GPT Image 2.5 Flare is an image generation and editing model focused on generation speed. It is suitable for turning creative briefs into advertising visuals, interface concepts, and product scene images, and can also adjust colors, backgrounds, and composition using reference images. It is better suited to creative workflows that explore directions first and then refine selected results; Sunburst in the same series focuses on high fidelity and precise control, and can be used for tasks with higher requirements for detail preservation.
Clarify capacity, input/output, and invocation methods before choosing a model.
The quantities, dimensions, and formats above are invocation rules for this platform's endpoint and do not represent the model's native specifications or guarantee the effects of all parameter combinations.
Learn what gpt-image-2.5-flare can bring to your work.
Flare is suitable for organizing subject, scene, lighting, color palette, and composition requirements into comparable image concepts. When creating marketing assets, you can explore different backgrounds and visual styles around the same theme, first select the most suitable direction, then continue refining the chosen image instead of locking into a complex final version from the start.
When editing, you can submit product photos or existing designs and clearly state the subjects, angles, and layouts that need to be preserved, as well as the colors, environments, and backgrounds you want to change. Multiple reference images can be used to express different visual requirements, but you should explain the role of each image to avoid treating conflicting compositions or styles as simultaneous goals.
Generation and editing each use dedicated image endpoints, and the output remains an image rather than an editable design project. Applications can receive image links or use Base64 image data; for tasks that require longer waits, asynchronous callbacks can be used to separate task submission from asset ingestion, making subsequent review and delivery easier.
Start with specific tasks to find where the model can make an impact.
Enter the campaign theme, target audience, product features, and placement format to generate a set of poster or social media image alternatives. Use text to specify the subject position, background mood, and copy space separately, then select compositions suitable for placement. Before delivery, add accurate brand elements and check whether the text in the image and product appearance meet requirements.
Enter the page objective, module order, brand colors, and desired interface style to create visual concepts for landing pages or application screens. It is suitable for discussing layout mood, illustrations, and information hierarchy; the deliverable is a concept image, while component dimensions, interaction states, typography specifications, and functional pages still need to be completed during design and development.
Use product photos or scene descriptions as input to explore seasonal backgrounds, lighting, and promotional scenes, and also create video opening frames and storyboard sketches. Prompts should specify the camera angle, subject position, and details to preserve; after obtaining static images, pass them into subsequent design or video production workflows for continued use.
Choose based on task complexity, input materials, and expected results.
When tasks involve everyday visuals, creative proposals, and multi-round direction exploration, Flare's speed-oriented approach better aligns with the work objectives. If you need to preserve product structure as much as possible, make fine adjustments to local content, or require higher consistency with reference images, you can compare GPT Image 2.5 Sunburst. It is recommended to use the same materials and instructions, and judge the trade-off by usable generated images rather than model names.
GPT Image 2 is a related previous-generation model, and its example results should not be directly regarded as Flare's results. gpt-image-2.5-flare and gpt-image-2.5-flare:official should also be used as different invocation IDs. They share Flare's basic positioning, but their parameters and billing must be configured separately; do not assume that all request formats are exactly the same merely by adding a suffix.
From a small-scale task to formal integration.
Clarify the objective, required inputs, and output requirements, and use real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand output quality and capability scope.
Answers to common questions about using gpt-image-2.5-flare.
Flare focuses on generation speed, making it suitable for everyday image creation, creative alternatives, and quick revisions; Sunburst focuses on high fidelity and fine-grained control. If the task emphasizes product details, reference image consistency, or complex editing, compare Sunburst first; if the main goal is to explore visual directions, you can start with Flare.
Use /openai/images/edits, explicitly set model to gpt-image-2.5-flare, and submit a single image URL or an array of URLs through image. In the prompt, clearly specify what to preserve and what to change, for example, keep the product angle and outline while changing only the background and lighting.
The range for n is 1–10, which is suitable for generating alternatives around the same brief. If response_format=b64_json is selected, only 1 image is supported; use the url return method when multiple images are needed. Batch generation still requires reviewing each result, and you cannot assume all images meet the same detail requirements.
Do not copy it directly. The mask workflow should use the gpt-image-2.5-flare:official endpoint that supports this method, and upload both the original image and the Alpha PNG mask through multipart. For basic Flare reference image editing, first use text to clearly define the modification scope; do not mix the two calling methods.
When exact pixels are required, write size as WIDTHxHEIGHT and follow the size limits; use auto when you only want to express composition intent. For long tasks, add callback_url, save the returned task_id first, then receive the completed result; callback handling should deduplicate by task ID to avoid duplicate storage.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with a clear goal and judge from real results whether it fits your work.
