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

Start with Flare when people are waiting for an image. Try Sunburst when a small unwanted change can invalidate an otherwise approved asset. That is the practical answer to GPT Image 2.5 Flare vs Sunburst: OpenAI positions Flare for fast everyday generation and Sunburst for work where editing precision matters most.^[1]^[2]

The useful decision is more specific than “drafts versus final images.” A final social graphic may need little revision; an early product mockup may require exact preservation of a label. Compare the variants against the part of your workflow that fails, keeping the prompt, references and output settings consistent. The model descriptions tell you what to try first; your acceptance criteria determine what stays in production.

TL;DR

Use Flare as the initial baseline for interactive generation. OpenAI gives it a “Very fast” speed rating and describes it as the everyday option.^[1]
Evaluate Sunburst when preservation across edits determines acceptance. Its model page emphasizes editing precision and rates speed “Medium.”^[2]
Both variants expose the same quality ladder. It includes low, medium, high, xhigh, max and auto; choosing Sunburst does not automatically choose a higher quality setting.^[3]
Equal token rates do not establish equal image costs. OpenAI says consumption can differ between models and settings.^[3]
A mask is guidance, not a promise of pixel-perfect preservation. Review the supposedly unchanged area as well as the requested edit.^[3]

GPT Image 2.5 Flare vs Sunburst at a glance

The official model pages establish a positioning difference. They do not provide a controlled Flare-versus-Sunburst latency benchmark for your requests.^[1]^[2]

Decision point	Flare	Sunburst
OpenAI model ID	`gpt-image-2.5-flare`	`gpt-image-2.5-sunburst`
Published emphasis	Fast, high-quality everyday generation	Image generation and editing precision
Published speed rating	Very fast	Medium
Inputs and output	Text and image inputs; image output	Text and image inputs; image output
Explicit quality settings	low, medium, high, xhigh, max	low, medium, high, xhigh, max
First evaluation question	Does it deliver an acceptable result within the waiting-time target?	Does tighter editing control reduce rejected revisions?

Use the final row to design your evaluation. It gives each variant a specific job in the comparison. A model can produce a more impressive standalone picture while being less useful for a task that requires a recognizable product, unchanged copy or a fixed layout.

Write those requirements down before generating candidates. “Looks better” is too broad to explain why a request should move to another model. “The bottle label must remain readable after the background changes” gives the reviewer something concrete to inspect.

Measure waiting time and accepted work separately

Flare is the sensible starting point for a user who is actively adjusting a prompt, previewing a design or exploring compositions. That recommendation follows its documented speed positioning. It does not mean every Flare request will finish faster than every Sunburst request under different settings.^[1]^[2]

For an interactive product, record the time from submission to a usable image. Include any time your application spends waiting for the result, downloading it and displaying it. A fast generation that takes another round of editing has a different user experience from an acceptable first result.

For a background workflow, track accepted deliverables over the whole job. Review time and repeated attempts belong in that record. Sunburst earns a place if it resolves a recurring rejection that matters enough to justify the additional wait. Flare remains appropriate when both variants meet the acceptance criteria and the extra wait produces no useful difference.

Keep quality and dimensions explicit during the first comparison. The guide permits automatic selection for several output options, but allowing those settings to vary makes it harder to explain a difference you observe.^[3] Start with the actual delivery dimensions and a fixed quality value. Once you know which model fits, adjust the rendering settings separately.

One tempting shortcut is to quote OpenAI's launch latency improvement as a comparison between these variants. The launch announcement compares Flare with GPT Image 2, its predecessor. It does not establish that Flare is a particular percentage faster than Sunburst.^[4]

Test the edits your approval process rejects

Sunburst is worth evaluating when the image is already close to approved and the requested change is narrow. OpenAI's positioning emphasizes this kind of precision; the launch announcement also describes better adherence across successive edits for the new image generation family.^[2]^[4]

Use an edit brief with separate instructions for the change and the details that must survive. For example:

Replace the blue ceramic mug with a green glass bottle. Preserve the hand position, camera angle, lighting, table, background and headline text. Keep the bottle within the space occupied by the mug.

This is an example evaluation prompt, not a reported test. Reviewers should inspect the requested replacement first, then inspect the protected details. A correct bottle with a distorted hand or rewritten headline is a failed edit for this brief.

For a campaign sequence, retain the original reference and each approved intermediate image. Give the next request the visual context it needs. A sequence of unrelated generation calls does not gain conversation memory merely because every call uses Sunburst. OpenAI documents multi-turn image workflows through the Responses API, including continuation with previous response context.^[3]

The same distinction matters when switching models. Preserve the approved asset as input when you escalate an edit. Sending only the original text prompt invites a new interpretation of the image, which makes preservation harder to assess. The model choice and the information you supply are separate decisions.

Masks and transparency are shared controls

Both variants support the image generation and editing workflow. The guide documents masked edits and transparent output; transparency requires PNG or WebP.^[3] These controls are not reasons, by themselves, to choose Sunburst over Flare.

Use a mask when the task benefits from identifying the region to change, then check its boundary and the rest of the frame. OpenAI notes that the mask may not be followed with exact precision. Its limitations section also warns that text placement, recurring visual details and structured composition can still be difficult.^[3]

This is where an acceptance checklist helps more than another general quality adjective. Inspect product edges, logos, copy, shadows and objects adjacent to the edit. When those details are the reason Flare was rejected, they should also determine whether Sunburst succeeds.

Budget for the selected model and billing method

The variants share OpenAI's token rate card, but the guide explicitly warns that per-image consumption can differ. On reAPI Standard, the model selector displays different per-image quotes: $0.0173 for Flare and $0.023 for Sunburst, checked September 13, 2026. Whole-credit settlement also affects the amount charged for a request. Standard bills per delivered image; Official uses token metering, and the final amount depends on the request's usage. Compare the actual request estimate and final charge for your configuration. The GPT Image 2.5 API cost guide covers the rate tables and settlement examples.^[3]^[5]^[6]

When to switch from Flare to Sunburst

Use a small evaluation set drawn from the work you actually need to ship. Include straightforward generation, a narrow edit and a sequence of revisions if those occur in your product. Do not substitute attractive demonstration prompts for the requests that cause support tickets or manual cleanup.

For each request, preserve the prompt and references, select the same output settings, and record the model ID. Review results against the written requirements before looking at the model label. Keep a record of accepted outputs, rejected details and total waiting time.

Move a workload to Sunburst when:

Flare repeatedly changes a protected feature during an otherwise correct edit.
A recurring revision sequence loses details that later steps still need.
Sunburst demonstrably reduces those specific failures on your evaluation set.
The extra waiting time remains acceptable for the person or process receiving the image.

Keep or return the workload to Flare when both models satisfy the same requirements, when the task is mainly exploring composition, or when the delay prevents useful iteration. Apply these rules to the results from your own requests.

Record any quality change as a separate experiment. Moving from Flare at low to Sunburst at max changes more than the model. It may produce a useful image, but it cannot tell you which change solved the problem.

FAQ

What is the main difference between Flare and Sunburst?

OpenAI positions Flare for fast everyday image generation and Sunburst for editing precision. Both accept text and image inputs. Choose based on the waiting time and preservation requirements of the task.^[1]^[2]

Is Sunburst always better for final images?

No universal result is established by the documentation. A final asset that Flare already produces correctly does not automatically benefit from a different model. Evaluate Sunburst when you can name the detail or revision behavior that needs improvement.^[2]

Does Flare support image editing?

Yes. Flare can be selected for the documented image generation and editing workflow. Sunburst's precision positioning does not make editing exclusive to Sunburst.^[3]

Do the two variants support different quality settings?

No. Both list low, medium, high, xhigh, max and auto. Compare a fixed quality setting first, then investigate whether a different setting improves the accepted result.^[1]^[2]

Do equal API rates mean the models cost the same?

No. Equal rates price the same unit of usage, but the number of units can differ. The OpenAI guide explicitly distinguishes token-rate parity from per-image cost parity.^[3]

Can I select Sunburst inside Codex?

OpenAI's launch announcement includes Codex, while the current built-in image guide still names gpt-image-2. The reviewed documentation does not establish a built-in Flare/Sunburst selector. For an explicit variant choice, the Image API documents both model IDs.^[4]^[7]^[3]

Does “Flare vs Sunburst 違い” describe different models?

Yes. “違い” asks about the difference: these are distinct model IDs with different speed and editing priorities. They share the documented input types and quality options.^[1]^[2]

Is Microsoft's MAI-Image-2.5 the same model?

No. MAI-Image-2.5 is a Microsoft image model. GPT Image 2.5 Flare and Sunburst are OpenAI models. The similar version number does not make their model IDs or API instructions interchangeable.^[8]^[1]^[2]

Configure the task that needs a better result

Resolve GPT Image 2.5 Flare vs Sunburst with a specific acceptance requirement: a waiting-time target, a protected product feature or a revision sequence that must remain usable. Start from the GPT Image 2.5 model page, set the variant and rendering options explicitly, and keep the approved image available for subsequent edits. The reAPI documentation shows the model IDs, image inputs and task polling needed to carry that decision into an application.