GPT Image 2.5 Flare vs Sunburst: which should you use?
Choose GPT Image 2.5 Flare or Sunburst using official guidance, editing requirements and quality settings, then compare latency and cost on your own workload.
Ofox 
Start with GPT Image 2.5 Flare when speed matters most, or Sunburst when demanding image quality and precise editing matter most. OpenAI describes Flare as the smaller model with quality comparable to GPT Image 2, and Sunburst as the base model with higher quality than GPT Image 2. These are official positioning statements, not an Ofox benchmark.
The actual API choices are gpt-image-2.5-flare and gpt-image-2.5-sunburst. This guide checks the official documentation on September 9, 2026.
Flare vs Sunburst at a glance
| Your workflow | Start with | What to verify |
|---|---|---|
| Everyday generation with a tight response-time budget | Flare | Acceptable quality and measured latency |
| GPT Image 2 already produces suitable results | Flare | Retain quality, then look for faster completion |
| Complex images or edits that GPT Image 2 struggles with | Sunburst | Required details and subject preservation |
| Sunburst already meets the quality target | Also evaluate Flare | Switch only if quality holds and latency improves |
Both models accept text and image inputs and produce images. Both support generation, editing and transparent backgrounds. Neither their names nor the table above establishes a fixed speed multiplier for your application.
Choose around the result you need
For a product photograph, acceptance might mean preserving the bottle shape, readable label wording and clean edges while changing only the background. A social illustration may instead prioritize composition and a short wait. A single general quality ranking does not describe both tasks well.
OpenAI’s image prompting guide recommends using your current image quality as the starting point. If GPT Image 2 already passes, try Flare first. If it falls short on a complex task, start with Sunburst and establish whether it meets the requirement before optimizing speed.
For editing, explicitly separate the change from the details to preserve:
Replace the plain background with a softly lit studio background. Preserve the product shape, label wording, camera angle and crop. Add no extra objects or text.
That is an example instruction, not proof of perfect preservation. Inspect the output against the source, especially text, contours and shadows. Both models advertise improvements in precise editing and subject preservation; neither statement is a guarantee for a particular image.
How should you choose the quality setting?
Both model pages list auto, low, medium, high, xhigh and max. For the first comparison, keep an explicit setting unchanged where supported, along with the prompt, reference images and dimensions. The same setting label does not guarantee equal visual quality or response time across models.
If the result fails, test a higher setting. Once it passes, test whether a lower setting preserves acceptable quality. Use xhigh or max to address a visible problem within your latency budget. OpenAI cautions that higher quality settings do not guarantee better results for every prompt.
Do not compare a small Flare output with a larger Sunburst output and attribute the entire difference to model choice. Similarly, using auto can be appropriate in an application, but an explicit setting makes an initial comparison easier to interpret.
Does choosing Flare save money?
It is not established by the price table. Both official model pages list the same token rates. Faster completion does not necessarily mean fewer billable tokens. The GPT Image 2 calculator cannot estimate 2.5 token consumption; the current image generation guide has a separate 2.5 output-cost estimator with exclusions.
Read the GPT Image 2.5 pricing guide for the complete billing categories and a worked calculation. Compare cost per accepted image, including billed corrections, rather than the cost of one especially successful attempt.
A practical comparison checklist
- Collect representative prompts and references, including cases your current workflow handles poorly.
- Define pass criteria before examining results: readable text, subject details, composition and valid output format.
- Hold prompt, references, dimensions and explicit quality constant in the initial comparison.
- Record response times, failures, retries, returned usage and acceptance decisions.
- Choose the model that passes the quality threshold within the time and cost budget. Retain failure examples as well as successes.
We have not run paid inference tests for this article. There is no measured claim here that Flare is a particular number of times faster or that Sunburst wins every task.
For implementation, follow the API generation and editing guide. Existing applications can use the GPT Image 2 migration checklist before changing production traffic.
Sources
Frequently Asked Questions
- Which GPT Image 2.5 model should I choose?
- Start with Flare when speed is the priority and Sunburst when demanding quality is the priority. Verify quality and latency on your own workload.
- Is Flare cheaper than Sunburst?
- Not necessarily. Published token rates are identical, but usage and the number of attempts can differ.
- Does max quality always produce a better image?
- No. OpenAI says a higher quality setting does not guarantee better results for every prompt.
