GPT-Image-2.5 Sunburst API

GPT-Image-2.5 Sunburst is OpenAI's base and highest-quality model in the GPT Image 2.5 API family. It generates images from text, transforms supplied images, and performs localized edits. OpenAI recommends it when GPT Image 2 does not satisfy a complex quality requirement or when a workflow needs the tightest control over what changes between revisions.

The model focuses on subject fidelity, natural lighting and textures, fine visual detail, and preservation of composition across edits. This matters in workflows where an apparently small change can invalidate the asset—for example, altering a product label without reshaping the package, changing a garment without changing the model's identity, or updating weather while retaining the existing camera angle and scene.

Sunburst uses longer generation times than Flare. That trade-off makes it a better fit for final campaign assets, polished product scenes, key visuals, book illustrations, and multi-round edits than for every preview in an interactive application. It supports the same six quality values and flexible size controls as Flare, including experimental resolutions above 2560×1440 and maximum 4K landscape or portrait outputs.

GPT-Image-2.5 Sunburst vs Nano Banana Pro, GPT Image 2, and Flare

Sunburst's closest alternatives solve different parts of the professional image workflow. Compare them against a fixed acceptance checklist rather than judging one attractive sample.

Choose Sunburst when the asset must preserve identity, typography placement, packaging geometry, or an approved composition through several revisions. Choose Nano Banana Pro when search-grounded visual information or a Gemini-centered multi-turn workflow is essential. Choose Flare when the same acceptance criteria can be met with less waiting.

A Quality Test for Sunburst vs Flare

Run an A/B test before routing every request to the slower model. Keep the prompt, input images, dimensions, format, background, and quality setting identical. Score each result against four criteria: instruction completion, unchanged-region preservation, subject fidelity, and usable detail at final size.

Start with Sunburst when GPT Image 2 previously failed the task. If Sunburst passes, submit the same request to Flare. Route future calls to Flare only when it also passes and the latency improvement matters. Keep Sunburst for the requests where its quality advantage changes the business outcome. This test prevents a subjective “looks better” decision from turning every image into a higher-latency call.

GPT-Image-2.5 Sunburst Prompt Guide for Precise Editing

Write edit instructions as a visual change list. First identify the exact target, then state the new condition, then lock every region that must remain untouched. Avoid broad requests such as “make it better,” because they give the model permission to reinterpret the whole image.

Packaging revision:

Replace only the flavor name on the front label with “Blood Orange.” Preserve the bottle shape, cap, logo, ingredient text, label dimensions, reflections, camera position, background, and contact shadow. Keep all other pixels visually consistent.

Reference-subject transformation:

Place the referenced person in a quiet winter train carriage at blue hour. Preserve facial proportions, eye color, hairstyle, and age. Match cool window light to the face naturally. Keep the framing at waist height and do not add text or logos.

Review the first output at its final display size before requesting another edit. In later turns, repeat the locked elements and change one condition at a time. This gives Sunburst a clearer preservation boundary and makes regressions easier to identify.

GPT-Image-2.5 Sunburst API Access on GPTProto

Call gpt-image-2.5-sunburst when you want the current alias, or pin gpt-image-2.5-sunburst-2026-09-08 for a repeatable evaluation. GPTProto applies 5% off OpenAI's listed token rates and lets the same account balance cover other models. Because Sunburst and Flare can consume different output-token quantities at the same nominal quality, compare recorded usage as well as latency and visual results.

Who Should Choose gpt-image-2.5-sunburst for Text to Image?

The gpt-image-2.5-sunburst model is ideal for professionals who require high-quality, reliable visual outputs with a high degree of prompt adherence. It is particularly well-suited for:

• Content Creators and Designers who need to rapidly iterate on visual concepts for digital media.
• Software Developers building applications that require automated background, illustration, or asset generation.
• Marketing Teams looking to produce consistent visual campaigns at scale through structured prompt engineering.
• Creative Agencies aiming to streamline the transition from text-based briefs to high-fidelity visual proofs.

Pro Tips for Using gpt-image-2.5-sunburst for Text to Image

• Define your subject clearly at the beginning of the prompt to ensure the model focuses on the core element of the image.
• Use specific lighting and lens descriptors to influence the mood and depth of the generated output.
• Experiment with stylistic keywords to refine the aesthetic, such as 'cinematic lighting', 'minimalist design', or 'hyper-realistic texture'.
• Maintain a consistent aspect ratio in your API calls to ensure that the composition remains stable across multiple generations.
• If the output is too complex, simplify the prompt by focusing on fewer, more impactful descriptive elements to guide gpt-image-2.5-sunburst more effectively.