It's 11 p.m., and Xiaojie's screen is still glowing. The new product poster is supposed to go live Monday, and the hero image has already been regenerated seven times.
What she wants isn't complicated: move the skincare bottle onto a brighter, more polished background, and leave everything else exactly alone — the logo can't shift, the ingredient text on the label can't blur, and the reflection has to look natural. But every round of edits, the AI hands back a new image that has quietly moved something it wasn't supposed to touch: the logo tilts a few degrees, the cap's highlight changes shape. She has to hunt for what broke, redo the prompt, and start over. The night isn't going toward "adjusting" the image. It's going toward "redrawing" it, again and again.
Call her Xiaojie — she's a visual designer at a skincare brand, but you can drop yourself into her seat too, as long as you've lived through the same thing: every edit feels like a bet, and you never know if this round will quietly undo the one detail you finally got right last time.
She always assumed this was just how AI image generation worked
Xiaojie didn't think she was doing anything wrong. She just assumed this was the deal: every edit is a new roll of the dice. Sometimes you get close to what you wanted, sometimes you don't and you start the whole thing over. Wanting multiple rounds of iteration and wanting the details you'd already locked in to stay locked in sounded like asking for too much — you don't get to have both.
She had no idea that exact "asking for too much" was the first thing OpenAI's announcement addressed.
What OpenAI just announced happens to be the exact thing she was fighting last night
OpenAI released two new image generation models under GPT Image 2.5: Sunburst and Flare.
Sunburst is positioned by OpenAI as the most capable image generation model in its own lineup so far, and the announcement leans hard into one thing: a major jump in visual fidelity — sharper detail, more natural lighting, richer material and texture rendering. The part that lands closest to Xiaojie's situation is this: Sunburst supports multi-turn iterative edits, and across those rounds of adjustment, it holds onto the details the user wants to keep. The example OpenAI uses is trying out different furniture arrangements in the same room while preserving the details you care about — translated into Xiaojie's world, that's "change the background, but don't let the logo or the ingredient label drift."
Another capability called out specifically is that Sunburst follows detailed instructions with more precision — precise enough to render what a person's handwriting actually looks like. For brand teams that constantly need handwritten-style taglines, limited-edition labels, or holiday card copy in their assets, that's exactly the kind of detail where AI-generated images have historically fallen apart.
But marketing doesn't want precision. Marketing wants volume — and that's where Flare comes in
Xiaojie's problem is "get one image exactly right." The person sitting next to her on the marketing team wants something else entirely: a visual concept for a new launch that needs a dozen-plus variations in a single afternoon for internal review or A/B testing. None of those versions need to be perfect. They need to exist, fast.
That's the problem Flare is built for. OpenAI positions it as the fastest image generation model in its own lineup, more than 50% faster than the previous generation, GPT Image 2, while holding the same quality bar. In OpenAI's own words, this lets people explore more ideas, faster. For teams that need to generate images in bulk for ideation, pitching, or rapid visual testing, Flare covers the "volume" end of the job. Sunburst covers the "precision" end — the part Xiaojie was fighting for at 11 p.m.
Both models quietly remove a step she didn't expect: background removal
There's one more thing Xiaojie hadn't expected this announcement to mention: both models support transparent background output. That means the image comes out already isolated — no trip into Photoshop to manually cut it out.
OpenAI notes this lets users generate complex assets directly, from charts to game assets. Back in Xiaojie's day-to-day, that means a product shot can drop straight into an ad template or an e-commerce listing without waiting on background removal. For game art teams, character and item assets skip a post-processing step too. It's not a dramatic new capability, but it's exactly the kind of detail that gets used every single day, and the time it saves adds up.
The twist: this isn't "which one is stronger," it's "which one, at which stage"
Xiaojie originally assumed that with two new models on the table, her homework was figuring out which one was better and picking that one. Reading the announcement, she realized she'd been asking the wrong question.
Sunburst and Flare aren't a high-end and low-end version of the same model. They're two tools OpenAI has explicitly split by job: Sunburst for the most demanding, detail-critical image generation work; Flare for scenarios that need fast, high-quality output at volume. If Xiaojie had started her Friday night by running a dozen background compositions through Flare first, picked a direction, and only then brought Sunburst in for the final precision pass, she probably wouldn't have made it to redraw number seven.
What's genuinely worth flagging is what the announcement doesn't say: no specific API pricing, no resolution specs, no regional rollout timeline. OpenAI confirms only that both models are now available across the API, ChatGPT, and Codex. That means if you're planning to fold these into an actual team workflow, the billing structure and how fast you burn through usage — the numbers that actually determine whether this is worth using for daily, high-frequency iteration — are things you can only get from OpenAI's current documentation, not from guessing off the demo reel. And that's exactly the moment where picking the right model for the job, and actually being able to see what you're spending, starts to matter — not which model's demo looked more impressive.
She didn't switch tools. She reordered her workflow
By the end of the story, Xiaojie hadn't thrown out the tools she was already using, and she hadn't committed to just one model going forward. What she actually changed was the order of her workflow: start the ideation stage fast and high-volume, let several composition directions run in parallel, and only once a direction is locked in, move into the precision stage — without worrying that the next round of edits will undo what she just spent an hour getting right.
Transparent background output quietly crossed one manual step off her list too. It's not a huge breakthrough, but at 11 p.m. on a Friday, she'd honestly tell you: not having to open Photoshop to cut out a background is, on its own, worth being happy about.
That's also the thing we keep coming back to at AI Token King: as models multiply and specialize, what actually separates teams isn't "using the strongest model available." It's knowing which task belongs to which model, and being able to see exactly what you've spent doing it. Before you sink another night into a seventh redraw, it's worth asking whether this step needs speed or precision — because now there are finally two models built to answer exactly that question, each on its own side.
FAQ
What is GPT Image 2.5, and how is it different from GPT Image 2? GPT Image 2.5 is OpenAI's newest image generation lineup, made up of two models: Sunburst and Flare. The announcement centers on two differences: Sunburst brings a meaningful jump in visual fidelity (sharper detail, more natural lighting, richer texture) and better detail retention across multi-turn edits; Flare is more than 50% faster than the prior generation, GPT Image 2, while holding the same quality level. OpenAI didn't share a full technical spec comparison beyond that, so treat anything more granular as something to confirm directly with official documentation.
How do I choose between Sunburst and Flare? Based on how OpenAI positions them, Sunburst fits the most detail-sensitive, multi-round work where brand consistency has to hold — final product shots, or assets that need precise handwritten text. Flare fits the exploration stage, where you need a large volume of concepts fast — pitch decks, A/B test variants, early-stage ideation. They're not a replacement relationship. They're two tools for two different stages of the same workflow.
Which workflows benefit most from transparent background output? Both models support transparent backgrounds, which OpenAI says can be used to generate complex assets, from charts to game assets. For e-commerce teams, that means product shots can drop straight into ad templates or listing pages without a manual cutout step. For game or ad design teams, character, item, and icon assets skip a post-processing pass too.
Where can I actually use Sunburst and Flare right now? According to the announcement, both models are now available across the API, ChatGPT, and Codex, for developers and general users alike.
Did OpenAI publish exact API pricing or resolution specs? No. The announcement doesn't include specific pricing figures, resolution specifications, or a regional rollout timeline, so this article doesn't cite any numbers on those points. If you're evaluating the real cost of folding these into a team workflow, check OpenAI's current official documentation directly and track actual usage through a spend dashboard, rather than estimating from the announcement alone.
Source disclosure: This article is adapted from an announcement video published by OpenAI's official YouTube channel, "Introducing GPT-Image-2.5 in the API" (published 2026-09-08). The original captions were in Arabic; this piece was rewritten based on that meaning rather than translated word for word, but it remains sourced from OpenAI's own first-party announcement. "Xiaojie" is an illustrative persona used to walk through a realistic use case, not a real customer. Specific API pricing, resolution specifications, and regional availability timelines are not covered in the source material and are not stated in this article; check OpenAI's official documentation for current specifics.
Further reading: We've also covered what OpenAI Codex actually is and how it differs from ChatGPT and Claude Code for non-technical users, why AI tokens end up costing what they do once you break down how autoregressive generation burns through them in coding agents, five tested ways to cut token costs in AI coding workflows, and what Claude Fable 5's safety tiering means for developers building on top of it.
Precise, or fast — do you actually know how many tokens each one burned? Try AI Token King free → (https://aitokenking.com.tw/home), so you can see exactly what each step costs when you're splitting work across multiple models.
