GPT Image 2.5 Sunburst vs Flare: Which Model Should You Use?

Flare is the sensible starting point for most image applications. Sunburst is worth evaluating when an edit must change one thing while preserving several others. That recommendation follows OpenAI's September 8, 2026 launch positioning: Flare is its default for most API applications, while Sunburst emphasizes editing precision for work such as campaign creative and product imagery, with longer generation times. It is a starting hypothesis for your workload, not a measured winner. OpenAI's launch announcement

Both models generate new images and edit existing ones. They also share the same Standard token rates, so the choice is not simply “cheap model or premium model.” The useful question is which combination of model and quality setting delivers an acceptable asset with less waiting, fewer retries, and less manual repair.

This comparison uses official documentation checked on September 9, 2026. We did not run a paid API benchmark. The examples below are suggested evaluation tasks; the illustrations explain the decision process and are not sample outputs from either model.

What actually differs between Sunburst and Flare

Capabilities and model names come from the official Flare model page and Sunburst model page; the dollar rate is from OpenAI's Standard pricing. The last row is our recommendation, rather than an API guarantee.

There is no documented resolution advantage that makes Sunburst the automatic choice for a larger canvas. Both models support the same size constraints and quality options. Use the same explicit output size for an initial comparison—for example, the guide's recommended 1536x1024 landscape size. This keeps a change in dimensions from obscuring the model comparison. Size and quality options

Choose around the edits you cannot afford to miss

For a new blog illustration or an early advertising concept, you may have considerable freedom over the composition. If the output communicates the intended idea and fits the page, it can be usable even when it differs from your first mental picture. Flare is a reasonable first candidate for this kind of flexible brief.

A product photograph has a different acceptance test. Suppose a customer asks you to replace a neutral studio background with a warm kitchen scene, while keeping the bottle's shape, label text, cap, and camera angle. A beautiful kitchen does not compensate for a changed logo. This is where Sunburst's advertised editing precision becomes relevant: it gives you a reason to test the model on the difficult requirement, rather than a reason to approve its output automatically.

Write the acceptance criteria before generating anything. For that bottle edit, separate the requirements into two groups:

• Requested changes: replace the background, use warm daylight, and add a plausible contact shadow.
• Required preservation: retain the product silhouette, cap color, label wording, and the position of the label on the bottle.

An image passes only when both groups pass. If you judge only the new background, you can accidentally reward a model for doing the easy part while damaging the product.

The same logic applies to a campaign revision. Changing the headline, moving a person to make room for a CTA, and retaining a recognizable face are separate checks. Decide which misses make the image unusable and which can be corrected quickly in your normal design tools. Record correction time separately so that a nominally fast generation does not hide a long cleanup step.

Keep expectations realistic for both models. OpenAI documents remaining limitations in text rendering, recurring subject consistency, and composition. A mask guides an edit but does not guarantee that changes will follow its exact shape. Inspect the surrounding image as well as the requested edit; do not assume that a precise mask locks every other pixel. Image editing with masks · Documented limitations

Model choice and quality choice are separate decisions

Flare at max is still Flare. Sunburst at low is still Sunburst. Both support low, medium, high, xhigh, max, and the default auto, but shared setting names do not establish equivalent visual quality or identical consumption. OpenAI recommends lower quality for drafts; use higher settings when the final asset needs them. Quality settings

For a useful first comparison, choose one explicit quality setting and hold it constant across models. Use the same prompt, reference files, and dimensions. Leaving quality at auto can be appropriate in a finished application, but it makes an initial comparison less clear because you have delegated another decision to the system.

Once you understand the model difference on your tasks, try a different quality setting on the promising candidate. For example, if Flare already meets your requirements at high, evaluate whether medium remains acceptable before making max your default. If a recurring failure concerns preserved product details, compare Sunburst at the same setting before assuming that a higher Flare setting solves it. These are experiments to run, not claimed outcomes.

Read speed and pricing claims carefully

OpenAI's API announcement describes Flare as having 50% lower latency than GPT Image 2. That comparison is against the predecessor; it does not say that Flare takes half as long as Sunburst. The announcement positions Sunburst as taking longer, but it does not provide a universal number of seconds for your request. Launch latency claims

Measure elapsed time from submitting your request until the complete image file is available for use. Keep any preview timing separate. For an interactive application, a slow outlier can matter more than a small improvement in the average. For unattended production, the time required to finish the whole job—including retries—may be more useful than the duration of one successful request.

The models' Standard rates are identical: $5 per million text input tokens, $8 per million image input tokens, and $30 per million image output tokens. Cached input has separate lower rates when usage is actually reported as cached. Equal rates do not guarantee equal request totals: the prompt, references, size, quality, and actual token consumption still matter. Official image generation pricing

For the business comparison, use:

text
Cost per accepted image = total applicable API cost / accepted images

Include the paid attempts needed to get those accepted images. If your application uses the Responses API, include its mainline model usage in the workflow's cost as well. Track designer correction time alongside the dollar figure rather than silently folding it into an API price.

This measure can favor either model. A faster first attempt is not necessarily the cheaper completed job, and a more capable model is not necessarily worth its added wait on a flexible brief. For calculator estimates and a worked budget, use our GPT Image 2.5 API pricing guide.

Set up a comparison you can act on

Use work you expect to ship: an actual illustration brief, a representative product edit, or a campaign revision. Include tasks that previously required retries, so the evaluation does not consist entirely of easy wins.

1. Save the inputs. Keep the exact prompt, original reference images, output dimensions, and quality setting. Define acceptance criteria before reviewing results.
2. Change only the model first. Send the same task to Flare and Sunburst. Retain the original returned files so that thumbnail previews do not conceal text or preservation errors.
3. Review against the brief. Check the requested changes and the details that must survive. If possible, review without the model name visible to reduce expectations influencing the decision.
4. Record delivery and expense. Store the model ID, settings, complete-image latency, returned usage, paid retries, acceptance decision, and correction time. Keep failures in the record.
5. Choose by task category. A model that works well for open-ended illustrations may not be the best choice for label-sensitive edits. Keep separate results when the acceptance requirements differ.

Avoid making a production decision from one attractive pair of images. Repeat representative tasks enough to see whether a failure is recurring and whether slow results disrupt your workflow. If no images pass, cost per accepted image is undefined; record that failure rather than reporting a misleading zero.

Select the model in the API

With the Image API, set model directly to gpt-image-2.5-flare or gpt-image-2.5-sunburst on a generation or editing request. With the Responses API, the top-level model is a supported mainline model; the image choice belongs in the image generation tool, such as tools: [{type: "image_generation", model: "gpt-image-2.5-sunburst"}]. Responses is useful when the product needs conversational, multi-turn image edits. Official API selection guide

For reproducible evaluation records, the documented dated snapshots are gpt-image-2.5-flare-2026-09-08 and gpt-image-2.5-sunburst-2026-09-08. Pinning a snapshot identifies the model version; it does not make image generation deterministic. Check the current Flare and Sunburst pages before adopting a snapshot.

API access is also separate from seeing Images 2.5 in ChatGPT. Your organization may need verification, and your account's limits apply. The documentation's model listing alone does not confirm that your own request can deliver an image. API access requirements

Start with Flare if you need a general default. Keep it when your real assets pass. Try Sunburst where preservation failures or repeated edits consume the time you expected to save, and adopt it where the accepted results justify the wait. That decision can be different for two workflows in the same application.