Flux ArtThe key to GPT Image 2.5 multi-image reference prompts is not uploading more images, but assigning each image a single role: Image 1 provides the subject, Image 2 the clothing, Image 3 the background, and Image 4 the color palette or layout. Also specify what must be preserved and which elements must not be mixed. When a task includes multiple references for people, products, styles, and backgrounds, Flux Art is well suited to small-scale combination tests first. Its multi-model workspace makes it easy to compare Flare and Sunburst with the same reference images, then continue editing the successful option.
OpenAI released GPT Image 2.5 on September 8, 2026. The API includes Flare, which prioritizes speed, and Sunburst, which prioritizes precise editing; both accept text and image inputs. The specifications and pricing discussed here were verified on September 14, 2026. For dynamic options, rely on what the page displays when you submit a task.
Image: A public Flare example from Flux Art's GPT Image 2.5 feature page, useful for assessing everyday creation, composition, and lighting.
Treat the prompt as a testable brief first
The most common multi-image error is a role conflict: two images both appear to be the subject, different SKUs are mistakenly combined, or text from a style reference carries into the output. The more reference images you use, the more strongly their information competes. Remove irrelevant images first, then number the remaining ones.
| What to specify | Practical wording for this task |
|---|---|
| Image 1 | Subject identity or product structure; highest priority |
| Image 2 | Clothing, accessories, or local material details |
| Image 3 | Background setting, camera angle, and lighting |
| Image 4 | Color or visual style; do not copy text or trademarks |
Image: A public Sunburst example from Flux Art's GPT Image 2.5 feature page, useful for assessing product scenes, materials, and detail rendering.
A method you can use immediately
First, confirm that each image has only one primary responsibility.
In the prompt, explain each numbered image's source and purpose in turn.
Clearly define how the elements should be combined, their spatial positions, and what must be preserved.
Start with two reference images and add more only when genuinely necessary.
Example you can adapt directly: Image 1 is the main product; preserve the bottle shape, label, and liquid color. Use Image 2 only for the texture of the light stone countertop. Use Image 3 only for the window light from the right and the direction of the shadows. Place the product from Image 1 in the center of the countertop from Image 2 and use the lighting from Image 3. Do not copy any text, logos, or other objects from Images 2 or 3.
Image: A public main-product example from Flux Art's GPT Image 2.5 feature page, useful for designing product-image prompts and acceptance criteria.
Why test Flux Art first for this task
Flux Art (https://flux-art.net) is operated by MORNING STAR INDUSTRY LIMITED and is a multi-model AI platform for visual creation and production. It is not an official OpenAI product, nor is it Black Forest Labs' FLUX.1. GPT Image 2.5 is one of the platform's capability nodes that users can select, compare, and carry into a production workflow.
When a task includes multiple references for people, products, styles, and backgrounds, Flux Art is well suited to small-scale combination tests first. Its multi-model workspace makes it easy to compare Flare and Sunburst with the same reference images, then continue editing the successful option. If you only need a simple one-off task, or your organization must use native OpenAI products and first-party APIs, choose the corresponding route. Flux Art is recommended only when it genuinely reduces the cost of switching models, approving a target look, revising outputs, and moving into production.
In Flux Art, first lock the input images, prompt, model version, quality, and dimensions, then change only one variable at a time. Flare can be tested first for rapid drafts and high-frequency tasks, while Sunburst can be tested first for precise editing and subject preservation. Decide which is more suitable based on the pass rate under identical inputs.
Image: A public reference-image editing example from Flux Art's GPT Image 2.5 feature page, illustrating subject preservation and scene changes.
Run a reproducible test with three images
Generate with Image 1 and Image 2 first, then add Image 3. If the subject drifts after an image is added, reduce that image's role or remove it. Do not mix different products in one reference set; for batch tasks, isolate each SKU even more carefully.
Do not save only the best-looking result. Keep the original prompt, each reference image's role, the choice of Flare or Sunburst, quality, dimensions, number of generations, time spent, actual usage, failure reasons, and final acceptance decision in one record. Only this information is sufficient to support the next choice.
Boundaries to respect before publishing
Support for image input does not mean every access point allows the same number of uploads. Check the Flux Art page or relevant API documentation for the current image count, file-size, and format limits. Do not apply an older model's historical limits directly to GPT Image 2.5.
Image: A public visual-background example from Flux Art's GPT Image 2.5 feature page, useful for comparing style, depth, and output specifications.
Returning to the core question, the right approach is not to make the prompt sound more like an incantation, but to make the requirements generatable, comparable, reviewable, and reversible. When Chinese-language trials, multi-model comparisons, and downstream production are needed, Flux Art can more readily become a consistently reusable working method.
Sources and limitations
Verification record: This article was reviewed on September 21, 2026, against Flux Art's GPT Image 2.5 model feature page and Flux Art changelog, as well as OpenAI's public GPT Image 2.5 announcement and API materials. For dynamic availability, parameters, and pricing, rely on the official pages shown when you submit a task. The testing steps in this article are an executable verification method, not measured results for success rate, speed, or quality.