GPT Image 2.5 vs Nano Banana Pro: Where Each One Wins

We sell one of these two, so the bias is declared. What keeps this honest: the two jobs Nano Banana Pro wins are named before anything we win, one of those we lose badly enough that we say so in the first table, and where three independent reviewers disagree with us we cite them rather than argue.

And what this page is not: a same-prompt gallery. We have not finished running one, so nothing below is presented as a test result. Every claim here comes from one of three places — the two vendors' own documentation, published third-party reviews, or community reports we counted rather than sampled — and each one says which.

The short version is that "which is better" is the wrong question. A single score would average away the two rows you most need to see before paying anyone.

GPT Image 2.5 vs Nano Banana Pro: which one for which job

Two of those rows are unresolved on purpose. Text rendering is the claim both vendors lead with and neither of us has settled it — see the technique that moves both further down, which is more useful than a verdict. Character consistency is improved on both and guaranteed by neither.

There is no overall score in that table and there will not be one. These two lose to each other on different jobs; the row you care about is the only one that matters, and a single number would hide the first two rows entirely.

Two jobs Nano Banana Pro wins

If either of these is what you do all day, Nano Banana Pro is the better tool, and we would rather you found that out here than after paying us.

Large soft textures. This one isn't close.

GPT Image 2.5 still produces a faint checkerboard pattern across broad soft areas — clouds, grass, hair, and behind dense text. It was the single most consistent complaint in the first 24 hours after launch: nine separate people across four subreddits, and 2.5 did not fix what 2 had. One user drew the comparison directly:

It's not a watermark, Nano banana also have synth id and does not have this issue.Launch-day thread

gpt-image-2.5-sunburst · medium · 1536×1024

Two more, from people who went looking: "Have they fixed the checkerboard artefacts? Edit: No they have not." and "The more it's edited the more obvious it becomes. Once you notice it you'll see it everywhere." The second one matters more than it looks — it means a single-pass test will not show you the problem, and an edit chain will. What didn't change from GPT Image 2 has the rest of it.

Photorealistic portraits — the independent reviews lean the other way

We are not the only people testing this. Three independent 2026 round-ups landed on Nano Banana Pro for photorealism. We are not going to argue with three independent reviewers on a subjective call — a vendor claiming a win on a judgement it is not qualified to make is how a comparison page loses the reader on the row after it. Generate a portrait on both and decide for yourself; it is the kind of question a personal test settles better than anyone's table.

Text inside the image: GPT Image 2.5 vs Nano Banana Pro

This is the claim both vendors lead with. Google leans on Nano Banana Pro's reasoning engine for layout; OpenAI ships an eight-rule prompting guide for it. We have not settled which wins, and the useful thing is that the technique below moves both — usually by more than the gap between them.

• Put the exact words in quotes. Not "a sign saying the shop is open" but a sign reading "OPEN". Quoted strings get treated as literal content rather than as a description of content, and the change in hit rate is larger than the difference between the two vendors.
• Spell unusual brand names letter by letter. An invented word has no token the model has seen spelled correctly. Writing K-A-L-E-I-D-O is the difference between a logo and a plausible misspelling you don't notice until it is printed.
• Say "no text other than what is specified". Unprompted captions, invented logos and fake watermarks are the most common thing nobody asks for and everybody gets. One clause removes most of it.
• Compare tiers before you commit on small type. OpenAI recommends the higher quality tier specifically for small text. On anything with labels under about 20px this is one of the few places where the tier reliably changes the outcome rather than only the price.

gpt-image-2.5-sunburst · medium · 1536×1024

All four carry across vendors, which is the point of listing them instead of declaring a winner. Write one and see.

Changing one thing without wrecking the rest

A paying ChatGPT user described the requirement better than any spec sheet: "Existing image → preserve it → make one requested change → preserve everything else." This is the row GPT Image 2.5 is documented to have improved on, and it is also the row where most of the damage is done by how a tool calls the API rather than by the model.

Chained editing is where quality goes to die

Each pass re-encodes the pass before it. Edit four times and you are four generations away from your source, carrying every artifact the previous three picked up — including, on GPT Image 2.5, the checkerboard from the section above. This is true of both vendors and of every tool in the category. It is a property of chaining, not of a model.

Re-rendering from the original avoids it entirely

Keep the uploaded file, merge everything asked for into one instruction, and render from the source every time. Edit number four is then computed from the same pixels edit number one was. That is a claim about how the API is called rather than about the model, which is why it is portable to whichever one you pick — and it is how the editor here does it.

C2PA vs SynthID: what's in the file either way

Neither one stamps a logo across your picture. Both write a provenance signal inside the file instead — C2PA content credentials on one, an invisible SynthID watermark on the other — added by the model vendor rather than by whichever site you generated on. What differs is who can read it back, and how easily it survives a crop or a re-save.

Switching to GPT Image 2.5 from Nano Banana Pro, and when not to

Four things this front end gives you that Nano Banana Pro's does not, and one honest reason to stay where you are. The four are the model picker, the receipt under every result, the Keep-Intact list on edits, and per-image credits instead of a flat monthly count. The reason to stay is below them, and it is a real one.

• You can pick the model. Flare for speed, Sunburst for the quality ceiling, same price either way. Neither ChatGPT nor Codex exposes that choice; only the API does, and this is a front end for it — Flare or Sunburst.
• A preserve list. Tick what must not change — identity, framing, text, lighting, product geometry — and it goes into the prompt as OpenAI's own change only X construction. Four sentences of boilerplate nobody types every time, turned into five checkboxes.
• Failed runs cost nothing. A run that errors, times out or is refused by moderation is refunded to the credit it came from, in the same request — you are never billed for a picture you did not get.
• A receipt under every image. Model ID, quality tier, pixel size, output format, and elapsed time, read straight out of the API response rather than typed in by us.

Where each one sits on the Image Arena

This is the one claim on the page you cannot verify from your own account, so treat it differently from the rest. Arena standings are a blind human-preference vote run by a third party: the least gameable number in this field, and still a vote rather than a measurement. Everything else here you can reproduce with one prompt.

Going from 1,381 to 1,421 isn't 'destroying' the benchmark they previously set — it's an incremental gain.A commenter on the announcement thread

Plenty of people cannot see the difference at all yet, which is a legitimate position. Blind-vote Elo is not the same thing as better for your job — which is why the table at the top of this page is organised by job and not by rank.

How this page was checked, and what's still owed

• Vendor claims are quoted, not paraphrased. Where OpenAI's own wording decides something — including "the same quality label does not imply the same image quality or response time across models", which is why nothing here matches quality labels across the two vendors — the sentence is reproduced word for word.
• Community reports are counted, not sampled. The checkerboard finding is nine separate people across four subreddits inside a day of launch. One report is an anecdote and nine is a pattern.
• Where reviewers disagree with us, they win. Photorealism is a subjective call and three independent round-ups went the other way. It is recorded as their finding, in their direction.
• Prices are read on a stated date. Both vendors quote per output token and neither rate is stable enough to publish without one.