GPT Image 2.5 for ad creative: Flare, Sunburst, and why both cost the same

The choice between them is detail, not budget

A 21:9 banner of an amber serum bottle, running the full width of a product page. The label copy has to stay readable at that size, the knurled ring on the cap has to stay a set of separate rings rather than a smear, and the frame has to survive somebody cropping a square out of it next month. That is roughly the only shot in a normal ad set where the two new GPT Image 2.5 variants behave differently enough to matter.

OpenAI published GPT Image 2.5 on September 8, 2026, and it arrived as two variants rather than one model. On fal, which hosts both, the Flare model page describes it as "OpenAI's default image model for most applications. Fast, high-quality generation with natural lighting, rich textures, and support for complex layouts including transparent backgrounds." The Sunburst page describes the other half of the split: "OpenAI's precision-focused image model, built for premium visual work, extra fidelity on intricate detail, in exchange for longer generation times."

Both are already in the Novoads image picker, beside GPT Image 2 and at the same price.

The sentence that changes how you actually work is not in either description. It is one line in fal's comparison of the two, where Flare's pricing row reads "Identical to Sunburst at every size and quality." A model family usually splits into a cheap tier and an expensive one, which pushes a budget argument into every brief. This one splits on time. You are choosing a schedule, not a spend.

What shipped, and in what shape

Two variants, four endpoints

fal lists four GPT Image 2.5 endpoints: text to image and edit, for Flare and for Sunburst. Its own summary of the arrangement is blunt, and worth quoting because it settles most of the questions people are asking this week: "Same API surface, same pricing, different balance of speed and detail."

Nothing about the request changes when you move between them. Same prompt, same parameters, different endpoint id, different wait. That is a smaller decision than the one a tiered family forces, and it is a decision you can revisit per asset instead of per campaign.

The specs an advertiser actually reads

fal's comparison table publishes the bounds, and both variants share them. Max resolution is "3840px on the long edge." Quality levels run "auto, low, medium, high, xhigh, max." The edit arm accepts "Up to 16" reference images. A single request returns "Up to 4" images. Transparent backgrounds are supported. fal's summary of the family states that both "generate from text and edit existing images, at up to 3840px."

Two of those numbers do real work in an ad pipeline, and they are not the resolution.

• Four images per request is a variant batch, not a single asset. A set of 24 statics is six requests.
• Sixteen references on the edit arm is enough to carry a product shot, a pack shot, a colourway and a brand board into the same instruction without picking which one matters most.

What OpenAI says changed since GPT Image 2

fal's family page carries a changelog and, to its credit, labels the provenance out loud: "Latency, rendering, style, reference and editing claims are OpenAI's own, published with the model on September 8, 2026." Read them as vendor claims, because that is exactly what the page says they are. Six headings, paraphrased down to what an advertiser would notice:

• Latency. Flare is listed as "Up to 50% faster than GPT Image 2," at higher quality than GPT Image 2.
• Rendering. "More natural light, richer texture," with sharper detail across the frame.
• Style. "Stronger style adherence," described as a requested visual style holding more consistently from one generation to the next, which is the property a matched set depends on.
• References. "Subjects stay themselves," with up to 16 references per edit request.
• Editing. "Edits that carry across turns," so an earlier change is more likely to survive a later one.
• Output. "Two more quality levels," because "quality now reaches xhigh and max above the old high ceiling."

The third and fifth of those matter more to a creative team than the first. A set of six frames that share a look, and an edit that does not undo the previous edit, are the two things that turn a model into a pipeline.

Here is the part nobody publishes: a seconds-per-image figure, for either variant, at any quality level. fal's pages carry none, and the model cards give no benchmark. So "faster" and "longer generation times" are directional language, not measurements, and the only way to size them for your own work is to send one real brief through both endpoints and time it. We have written before about how long AI renders actually take when you measure them rather than reading the launch copy, and the gap is usually the interesting part.

Flare is the default, and that is a real recommendation

Where the speed claim comes from, and what it is not

fal's guidance is unusually direct for a model page: "Flare is the one to reach for by default. Move to Sunburst when the detail has to survive a close look." That is a vendor telling you not to buy the premium-sounding thing, which is rare enough to be worth taking at face value.

The reasoning holds up against how static ads are consumed. A feed static is looked at for about a second, at a few hundred pixels wide, on a screen held at arm's length. Extra fidelity on intricate detail has nowhere to land there. What does land is whether the product reads instantly, whether the headline is legible, and whether the frame looks like it belongs to your brand rather than to a stock library.

The ad work that belongs on Flare

Flare is the right endpoint for the bulk of a creative calendar:

• Feed and story statics in 4:5 and 9:16.
• The iteration loop, where you are trying six framings before you commit to one.
• Any asset that exists to be tested against three others and killed by Friday.
• Background and scene variations around a product shot you already have.

The pattern there is volume and disposability. When you expect to throw away five of every six frames, the variant that returns them sooner is the one that changes your week.

Sunburst is for the frame that gets looked at closely

What the extra fidelity buys on a product still

fal's own illustration of Sunburst is a tree cross-section rendered at 3072px, and the page frames the point as durability rather than beauty: "Sunburst is the variant to reach for when the image will be viewed large."

Translate that into ad language and it stops being abstract. The frames that get looked at closely are a short, identifiable list:

• The full-width banner across a product page, where the asset is displayed at desktop width rather than thumbnail width.
• The hero on a landing page that a paid click lands on, which is the one frame in the funnel a visitor is actually looking at rather than scrolling past.
• The asset a designer will crop three shapes out of six months from now, because a crop is an enlargement of whatever detail was there.
• The pack shot where a reviewer zooms in to read an ingredient list, a dosage or a certification mark.

Those are the frames where a smeared serial number or a mushy embossed logo costs a reshoot. Everywhere else in the set, that same fidelity is being rendered into pixels nobody will resolve.

The cost of reaching for it by default

Setting Sunburst as your standing default is the mistake this split invites, because it sounds like the safe choice and it costs nothing on the invoice.

It is still paid for. It is paid in the one resource a creative calendar is actually short of, which is turnaround. A team that waits longer for every frame runs fewer rounds, and fewer rounds is the thing that reliably hurts ad performance. The volume argument is well trodden and it is not new with this model: more shots on goal beats a better single shot, which is why testing more creative beats polishing one at almost every budget.

The quality dial is the price dial

What fal publishes per image

Here is the table both variants share, at 1024x1024: "$0.00588 $0.01317 $0.05268 $0.09366 $0.21072" across low, medium, high, xhigh and max. The same table runs up to 3840x2160, where max quality is listed at $0.40026.

Read that ladder next to the variant question and the asymmetry is the whole story. Moving from Flare to Sunburst changes the per-image price by nothing. Moving from medium to high on either of them multiplies it by four. Moving from medium to max multiplies it by sixteen.

fal states the trap plainly on both model pages: "Changing the quality parameter significantly affects cost; by default we use high." The default is not the cheapest rung. Vendors that price per call rather than per credit make you do this arithmetic yourself, which is why the Higgsfield API puts an estimate endpoint in front of every submission. It is the third of the five priced levels, and four times the cost of the one below it, so a first bill built on defaults is a first bill built four times higher than it needed to be.

What that does to a variant set

Take the set from earlier: 24 statics, six requests of four images. The numbers below are the Novoads credit ladder, which is the same for both variants because the vendor charges the same for 2.5 as it does for 2.

Setting	Per image	24 statics	Variant surcharge
Low	0.1 credits	2.4 credits	none
Medium	0.3 credits	7.2 credits	none
High	0.8 credits	19.2 credits	none

The right-hand column is the point of this post. Against a 50-credit month, the quality dial is worth 12 credits on a single set of 24 images. The variant choice is worth zero. If you are going to audit one decision in this pipeline, audit the dial, not the endpoint. Our breakdown of what a credit actually buys walks the same arithmetic across video.

Three ad jobs the 2.5 pair changes

Text inside the picture

Both fal model pages carry "typography" as a tag, and the family page's own examples lean hard on dense, labelled layouts. Legible in-image text is the capability that separates a usable static from a pretty one, because headlines, price badges and CTAs are made of it, and an image model that mangles a product name inside a set of six is a model that costs you a review cycle.

It is also the capability with a compliance edge attached. If you put a price or a claim inside the pixels, it is an ad claim like any other, and the platform rules that govern AI-generated imagery in ad accounts apply to the words as much as to the picture.

Cutouts without a rotoscoping step

This is the underrated one for e-commerce. fal describes the transparency support in operational terms: "Set background to transparent with a PNG or WebP output and the model returns a real alpha channel."

A real alpha channel is the difference between an asset and a task. A product shot that arrives already cut out drops onto a layout, a carousel frame or a template without a designer opening an editor. If you run seasonal refreshes across a catalogue, that removes an entire step from every SKU, which is a bigger saving than any per-image price.

Edits that leave the product alone

fal describes the Flare edit endpoint as one that "Changes only what is asked, keeping subject, composition and background intact," and says a mask is optional when the change needs to be confined to a region.

The failure mode this addresses is specific and familiar: you ask for a new background and the bottle silently changes shape, or the label drifts a shade warmer between frames. That is the same consistency problem that makes a repeatable style reference so valuable across a product set, and it is the reason the edit arm, not the text-to-image arm, is where most ad work ends up living.

Where both variants still need a person

None of this makes the output shippable on its own, and the failure modes are the same on Flare and on Sunburst because they are not detail problems:

• Invented specifics. A model asked for a serum bottle will happily invent a volume, a percentage or a certification badge that your product does not carry. Every number inside the pixels needs checking against the real pack.
• Drift across a set. Six frames generated from six prompts will not match each other unless the same references carry through the edit arm. This is what the reference slots are for, and skipping them is why sets come back looking like six brands.
• Disclosure. A synthetic image in a paid placement is governed by the platform's rules and, in several markets, by the local advertising code. Neither variant labels anything for you.

A rule for picking a variant

The rule

Default to Flare. Move to Sunburst on two conditions, and only two:

1. The frame will be displayed larger than about 1,200 pixels wide.
2. The frame will be cropped, enlarged or reused later instead of shipped once.

Everything else stays on Flare. If neither condition is true, the extra wait buys detail no one in the funnel will ever see. Written out as a working checklist:

Use Flare when

• The asset is a feed static, a story frame or a carousel panel.
• You are still exploring framings and expect to discard most of them.
• The set has to be in front of a media buyer today rather than tomorrow.
• The frame will be seen at phone scale and retired within a flight.

Use Sunburst when

• The asset is a page banner, a landing hero or anything above roughly 1,200 pixels wide.
• The frame is a master you will crop other shapes out of later.
• Fine surface detail is the selling point: a texture, a weave, an engraving, a pour.
• The image outlives the campaign, which is where reshoot cost actually lives.

The arithmetic on a 24-asset set

Run the rule across a realistic month. Of 24 statics, the ones that clear both conditions are typically the two page banners and the one hero. That is three frames on Sunburst and 21 on Flare.

The cost difference between that split and putting all 24 on Sunburst is zero credits. The difference in turnaround is 21 frames' worth of waiting, on the exact frames that were going to be reviewed at thumbnail scale and half of which were going to be killed. That is the trade, stated as plainly as it deserves: you are not protecting quality by defaulting to Sunburst, you are paying for fidelity in rounds you will not get to run.

How Novoads solves the variant choice

Both GPT Image 2.5 variants are in the Novoads model picker, on the canvas and in the credit guide, listed as GPT Image 2.5 Sunburst and GPT Image 2.5 Flare. They sit beside GPT Image 2 rather than replacing it, at the same per-image price, because the vendor charges the same for 2.5 as it does for 2.

That means the decision you make in the picker is the one this post has been arguing is the real one. The variant row is a speed setting. The quality setting is the price. That makes the comparison cheap to run for yourself: send the same brief to each variant, put the two outputs side by side in the same project, and decide from your own frames rather than from a model card. No second subscription, no moving files between tools. If you also need the frame to move, the product video side of the same pipeline starts from the still you just made.

The model stopped being the variable

For two years the interesting question about an image model was whether it could render the thing at all. GPT Image 2.5 answers that question twice, at the same price, and hands you back a question about scheduling instead. That is what a maturing tool looks like: the capability stops being the scarce input and your own operating discipline becomes it.

So pick Flare, watch the quality parameter rather than the variant name, and spend the time you save on more rounds instead of finer pixels.

You can run both variants inside Novoads from $49 a month on the Inicial plan, which includes 50 credits every month. Cancel anytime.