ChatGPT Images 2.5, Explained

OpenAI shipped ChatGPT Images 2.5 on September 8, 2026, and within a day the feeds were full of it. The interesting part is what people are already making with it — but first, the short and accurate version of what this release actually is, because most of the coverage gets two things wrong.

What it is

One release, two names. Inside ChatGPT it’s just ChatGPT Images 2.5 — no model picker, you generate an image and you’re using it. In the API it ships as two models: GPT-Image-2.5 Flare, the small fast one, which OpenAI says matches GPT Image 2’s quality at roughly half the latency, and GPT-Image-2.5 Sunburst, the base model, which OpenAI says beats GPT Image 2 on quality but takes longer. There is no single model called “gpt-image-2.5”.

What changed — and what didn’t

This is a control release, not a resolution release. Every hard limit people assume moved — image size, reference count, output shapes — sits exactly where it was. What improved is restraint: swapping one element while the subject, composition and surroundings survive, and holding that steady across a long editing conversation.

The thing nobody claims: there is no line anywhere in OpenAI’s announcement about better text or typography, and their developer docs still list text rendering as a limitation for GPT Image models generally — “the model can still struggle with precise text placement and clarity.” Keep that in mind when you read the demos below, because this is exactly where real-world testing gets interesting.

What it costs

The per-token price didn’t change. What changed is that the tier names mean different things now:

API cost per 1024×1024 image, from OpenAI’s pricing calculator.

2.5’s max is what 2’s high used to be. Send the same high request to the new model and you pay a quarter as much — for a lower tier than you got before.

What OpenAI is showing off

OpenAI opened the announcement with a number worth pausing on: people create more than 3 billion images a week across ChatGPT Images and the GPT-Image API models. The demo set they built around 2.5 is almost entirely about editing rather than generating — Remixed baby portrait, Dog in costume, Headshot from photobooth, Composite party photo and Making the bed for fidelity; Full-body edits and Multi-city ticket for precision; Cube rotation, Travel infographic and Birthday candles for multi-turn consistency.

Look at that list again: nearly every one starts from a photo you already have. Then independent testers got hold of it, and the picture got more interesting.

The one edit that proves the point

Simon Willison ran the cleanest demonstration of what “precision editing” actually means. He took a real line chart — OpenAI’s own internal coding-agent usage data — and sent a single instruction to gpt-image-2.5-sunburst:

add a raccoon scientist studying the chart thoughtfully

The raccoon shows up. The axes, gridlines and data curve survive intact. That is the entire pitch in one image: you can add something to a picture without the model quietly rewriting the parts you needed to keep.

Speed is the change people actually feel

The most convincing number of launch day didn’t come from OpenAI. A developer building an AI UI-design tool posted his production baseline on Hacker News:

“I've generated around 50k images with gpt-image-2 via api, the average latency has held at around 104s… images are coming in at around 35-40s.”

Fifty thousand images is a real baseline, not a benchmark. 104s down to 35–40s is a larger gain than OpenAI’s own “up to 50%” claim, measured on a workload he was already running.

Where it lands on the leaderboards

Arena's community voting put both new models on top within a day — with a caveat almost nobody is repeating:

Both 2.5 entries are still flagged Preliminary. Sunburst's text-to-image score rests on about 3,100 votes; GPT Image 2's sits on more than 78,000. Two orders of magnitude separate them, which is why the error bars are three times wider. First place on day two is a signal, not a settled result — check back in a few weeks.

What the launch partners say

Four companies shipped with it on day one, and their quotes are more specific than the announcement copy.

Higgsfield AI's Head of Product, Axultan Alimkulov, put his finger on the same thing Simon Willison's raccoon demonstrated:

“What impressed us most about GPT‑Image‑2.5 Flare is how well it understands what not to change. You can make a meaningful edit without losing the character, composition or visual identity of the original image.”

Manus is the only partner who published a number:

“In our evaluations, Flare delivers high-quality images at two to four times the speed of GPT‑Image‑2. With improved transparent-background generation, it is an excellent fit for creative work.”

Worth noting those two speed figures — OpenAI's “up to 50% lower latency” and Manus's “two to four times” — come from different tests under different conditions. They don't contradict each other, but they aren't interchangeable either.

Adobe confirmed the models are landing in Firefly, and Runway's Chief Creative Officer, Jamie Umpherson, summarised the appeal as “low latency and outstanding quality… it fits seamlessly into how creators already work.”

Two new ways in: Sketch and Templates

These arrived alongside the model and are ChatGPT-only — they're interface features, not model capabilities, so they don't exist in the API and no other platform inherits them by adding 2.5.

Sketch lets you draw the thing you can't describe. You type @Sketch, get a drawing canvas, scribble a rough shape, and the model treats your drawing as a reference rather than something to trace over.

Templates gives you starting points — “Poster,” “Merch” and similar — where you fill in what the piece needs to say instead of writing a full prompt. It isn't available in Work mode yet.

TechRadar's reviewer rated the new editing toolbar — markup, comment, remove background, erase, resize — as the feature he'd actually keep using, above the model upgrade itself.

Multi-turn editing, stress-tested

The claim that edits stop degrading over a long conversation is the hardest one to verify from a demo, so OpenAI built a deliberately boring test for it: rotate a cube, over and over, and watch whether it stays the same cube.

One Hacker News commenter pushed back on exactly this: a cube is a simple object, and “for complex scenes — people, wide environments — edited many times over, whether it holds up as well as this example, you'd have to test.” Fair. Independent creator Paul Solt ran a 150-frame version inside Codex and walked through where it does and doesn't hold:

Text rendering: the claim nobody made, and nobody proved

Remember that OpenAI never said 2.5 renders text better. The independent testing bears that out — and this is the most misreported part of the launch.

Matthew Berman ran the only systematic, unsponsored A/B comparison I could find: six posts putting both models against the same layout prompts. 2.5 won on readability, but his own summary is blunt:

“gpt image 2 could already spell. but the bar is raised for composition, semantic understanding, props, layout heirarchy, negative space.”

That is a different claim from “text got fixed.” The gain is layout intelligence, not spelling.

Evidence pointing the other way exists too. The same developer with the 50,000-image baseline noticed the fine stuff got worse: “the microglyphs… they're kinda blurry / not straight.” A Reddit tester got Mercantile rendered as Merchantile on a book cover. And the accounts posting about “flawless text rendering” were largely running paid partnerships — one enthusiastic follow-up let the real boundary slip: “clean, readable, 3-4 words max.”

That last phrase is the honest summary of the whole topic. Short text is reliable. Dense text isn’t. Neither of those really changed in 2.5. If your work is a poster with a headline, you’re fine. If it’s a menu, a spec sheet or a course page, budget for a human typesetting pass.

Prompts worth stealing

The best things people posted on launch day weren't elaborate. Three that are worth copying, all from testers who published their exact input:

1. Add something without disturbing the data. Simon Willison's chart edit is one line against a reference image:

add a raccoon scientist studying the chart thoughtfully

The value isn't the raccoon — it's the test. Feed it a chart, a screenshot or a diagram where you'd immediately notice corruption, and you learn more about a model's editing discipline in one shot than from any gallery.

2. The oldest benchmark in the business. One Hacker News user went back to the prompt from Stable Diffusion's Wikipedia page:

a photograph of an astronaut riding a horse

then pushed it to “horse on the moon.” It took him several tries earlier in the day before he got there — a useful reminder that the first attempt failing doesn't mean the model can't.

3. Turn a phone photo into a menu shot. Another commenter took a genuinely unglamorous photo — a cheeseburger meatloaf with potato wedges, shot on a phone — and asked for menu-grade photography without changing what was actually on the plate. That constraint is the whole trick, and it's the version of “product photography” that doesn't misrepresent the product.

The pattern under all three: start from something real, change one thing, and pick a subject where you'd spot a lie immediately. That's the workflow 2.5 is built for, and it's a better way to evaluate it than generating fresh images from scratch.

Where it still falls down

Launch-day Hacker News is the least flattering and most useful reading available on this model.

Edit fidelity is still not best in class. One developer: “I've also found the OpenAI image models to lose fine detail on image edits compared to Nano Banana or Flux models which faithfully retain input source image geometry and details… it sounds similar to previous OpenAI image models.”

The noise-gradient artifact isn’t fixed. Asked directly whether it had been, a commenter pointed at OpenAI’s own demo video: “doesn't look like it, looking at the motocross photo.”

The official demos don’t survive close reading. On the composite party photo: “the minuscule details are being lost, like the teeth structure of the guy in the middle or the fact that the guy on the left is holding the cup with three fingers.”

And on the very first demo image, a child in costume: “The shoulders of ChatGPT's output are just wrong… it's not him.”

Moderation is still the loudest complaint, carried over unchanged from 2.0 — one user notes that simply rendering a gun in a scene gets the request flagged.

One myth worth killing early

A widely-shared post claimed 2.5’s API rates are twice those of GPT-Image-2. They aren’t. Both 2.5 models bill at $30 per million image output tokens, $8 per million image input and $5 per million text input — the same rates GPT-Image-2 carried before launch, as OpenAI’s pricing page shows. What changed is the token count per quality tier, which is why a like-for-like request gets cheaper, not more expensive.

The 24-hour verdict

TechRadar’s Graham Barlow spent a day with it and landed roughly where the careful testers did:

“All in all these improvements are welcome, if not groundbreaking. I can see why they didn't call this Images 3.0.”

That reads about right.

Where you can use it today

Images 2.5 rolled out to ChatGPT on every tier — free and paid — across web, iOS and Android, with existing image-generation limits unchanged. In the API, both Flare and Sunburst are live with no waitlist. Several third-party platforms picked it up on day one.

When is it coming to Renoise?

Not yet — and we’d rather say that plainly than imply otherwise.

Renoise runs GPT Image 2 today: up to 4K output, eight aspect ratios, and up to 16 reference images fused on one canvas, sitting next to Nano Banana Pro, Seedream and the rest of the lineup so you can move an image between models without leaving your project. We plan to add 2.5 as soon as we can, and we’ll update this post the day it lands. We’re not going to promise a date we don’t control.

To be clear about sourcing: every spec above is OpenAI’s, from their announcement, model pages and pricing calculator — none of it is a Renoise capability claim.

See it for yourself

Every claim above comes from something you can open:

• OpenAI — Introducing ChatGPT Images 2.5 · model pricing
• Simon Willison — the raccoon-on-a-chart edit
• Matthew Berman — six-post A/B comparison against GPT Image 2
• Hacker News launch thread — 50,000-image latency baseline · blurrier microglyphs · edit fidelity vs Nano Banana and Flux · noise gradient · detail loss in the party photo · the costume demo · moderation
• TechRadar — Graham Barlow’s 24-hour hands-on
• Arena — text-to-image leaderboard · image edit leaderboard (both 2.5 entries still marked Preliminary)

Try GPT Image 2 in Renoise