GPT Image 2.5 Flare vs Sunburst: Which One Should You Pick

Open the model picker on GPT Image 2.5 and you get two buttons: Flare and Sunburst. Most people click the first one and never think about it again.

That's usually fine. But the two names don't mean what you'd guess from the labels, and the advice floating around the internet was written for a different situation than yours.

What OpenAI Actually Says

Straight from the official image prompting guide:

GPT Image 2.5 Flare is the small model, optimized for speed, with image quality comparable to GPT Image 2. GPT Image 2.5 Sunburst is the base model, optimized for quality, with higher image quality than GPT Image 2.

Read that twice, because it's easy to skim past the important part.

Now for the awkward part: OpenAI's own materials don't agree on where Flare's quality lands.

The prompting guide, quoted above, calls it comparable to GPT Image 2. The launch announcement says the opposite in its API section, that Flare "delivers higher-quality images than GPT‑Image‑2 at 50% lower latency."

That's a direct conflict between two official OpenAI pages: one says Flare improves on GPT Image 2, while the other says it is comparable. We can't resolve it for you, and it's worth knowing that either claim can be traced to an official source.

What both documents agree on: Flare is the speed-optimized model and Sunburst is the quality-optimized one. Sunburst is the base model, Flare is the small one. That ordering is uncontested, and it's the only thing the decision below actually rests on.

How much speed is the part worth being careful about. OpenAI's launch announcement puts it at up to 50% lower latency than GPT Image 2, and that "up to" is doing real work. The prompting guide is blunt about it:

Measure response time and quality on your own workload. Results depend on your prompts, reference images, output dimensions, and quality settings; a speed improvement on one workload doesn't establish a fixed improvement on another.

So treat 50% as a ceiling, not a typical result. What you actually wait for is end to end time, which includes queueing and delivery on top of model compute. In one of our own 1K Flare runs the whole thing took roughly 70 seconds, which is a single data point rather than a benchmark, but it should tell you that "half the latency" is not the same as "instant."

Sunburst is the one tuned for quality. It's the base model rather than the small one, and the prompting guide places its image quality above GPT Image 2 without qualification. The launch announcement doesn't rank it against GPT Image 2 directly, describing it instead as the tier that adds precision for detailed creative work at the cost of longer generation times. Different emphasis, same direction.

Both tiers improve on precise editing and subject preservation, so the "change one detail, keep everything else" behavior that makes 2.5 interesting is in both. The split is narrower than the marketing suggests: it's speed against fidelity, not features against features.

Why Most Guides Tell You to Default to Flare

Search for this comparison and you'll find a consistent recommendation: start with Flare, escalate to Sunburst only when an edit has to survive scrutiny.

That advice is sound, and it's worth understanding where it comes from. It's written for developers calling the OpenAI API directly, where two things shape the decision:

• Latency is a product cost. If you're generating images inside a user-facing app, every extra second of wait is churn. A meaningfully faster tier is worth real quality tradeoffs.
• Volume matters. At thousands of generations a day, routing the bulk of traffic to the cheaper, faster path and reserving the heavier model for a small slice is just good engineering.

Neither of those is about which model makes better pictures. They're about running a service.

On Veevid, the Cost Half of That Argument Disappears

Here's where your situation differs from the one those guides assume.

On Veevid, Flare and Sunburst cost the same number of credits. Same tier, same generation, same price. Pricing follows output resolution, not which model you picked.

Strip cost out of the equation and the decision collapses to a single question: how much do you mind waiting?

If you're iterating on a prompt and want to see ten variations quickly, Flare's speed is genuinely worth it. If you're making something that ships, there's no reason to leave quality on the table. Sunburst takes longer and costs you nothing extra.

The default that makes sense for an API pipeline is not the default that makes sense when you're sitting in front of a generation panel with no meter running.

When Flare Is Still the Right Call

Speed is not a consolation prize. There are real cases where Flare is the better pick, and picking Sunburst out of habit would be the mistake:

• Prompt exploration. The first five attempts at a prompt are throwaway. Getting them back faster means you find the right direction sooner.
• Batch work. Twenty social variations of the same layout. The marginal quality difference won't survive a feed anyway.
• Reference hunting. You're testing which of your photos works as a reference image, not producing a final.
• Anything that gets composited down. If the output lands as a small element inside a larger design, extra fidelity gets thrown away in the resize.

The rule of thumb: Flare while you're deciding, Sunburst once you've decided.

What Both Tiers Give You

The tier choice doesn't gate any capability. Everything below works identically on Flare and Sunburst:

The transparent background option is worth a specific mention. Setting it returns a PNG with a real alpha channel, built during generation rather than cut out afterward. That matters for the things background removal tools historically mangle: glass, hair, soft shadows, fine edges. You skip the cleanup pass entirely.

One catch that isn't obvious: if your prompt describes a backdrop or a scene, that description wins over the transparency setting. Describe the subject and leave the background unmentioned.

The Resolution Constraint Nobody Mentions

Four of the thirteen aspect ratios are capped at 1K: 27:16, 16:27, 9:8, and 8:9. Everything else supports the full 1K / 2K / 4K range.

These are the less common ratios, so most people never hit the limit. But if you've picked one of them and wondered why the 2K and 4K options went grey, that's why. Switch to a neighboring ratio (16:9 instead of 27:16, for instance) and the higher resolutions come back.

How to Actually Decide

Skip the spec comparison. Ask yourself one thing: is this image going to be looked at closely?

A hero image, a product shot, a poster someone will stand in front of, an edit where a face has to stay recognizable: Sunburst. It's the tier OpenAI tuned for quality, and on Veevid it doesn't cost you anything to say yes.

A quick test, a thumbnail, the fourth variation of a layout you're still sketching: Flare. You'll get it back faster and the difference won't show.

Switching is one click in the Model Version card, so you're never locked in. Start where the work is, not where a default put you.

Try Both

Run the same prompt through both tiers and see the difference for yourself. It's the fastest way to build an instinct for when the upgrade is worth the wait.

• Text to image with GPT Image 2.5
• Image to image with GPT Image 2.5
• GPT Image 2.5 overview

If you're coming from the previous generation, the GPT Image 2 guide covers what that model does well and where 2.5 picks up.

Sources

• OpenAI - Image prompting guide - model positioning for Flare and Sunburst, and the instruction to measure latency on your own workload
• OpenAI - Introducing ChatGPT Images 2.5 - the up to 50% latency figure, and the API section describing Flare as higher quality than GPT Image 2

Checked against these sources on 2026-09-11. Capability details reflect what's available on Veevid.