OpenAI shipped ChatGPT Images 2.5 on September 8, and the pitch was specific: sharper details, richer textures, more natural lighting, and editing that actually respects what you didn’t ask it to change.

The company says image-generation latency dropped by as much as 50% compared with Images 2.0, and two new models—GPT-Image-2.5 Flare and GPT-Image-2.5 Sunburst—now sit in the API, with Flare as the fast default and Sunburst built for premium editing precision.

It’s the direct sequel to a comparison we ran back in May, when GPT Image 2 and Nano Banana 2 traded wins across eight categories. GPT Image 2 took more of them but suffered from a distinctive oversharpening artifact whenever a prompt piled on too many constraints. That review closed with a specific question hanging over it: would OpenAI’s next model fix that problem?

So we ran it back.

Same evaluator’s eye, six categories carried over or adapted from earlier rounds, same prompts fed to both models. Google’s side of the test uses Nano Banana 2—Google’s name for Gemini 3.1 Flash Image—not the slower, more deliberate Nano Banana Pro.

This is a same-tier matchup: OpenAI’s new fast-and-precise model against Google’s fast-and-precise model.

What actually changed in GPT-Image 2.5

OpenAI’s image models have a pattern of shipping with a signature flaw. The model behind the original GPT Image 1 picked up a persistent warm, yellow color cast that the internet nicknamed the “piss filter”—a tint OpenAI never fully explained and never fully fixed.

GPT Image 2 traded that problem for a different one: feed it a prompt with too many stacked constraints and it would oversharpen the output into a crunchy, over-processed mess, thick with artifacts.

Neither flaw showed up in this round. Every image we generated with Images 2.5 held its color balance and its detail at full complexity, without the yellow cast or the crunchy over-sharpening that marked its two predecessors. That’s a real, visible fix, and it shows most clearly in the steampunk and portrait tests—both are the most photographically coherent ChatGPT outputs we’ve tested across three model generations.

OpenAI also added product features that don’t show up in a side-by-side image test but matter for workflow: Sketch, which lets you draw a rough layout directly in ChatGPT as a generation reference, prompt sharing, inline comments on specific image regions, and format templates for posters and merch. On the API side, quality tiers now run from low up through a new high and max, above where Images 2.0 topped out.

Here is how OpenAI’s new model stacks up against Google’s best offer.

Lettering density: the Kellerman’s Hardware scene

This is the punishing one—a gritty 2 a.m. intersection where nearly every surface carries readable text: a ghost sign, spray-painted graffiti, vinyl storefront lettering, a torn concert poster, a stenciled curb, and a sticker-covered payphone.

Nano Banana 2 rendered almost everything cleanly. The one slip is a payphone sticker that duplicates its own text in a garbled way—a minor, easy-to-miss flaw.

ChatGPT Images 2.5 added a detail Nano Banana skipped entirely: a lamppost with overlapping stapled flyers, torn and weathered, exactly as the prompt described.

But the model’s own street-art tag reads like “STILLL HERE” with an extra L, and the apostrophe in “KELLERMAN’S” on the ghost sign is missing or unreadable. For a model whose entire pitch is precision text rendering, two separate legibility slips in one image is a real miss.

Aesthetically speaking, OpenAI’s model generated a more realistic scene, but in terms of text it didn’t pay the same amount of care. Even if the image seems better, the text generation wasn’t as good as Google’s.

Winner: Nano Banana 2.

Spatial awareness: The steampunk clock tower

A demanding aerial composition test: a five-plane depth scene with a massive clocktower whose faces need to show different times in legible Roman numerals, plus six other text elements scattered from foreground to background.

ChatGPT Images 2.5 produced the more atmospheric image by a clear margin—visible steam rising off rooftops, a river cutting through the mid-ground, richer tonal range across the five depth planes. The letters are also easier to read in this generation.

Nano Banana 2’s atmosphere is flatter, but its two visible clock faces both render legible Roman numerals—XII, III, VI, IX—at similar hand positions which was not what the prompt asked for.

Winner: GPT Images 2.5, on the strength of following instructions better without sacrificing realism.

Illustration: The anime spirit medium

This prompt asks for a Studio Ufotable-style key visual: a girl mid-transformation into spiritual energy at a torii gate, with a nine-tailed kitsune fox and a Makoto Shinkai-painted twilight sky.

ChatGPT Images 2.5 delivers the best sky of any test we’ve run in this whole series—an actual sun disc, water reflection, and mountain silhouette that genuinely earns the Shinkai comparison. The asymmetric eyes land well too. Where it drifts from the prompt is the “dissolving into energy” instruction, which the model reinterprets as an electric-crackle effect running through her hair rather than the flowing, translucent dissolve described.

Nano Banana 2’s wispy blue-white energy trail is the closer literal match to that specific line. Neither model rendered a convincing nine-tailed fox.

Winner: ChatGPT Images 2.5, on overall visual impact, even with the interpretive drift.

Realism: The rooftop architect

A cinematic portrait with a long list of independent constraints: beige trench coat, round glasses, blueprints held in the left hand specifically, golden-hour lighting, shallow depth of field, film grain.

ChatGPT Images 2.5 produced gorgeous light—the sun disc is visible directly behind her, and the skin micro-texture is excellent. The skin is too smooth, but the overall scenery is very realistic, like taken with an analog camera.

Interestingly enough, adding some commands that would ordinarily make the photo bad quality actually end up increasing its realism. Here is a generation after adding keywords like “realistic, highlights, crushed shadows, uneven flash, blown out skin tones, candid moment, shot on a phone camera.”

Nano Banana 2 keeps the fuller composition, with right-hand placement instead of left-handed, and adds a legible blueprint label—”PROJECT: 124 DUANE ST”—that most renders skip entirely.

Winner: Nano Banana 2, for one shot, GPT for repeated iterations.

Agentic research: The Bitcoin timeline

Both platforms can research a topic before rendering it, so we asked for a widescreen Bitcoin history timeline in kids-drawing style, with a strict bar on factual accuracy. This is the category that mattered most for the comparison, and it’s where the biggest gap showed up.

ChatGPT Images 2.5 built a clean two-row infographic with specific dates throughout—and one of them is wrong. It labels 2023 as the year Bitcoin ETFs were approved in the U.S. The SEC actually approved the first spot Bitcoin ETFs on January 10, 2024—a full year later, even though the futures ETFs were actually approved that year, so it may be an interpretation issue.

Nano Banana 2’s output is less structured but portrays similar events, which says something about how little variance the model shows on repeated prompts. That said, it hedges the ETF approval and the fourth halving into a “2023–2024” bracket instead of asserting a single wrong year, so nothing it states is technically false.

Winner: Nano Banana 2. A confidently wrong date matters more in a category specifically testing whether “agentic reasoning” produces accurate output.

Abstract concepts: A prompt made of invented words

This one used a prompt built almost entirely from words that don’t mean anything: “A woman eating shmfiyxl in Lyxin. Next to her, her Lymglsushing plays Lakishkark.” There’s no dictionary definition for the dish, the venue, the companion, or the game—each model has to invent one and then decide how to show its work.

ChatGPT Images 2.5 solved it by writing the nonsense straight into the scene as text. “Lyxin” glows on a neon sign above a sleek, futuristic restaurant. “Lakishkark” is printed on the box of a board game the woman’s alien tablemate is playing. The made-up words stop being abstract the moment they become signage you can actually read.

Nano Banana 2 took the opposite approach. It built a warm, culturally specific scene—a Guatemalan market stall, a woman in a traditional huipil, an orc-like creature playing a hybrid stringed-and-pipe instrument—and read “plays” as playing music rather than playing a game, a different but equally valid interpretation. Nothing in the frame ties back to “Lyxin” or “Lakishkark” specifically, though; the invented words never surface as visible text anywhere.

Winner: ChatGPT Images 2.5. Turning undefined concepts into legible labels is a more literal answer to a prompt that gives it nothing else to work with.

The verdict

Nano Banana 2 won three of six categories in this round, so it’s basically a tie. It will all depend on your expectations and the way you interact with the model. ChatGPT Images 2.5 also fixed the oversharpening problem that dogged its predecessor, and its illustration output this round is arguably the most visually striking single image either model has produced across two full rounds of testing.

What separates the two isn’t overall quality but very tiny specific things: checkable misses, a spelling error in a lettering-heavy scene, a wrong year in a research-driven infographic, etc.

But in terms of overall aesthetics and quality they are pretty much on par.