GPT Image 2.5
Noise and Yellow Tint

If you zoomed into grass, hair or a studio backdrop and found a fine woven texture that shouldn't be there — you're not imagining it, and you're not the only one. The top-voted comment under the most careful launch-day test — 45 upvotes — was seven words long: "It's still got this weird noise to it."

We sell access to GPT Image 2.5, so the bias is declared. What keeps this honest: this page is sourced rather than self-tested. Every finding below is either OpenAI's own documentation, published research, or a named reviewer's hands-on test — each one linked. Where nobody has measured something, the page says so instead of filling the gap.

Three answers before you scroll. Is it fixed? Reduced, not gone, and it still finds the same surfaces. Is it the watermark? No — OpenAI's image watermark is SynthID and it sits in a layer built to be invisible. What makes it worse? Repeated edits, and large flat areas. Asking the model nicely does not help, and that one has been tested.

The surfaces it shows up on, according to two people who weren't comparing notes

The useful thing about this complaint is that it isn't vague. Two independent sources went looking and came back with lists — and the lists don't overlap by a single word, which makes what they have in common more interesting than if they had agreed.

The model excels in many ways but there's a huge weakness with the noise artifacts or whatever that is very noticeable on certain surfaces like grass, hair, vegetation.u/JoshSimili, r/OpenAI — 8 upvotes

a persistent fine texture that crops up in skies, studio backgrounds, and low-light areasEric Hal Schwartz, TechRadar — four tests of his own

It's not bad; but I still have a bone to pick for their pixelated feel around the panther and animals.u/DependentOriginal413, r/codex — 9 upvotes

Seven surfaces across three threads, barely a word in common, one property in common: every one of them is a large area that is supposed to read as smooth and low-contrast. Grass and hair aren't smooth, but they're the other end of the same problem — fine repeating detail with no strong edges to anchor to.

That is the sentence to keep, because it is also the answer to the next section. A watermark cannot be picky about what sits underneath it. This is picky.

Is the noise the watermark?

This is the question we kept seeing asked and never answered:

the noise patterning thing they do, which I assume is that watermark, looks just as bad as the previous version.u/MultiMarcus, r/OpenAI — 3 upvotes

It is a fair suspicion, and the reason it survives is that OpenAI ships two different provenance signals with every image and both get called "the watermark". They are not the same thing and they do not live in the same place.

The first one can't be it, by definition

C2PA is metadata. OpenAI describes it as signed information that travels alongside the file, and notes that "editing, converting, or sharing a file can remove its metadata". Strip it and not one pixel changes — that is what makes it metadata rather than a mark. So whatever you are looking at in the grass, it is not Content Credentials.

The second one is designed to be invisible — and to survive compression

SynthID is the pixel-level one, so it is the honest version of the question. Two things about it settle the matter. First, invisibility is not a marketing word here, it is the specification: OpenAI describes SynthID as embedding "a signal directly into generated media" that is imperceptible.

Second — and this is the part that does the work — the whole point of SynthID is durability. OpenAI's position is that the watermark "may survive some transformations" like resizing, compression and screenshots, precisely because metadata does not.

Now hold those two facts against what you are seeing. A signal engineered to survive heavy JPEG compression cannot also be the first thing you notice on a clean render. Visible texture and compression-proof signal are close to opposites: the more robust a watermark is, the less it looks like anything.

And the texture does something a watermark can't afford to do

A watermark has one job: be there when someone checks. To manage that across crops and re-encodes it has to be spread over the whole frame — which means it cannot care what is underneath it. The grain cares. Go back to the two lists above: six surfaces, no shared word, all of them large low-contrast regions. It shows up in the sky and not in the subject's jacket. A mark that skipped the jacket would be a bad mark.

It is worth saying that somebody in those threads had already worked this out and got two upvotes for it — which is roughly what being right and early pays on Reddit:

It's commonly referred to noise. It's an artifact of the diffusion process.u/solus42666, r/codex — 2 upvotes

The literature has a name for it, and it isn't "watermark"

There is a 2026 paper on this exact artifact — Mi-Ripple: Restoring Images Degraded by Iterative AI Editing. Two things in it are worth knowing even if you never read the rest.

• It attributes the texture to the editing process: "Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple."
• It splits the thing into two mechanisms rather than one — periodic lattice artifacts on one side, content-entangled granular texture on the other.

That second phrase is the whole argument in three words. Content-entangled is the opposite of uniformly applied, and uniformly applied is what a watermark has to be.

Does a higher quality tier help?

Honest answer first: we have not run a tier-by-tier measurement, and neither has anyone else publicly. Anyone showing you a curve on this is either publishing their method or guessing. Here is what is known.

GPT Image 2.5 added two tiers above the old ceiling — xhigh and max — on top of low, medium and high. OpenAI positions Sunburst as the higher-quality model and Flare as the faster one, with quality comparable to GPT Image 2.

Reviewers who ran both at the top settings report what the community reports: reduced, not gone. One creator testing Flare against Sunburst at max quality still saw the pattern return on film-still-style frames.

So the tier is worth raising if you are already unhappy — but treat it as a dial, not a fix, and do not pay for max on the assumption that it removes this. What each tier costs is a separate subject.

The yellow tint is a separate complaint

Two things get blurred together because they turn up in the same picture. They are not the same problem and they do not have the same cause.

I see they still haven't got rid of the piss filter or weird textures.u/honkballs, r/OpenAI — 20 upvotes

four versions in and they still cant kill that grain and the yellow tint, kinda wildu/presentofai, r/OpenAI

Grain is a texture — it is in the pixels' relationship to each other. Tint is a colour cast across the whole frame. Notice that both quotes name them as two items in a list, not one thing described twice.

There is a practical difference between them too, and it came out in the same thread. One commenter's read was that the cast is a default you can talk the model out of, while the texture is not:

Piss filter is the default, you can have it just not use it in the prompt (it will always have a default). Weird textures is the issue. It's noise and it's an ongoing issue.u/Grand0rk, r/OpenAI — 5 upvotes

Treat that as a lead rather than a finding — we have not tested it. But it matches the shape of the two problems: a colour decision is the kind of thing a prompt can reach, and a texture produced during generation is not.

We have not put a number on the tint, and we would rather say so than publish one we have not measured. What we can tell you is where it bites: neutral greys and white product backgrounds, the two places a cast has nowhere to hide. If you are shooting packshots against white, check that before you worry about the grain.

What makes it worse, and what to stop trying

Stop asking it nicely — that one has been tested

The most useful negative result out there comes from a reviewer who set out to prompt his way around it. He explicitly asked for smooth gradients and for visual noise to be eliminated, then generated four images. The grey studio background still came back with "a persistent granular texture", and a pale blue sky came back textured rather than "the completely clean gradient I would expect".

So prompt-level pleading is not a fix. Worth knowing before you spend an afternoon rewording.

Stop stacking edits on the result

This is the one with a mechanism behind it rather than a vibe. The paper named above puts iterative editing in its title, and the community observation matches: "The more it's edited the more obvious it becomes." Editing from the original rather than from the last output is the single biggest thing in your control.

Compose around it

It concentrates in large, flat, low-contrast regions. If a plain studio sweep or an open sky is where it shows, then a background with some legitimate structure in it — a gradient with an object in it, a surface with real texture — gives it less room to be the most interesting thing in the frame. This is art direction, not a setting. It is also the only advice here you can act on in the next thirty seconds.

Better than GPT Image 2, or the same?

Better, and not by enough to close the thread. Two independent hands-on reviews land in the same place: the grain "appears reduced but not fully eliminated", and a second tester puts it as reduced compared to Image 2 but not gone, with the pattern resurfacing on film-still frames.

That matches the community read, which is worth noting because the two groups disagree about almost everything else in this release.

Why it gets worse the more you edit

That observation now has a paper behind it. Mi-Ripple is a 2026 study of this exact artifact, and its subject is in the title: images degraded by iterative AI editing. Its finding that matters to you is that cleaning the reference image before regenerating cut output debris density by 45%.

Which points at the mechanism. Most tools feed the previous result back in as the next input, so whatever texture round one produced is baked into the source for round two, and round three inherits both. The artifact is not just reappearing each time — it is being fed back in.

Here, every edit re-renders from your original file rather than from the last result. Damage cannot accumulate across turns because there are no turns to accumulate across.

To be exact about what that does and does not buy you: it does not remove the grain from any single render. It stops it compounding. If you have been reaching for Photoshop after two edits, compounding was the part costing you the afternoon.

Does it show up in print?

Usually not, for a boring reason: the texture is fine-grained, and print resolution eats fine grain. At 300 dpi a 3840-pixel file lands around 13 inches wide, and at that density a per-pixel texture sits below what the eye resolves at normal viewing distance.

Where to be careful is the case that made you notice it in the first place — a large flat area. A poster with a big sweep of sky, viewed close, is the one place it can survive the press.

How this page was checked

This page is sourced, not self-tested, and that is a deliberate choice rather than an omission. The watermark question is answered by OpenAI's own documentation, which turns out to settle it completely — nobody had gone and read it. The surface lists come from two people who were not comparing notes. The mechanism comes from a peer-reviewable paper. Where a number would need our own lab run, this page says so rather than inventing one.