images: edits on 2.5 accept up to 16, against Nano Banana Pro's 14 blended inputs with resemblance held for up to 5 people. The counts are close enough that the number isn't the story — but 2.5 pairing 16 references with what OpenAI describes as strong subject preservation means you can throw a lot of visual context at it and expect the subject to survive.
The second, and the one worth caring about, is multi-turn stability. The classic failure mode in AI image editing is drift: you ask for one change, the model re-renders the whole frame, and something you liked quietly disappears. Three edits in, you're further from your target than when you started. OpenAI's claim for 2.5 is surgical edits that touch only what you named, and multi-turn sequences that hold their state. If that holds on your content, it changes the economics of the whole workflow — you stop re-rolling from scratch and start actually iterating.
Nano Banana Pro is not weak here. It does localized editing, it holds character resemblance across compositions better than almost anything else, and before September 8 it sat at the top of the LM Arena image-editing board — Google's Jeff Dean noted the 2K variant taking first place with standard Nano Banana Pro at second. If your editing problem is "keep these five people looking like themselves across a twelve-image campaign," it's still the specialist.
But for the more common problem — one image, six sequential refinements, none of which should disturb the others — 2.5 was designed for exactly that, and it displaced Nano Banana Pro at the top of the edit arenas on day one. Same preliminary-vote caveat applies.
Point: GPT Image 2.5 — surgical, non-destructive multi-turn edits are the concrete improvement in this release, and editing is where most working hours actually go.
Running score: Nano Banana Pro 1 — GPT Image 2.5 1 (1 draw)
Round 4: Speed & Iteration
This one isn't close, and the reason is architectural rather than incidental.
Nano Banana Pro runs a thinking pass. Before committing to a final image it generates interim drafts and reasons over them — a deliberate design choice, and part of why its layouts hold together. It's also why it's slow. Developers routinely report 20 seconds on a straightforward generation and upward of 50 when resolution and thinking level are both high. Dropping thinking_level to low reclaims some of that on simple prompts, but you're trading away the thing you were paying for.
GPT Image 2.5's Flare model is built for the opposite priority. OpenAI positions it as the fast default and claims up to 50% lower latency than GPT Image 2, with some launch partners reporting Flare running two to four times faster than the previous model at equal or better quality. Read "up to 50%" as a ceiling rather than an average — it depends heavily on quality tier and request size — but even the conservative reading puts Flare in a different responsiveness class than a model that thinks before it draws. Sunburst goes the other way, trading generation time for precision by design; if you swap it in and your latency climbs, that's documented behavior, not a regression.
The practical difference shows up in how you work rather than in a stopwatch number. At Flare speeds you explore — twelve variations while the idea is still warm. At Nano Banana Pro speeds you commit: write a careful prompt, wait, evaluate. Both are legitimate, but only one lets you brainstorm.
Point: GPT Image 2.5 — Flare is built for the loop where you're still deciding what you want, and Nano Banana Pro's thinking pass makes that loop expensive.
Running score: Nano Banana Pro 1 — GPT Image 2.5 2 (1 draw)
Both models say "4K." They mean different things by it.
Nano Banana Pro offers 1K, 2K and 4K as explicit settings, and its 4K is genuinely large — a 21:9 generation lands around 5632×3072 and roughly 24MB. The aspect ratio menu is broad: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. That 21:9 option is not a rounding detail; ultrawide is what a website hero, a cinematic still or a billboard crop needs.
GPT Image 2.5 caps the longest edge at 3840 pixels, requires both edges to be multiples of 16, and — this is the constraint that bites — limits the aspect ratio to 3:1 or narrower. 21:9 (2.33:1) is fine, but you're working inside a smaller envelope, and anything wider than 3:1 is simply unavailable. For a panoramic banner or an extreme letterbox, that's a hard stop.
2.5 wins one format capability outright, and it's a big one for a specific audience: native transparent PNG and WebP backgrounds. If you produce app icons, sticker packs, product cutouts or UI assets that have to composite onto an unknown background, that single feature outweighs every pixel-count argument here. Nano Banana Pro has no equivalent — the workarounds involve feeding it a transparent template and hoping, or cutting the background out afterward.
One more Google-side detail worth knowing before you commit: every Nano Banana Pro image carries an imperceptible SynthID watermark, and Google's free and Pro tiers add a visible Gemini sparkle mark that only Ultra removes. SynthID doesn't restrict commercial use, but it does make the image machine-detectable as AI-generated.
Point: Nano Banana Pro — more resolution and more aspect ratios win the round, though anyone who lives on transparent backgrounds should ignore this result entirely.
Running score: Nano Banana Pro 2 — GPT Image 2.5 2 (1 draw)
Round 6: Cost Per Image
This is the one dimension where I can give you exact numbers instead of vendor adjectives, because both models run on this site's credit pricing.
| Resolution |
GPT Image 2.5 Draft |
GPT Image 2.5 Standard |
GPT Image 2.5 Ultra |
Nano Banana Pro |
| 1K |
4 cr |
6 cr |
22 cr |
8 cr |
| 2K |
5 cr |
8 cr |
24 cr |
10 cr |
| 4K |
6 cr |
14 cr |
42 cr |
14 cr |
Read that table by column, not by row, because the two models are priced on different logic. GPT Image 2.5 gives you a quality dial — Draft, Standard, Ultra — and the spread between the ends is enormous. Nano Banana Pro has no dial. One price per resolution, and that price buys its top quality.
Three things fall out of this:
At 1K, GPT Image 2.5 is the cheap option. Four credits for Draft against Nano Banana Pro's eight. If you're generating thumbnails, testing compositions, or burning through variations to find a direction, 2.5 Draft costs half as much per attempt and returns results several times faster. For pure exploration this is not a close call.
At 4K Standard, they tie exactly. Both land on 14 credits. At that point the decision is entirely about which output you prefer, with price removed from the equation — which is a genuinely useful place to be.
At the top of each model's range, Nano Banana Pro wins decisively. GPT Image 2.5 Ultra at 4K costs 42 credits. Nano Banana Pro's 4K costs 14. That is a three-to-one gap for what is, in both cases, the best output each model offers at that size. Put it on the Standard plan — $29.90 for 300 credits — and the month buys you about 7 finished 4K images on 2.5 Ultra, or about 21 on Nano Banana Pro. Roughly $4.19 per image against roughly $1.40. Run that arithmetic against your own delivery volume before you standardize on Ultra for anything.
The API side tells the same story. Google lists Nano Banana Pro at roughly $0.134 per image at 1K/2K and $0.24 at 4K — flat, predictable. GPT Image 2.5 bills per token at $30 per million image output tokens, which public estimates put around $0.006 at low quality, $0.053 at high, and roughly $0.21 at max for a 1024×1024 image. Cheap at the bottom, comparable in the middle, expensive at the ceiling. One more API detail: GPT Image 2.5 shipped without Batch support, so if you were running overnight jobs at a batch discount, that lever isn't there yet.
Point: Split — GPT Image 2.5 owns the cheap exploratory end and Nano Banana Pro owns high-quality 4K delivery, so the winner here is whichever resolution your work actually lives at.
Final: Nano Banana Pro 2 — GPT Image 2.5 2, one draw, one split.
If You Need X, Choose Y
The scoreboard is a summary. This is the part you can act on.
A poster, infographic, or anything with more than a few words of type — Nano Banana Pro. Dense multilingual text at a readable size is what it does better than anything else on the market, with the track record to back it. Revisit in three months when 2.5 has more evidence behind it.
Transparent-background assets — GPT Image 2.5. Native transparent PNG and WebP output ends the argument; the Nano Banana Pro workarounds cost more time than the model saves.
Exploring, and you don't know what you want yet — GPT Image 2.5 in Draft at 1K. Four credits, fast turnaround, twelve directions in the time Nano Banana Pro takes to think through two.
One image edited six times without drifting — GPT Image 2.5. Multi-turn stability and 16 reference images are the specific problem this release was built to solve.
The same five faces consistent across a whole campaign — Nano Banana Pro. Google designed explicitly for resemblance across up to 5 people and 14 blended inputs, and character consistency is its longest-standing strength.
Final 4K commercial work on a budget — Nano Banana Pro. 14 credits against 42 for GPT Image 2.5 Ultra is not a margin you can reason away, and its lighting and color-grade controls are what you want at delivery anyway.
Why "Which Is Better" Is the Wrong Question Here
Every one of those recommendations is a job description, not a verdict. That's not hedging — it's what the evidence supports.
Asking "which is better, Nano Banana Pro or GPT Image" made sense when picking a model meant picking a subscription, learning an interface, and living with the consequences for a year. That isn't the situation now. On this site both models sit behind the same prompt box, take the same input, and are separated by a dropdown. Switching costs two seconds.
So the correct unit of decision is the job, not the year. The gig poster goes to Nano Banana Pro. The hoodie shot could go either way and you should try both. The forty exploratory thumbnails go to GPT Image 2.5 Draft at four credits each. The transparent product cutout goes to 2.5 because Nano Banana Pro can't make one. None of those commit you to anything, and treating them as a single loyalty decision costs you money on at least half of them.
There's a second reason to resist a clean verdict today: GPT Image 2.5 is two days old. Its LM Arena position rests on preliminary voting. Its editing claims are OpenAI's claims, not community consensus. Nano Banana Pro's strengths have been stress-tested by ten months of people trying to break them. Those are not equally reliable bodies of evidence, and an article that scores them as if they were is telling you something it doesn't know.
FAQ
Which is better, Nano Banana Pro or GPT Image 2.5?
Neither wins outright. Nano Banana Pro takes typography and high-resolution formats; GPT Image 2.5 takes editing and speed; photorealism is a genuine draw and cost depends entirely on your output resolution. Pick per job rather than picking a side — they're one dropdown apart on the same platform.
Is Nano Banana Pro still better at text than GPT Image 2.5?
Probably, but the gap is narrower than it was. OpenAI specifically targeted small print and typography in the 2.5 release and topped the LM Arena text-to-image board at launch, so the claim is credible. Nano Banana Pro still has ten months of shipped multilingual poster and infographic work behind its reputation, and 2.5 has two days.
How does Nano Banana Pro vs ChatGPT image quality compare for photos?
Close enough that prompt quality matters more than model choice. Nano Banana Pro renders skin, fabric and bokeh convincingly and gives you explicit camera and lighting controls, but skews glossy unless you prompt against it. GPT Image 2.5 claims sharper faces and product detail out of the box, with less direct control over the look.
Which one is cheaper per image?
It flips at resolution. GPT Image 2.5 Draft at 1K costs 4 credits against Nano Banana Pro's 8, but GPT Image 2.5 Ultra at 4K costs 42 against Nano Banana Pro's 14. At 4K Standard both land on exactly 14 credits.
Can GPT Image 2.5 generate transparent backgrounds?
Yes — it outputs transparent PNG and WebP natively, which is the main reason to pick it for icons, stickers, product cutouts and UI assets. Nano Banana Pro has no native equivalent, so you'd be removing backgrounds after the fact.
What's the difference between Flare and Sunburst, and which competes with Nano Banana Pro?
Flare is the fast default, targeting up to 50% lower latency than the previous generation; Sunburst trades time for precision on demanding creative work and edits. Flare is the one that beats Nano Banana Pro on speed, and Sunburst is the one that competes with it on final output quality.
Run Your Own Round Seven
Six rounds of specs and vendor claims narrow the field. They won't settle it, because your prompts aren't the ones anyone benchmarked.
Take a job you're actually working on — ideally something with both type and texture in it, since that's where these two pull apart hardest — and run the identical prompt on GPT Image 2.5 and Nano Banana Pro at the same resolution. Then edit each result twice and see which one holds. At 1K the whole experiment runs under 30 credits, which the free weekly check-in covers.
If it turns out you need both — and for mixed work, you will — the credit pool on the pricing page spends across every model here, so keeping both options open doesn't cost you two subscriptions.
Sources
- Nano Banana Pro: Gemini 3 Pro Image model — Google — verified 200: Google's own claims on multilingual text rendering, up to 14 blended input images, resemblance for up to 5 people, studio controls, 2K/4K output, and SynthID watermarking.
- Image generation — Gemini API docs — verified 200: Nano Banana Pro's 1K/2K/4K resolutions, supported aspect ratios including 21:9, reference-image limits, and the thinking mode that generates interim images before final output.
- Gemini API pricing — Google — verified 200: Gemini 3 Pro Image list pricing of roughly $0.134 per 1K/2K image and $0.24 per 4K image, plus image input cost.
- GPT-Image-2.5 Flare model reference — OpenAI — verified 200: six quality tiers including xhigh and max, $5/$8/$30 per million text-in, image-in and image-out tokens, inpainting support, and Batch listed as unsupported.
- GPT-Image-2.5 Sunburst model reference — OpenAI — verified 200: identical rate card and quality tiers, confirming Sunburst as the precision counterpart to Flare rather than a separately priced product.
- ChatGPT Images 2.5: Faster, more precise, but not the same for everyone — The Decoder — verified 200: per-image cost estimates at 1024×1024, the preliminary LM Arena scores and vote counts behind the launch rankings, and the 50% latency claim in context.
