GPT-Image-2.5 Sunburst or Flare: which to pick and how they differ

Short answer

If you want one answer without reading further: take Flare when there are many images and they are needed quickly, and Sunburst when there is one image and people will look closely at it. Here is why, and where the line actually falls.

What actually separates them

Both models shipped on 8 September 2026 and share one feature set. The difference is not in what they can do, it is in how much the model spends working out the frame.

Identical across both: generation from text, editing an existing image, up to sixteen reference images, a mask for localized edits, transparent backgrounds, six quality tiers, free dimensions up to 3840 pixels, up to twenty images per request and prompts up to thirty-two thousand characters.

Sunburst is positioned as the most capable model in the line. Its strength is dense scenes: many objects in frame, disciplined composition, natural light, honest materials, and predictability when the same image is edited several times in a row.

Flare is the fastest in the line. Per the official description it delivers higher quality than the previous GPT Image 2 generation at half the latency. This is the model for volume: content, prototypes, product cards in batches.

Where the line falls in practice

The difference does not show up where people usually look for it. Both engines render a simply pretty picture equally well. They diverge on frame density.

Take a scene with thirty small objects arranged on a grid where every one has to stay recognizable: a disassembled movement, a tool set, a product flat lay. Sunburst holds the geometry and does not collapse the small parts into mush. Take a single object on a clean background and you will not see a difference, while waiting twice as long and paying more.

The second divide is a series of edits. By the fourth pass over the same image, cheaper models start drifting: the face changes, the background shifts, an object goes missing. Sunburst is built for exactly this.

Mask editing: the reason to look at both

Both models support a mask — you mark the area to rework and the rest stays untouched. This is what is usually called inpainting.

One honest detail worth knowing up front: the mask works as guidance, not as a hard boundary. An edit can extend slightly past the marked area. For replacing or recoloring an object that is fine and even helps blend the edges. For pixel-exact work, budget for checking the result.

A mask cannot be sent on its own: a source image always goes with it. With several reference images, the mask applies to the first.

Transparent backgrounds

Both can return an image with transparency, but there is a format constraint: transparency only survives in PNG and WEBP. Choose JPG and the background comes back opaque regardless of the setting, because JPG has no alpha channel. This trips people up often, so remember the pairing: transparent background plus PNG.

How they sit next to the rest of the catalog

The GPT Image line also includes the previous generation, GPT Image 2. It costs less and still works for simple jobs, but it gives ground on fine detail and on holding a frame across edits.

If text inside the image matters — a headline on a poster, lettering on packaging, Cyrillic — look at Qwen-Image-3.0 and the higher tier Qwen-Image-3.0 Pro, where typography is the core strength.

If you need maximum photorealism in a portrait and will pay more per frame, there is Nano Banana Pro.

And if the job is not to draw something new but to lift material you already have, generation is the wrong tool: the catalog has FLUX Video Upscale for footage.

What neither of them does

There is no negative prompt: telling the model to keep text out of frame has to happen inside the ordinary prompt, there is no separate field for exclusions. There is no fixed seed either, so reproducing the same frame twice is not possible — the model returns a seed in the response but does not accept one on input.

One more thing to keep in mind for product work: these models like to add plausible logos and lettering to objects that you never asked for. If the image is going into commercial material, forbid that explicitly in the prompt.

Bottom line

Volume, product cards, social, prototypes — Flare. Key visuals, product photography, dense scenes, a series of edits on one frame — Sunburst. Both run off the shared balance with no separate subscription, and current prices with a calculator live on the model cards.