GPT Image 2.5 Walkthrough: From First Prompt to a 4K Product Image

TLDR GPT Image 2.5 supports text-to-image and reference-based workflows through a compact parameter set: prompt, aspect_ratio, resolution, and optional input_urls. This walkthrough shows how to plan a 4K product visual, preserve reference details, structure edits, select Flare or Sunburst, and handle documented limits around files, ratios, and text.

Key Takeaways

• Use Flare as the default variant, while Sunburst suits more premium visual workflows.
• Keep the prompt structured around scene, subject, details, and constraints.
• Reference uploads support JPEG, PNG, WEBP, and JPG files up to 30MB each, with 16 files maximum.
• Choose from 1K, 2K, and 4K, but four listed aspect ratios support 1K only.
• Treat text rendering and unmeasured latency claims as evaluation areas, not guaranteed outcomes.

1. Choose the output before writing the prompt

GPT Image 2.5 is presented as an image generation and editing model from OpenAI. Its documented surface supports text-to-image and image-to-image workflows. Before writing a prompt, decide whether the job needs a new composition or a controlled transformation of existing material.

The model page lists two variants:

• GPT-Image-2.5 Flare: the default choice for most applications, with lower-latency positioning for creator content, social visuals, product experiences, visual search, rapid prototyping, and higher-volume generation.
• GPT-Image-2.5 Sunburst: aimed at premium visual workflows, including campaign assets, branded visuals, polished product imagery, and editing tasks where tighter control matters.

These descriptions provide a useful starting rule. Choose Flare when the workflow values iteration and volume. Choose Sunburst when the brief prioritizes a more controlled creative process. The page does not provide a measured generation time, so avoid treating the lower-latency description as a guaranteed number.

For the model page and current parameter surface, use <a href="https://kie.ai/gpt-image-2-5">Kie AI</a>. The page exposes the expected fields directly, which makes it suitable for checking supported values before building an application request.

2. Start with a structured text-to-image prompt

The text-to-image form requires three fields:

• prompt
• aspect_ratio
• resolution

The prompt accepts up to 20,000 characters. That ceiling is generous, but a long prompt is not automatically a clear prompt. Separate visual priorities so the model can identify the subject, composition, and restrictions.

For a 4K product image, start with a brief like this:

A premium ceramic tea set on a pale stone table, one ivory teapot and two matching cups, soft morning window light from the left, subtle natural shadows, warm cream and muted green palette, clean editorial product photography, centered composition with open space above the set for a headline, realistic ceramic glaze and fine surface texture, no people, no extra objects, no watermark. Preserve the exact shape and color relationship between the teapot and cups.

This example gives the model a subject, materials, lighting direction, palette, layout, and exclusions. It also leaves room for a later edit. The phrase “open space above the set” is more actionable than simply requesting a “nice composition.”

Set resolution to 4K when the target aspect ratio supports it. The available values are 1K, 2K, and 4K. For a horizontal product banner, 16:9 is a practical starting point. For a square catalog visual, use 1:1. The available aspect-ratio values also include 3:2, 2:3, 16:9, 9:16, 4:3, 3:4, 21:9, 27:16, 16:27, 9:8, and 8:9.

The four ratios 27:16, 16:27, 9:8, and 8:9 support 1K only. If a brief requires 2K or 4K, choose one of the other listed ratios instead.

3. Add reference images when the subject must remain recognizable

Text alone is not always the right starting point for an AI art workflow. A reference image can establish the product shape, room layout, outfit contours, color direction, or object placement before the prompt asks for a new environment.

The image-to-image form adds input_urls as an array. The documented upload rules allow JPEG, PNG, WEBP, and JPG files. Each file can be up to 30MB, and the maximum is 16 files.

A reference-based prompt could read:

Use the supplied product photographs as the source of truth for the bottle silhouette, cap proportions, label placement, and amber glass color. Place the bottle on a dark green stone pedestal in a quiet studio setting. Keep the bottle fully recognizable and upright. Add soft side lighting, a controlled reflection, natural contact shadow, and a muted warm background. Do not redesign the label or add text.

This wording tells GPT Image 2.5 what to preserve and what to change. “Use the supplied product photographs” establishes the reference relationship, while the preservation list limits unwanted redesign.

The model page describes stronger reference fidelity for recognizable subjects, richer textures, and more natural lighting. It also describes support for sketches that communicate layouts, contours, shapes, and placement. For production work, confirm the current sketch input format and request details in the latest documentation before depending on that workflow.

A reference image should not be treated as a guarantee of perfect identity preservation. Community testing on September 9, 2026, produced different conclusions. @exploraX_ reported that a single phone photo survived seven prompt edits, with the sofas, rug, curtains, and floor seam preserved through changes to the room lighting. @Mho_23, also writing on September 9, said the model still showed a noticeable AI look without reference images. These observations support a practical rule: supply references when the subject or visual direction matters.

For another approach to building polished high-resolution visual assets, see Seedream 5.0 Pro Tutorial: Shipping Cinematic 4K Images Through Emix. The workflows differ, but both benefit from deciding the final composition and resolution before refining prompts.

4. Make one focused edit at a time

GPT Image 2.5 is documented for targeted edits to backgrounds, products, text, colors, materials, and individual objects. The safest editing pattern is to identify the locked elements first, then name one change.

For the bottle example, use a sequence like this:

1. “Keep the bottle silhouette, label placement, camera angle, pedestal size, and shadows unchanged. Replace only the dark green background with a warm gray studio wall.”
2. “Keep the same bottle, label, composition, and lighting. Change only the amber glass to deep cobalt blue.”
3. “Keep all existing objects and their positions. Add a small soft highlight on the bottle shoulder without changing the label.”

This approach matches community guidance from @eng_khairallah1 and @cgtwts, who recommended explicitly locking the existing frame and naming the desired change. They also suggested preserving size, angle, lighting, and shadow when replacing a single object.

Multi-turn continuity is one of the model’s documented strengths. The page describes better consistency across repeated edits, with later instructions carrying earlier adjustments forward. Community testing by @exploraX_ provides a useful but limited observation: the room structure survived seven edits, while text still broke. That distinction matters for brand work. Structural continuity may be strong even when lettering needs separate checking.

Do not combine every correction into one giant instruction. If the product is wrong, fix the product. If the background is wrong, fix the background. If copy is wrong, isolate the copy request and inspect it separately.

5. Handle text and complex briefs carefully

The documented prompt surface supports complex instructions involving multiple subjects, spatial relationships, text elements, visual styles, layout requirements, real-world information, transparent backgrounds, and multiple visual requirements.

A poster-style prompt might be:

Create a vertical event poster with a centered ceramic tea set in the lower two-thirds, pale cream background, muted green border, soft morning light, and generous empty space at the top. Render the exact headline “QUIET MORNING” in uppercase serif lettering, centered and dark green. Keep the headline separate from the product, with no additional words, logos, or decorative objects.

Putting exact copy in quotation marks follows practical guidance from @Mnilax, who also recommended separating edits instead of requesting many changes simultaneously.

Text remains a genuine caveat. On September 9, 2026, @exploraX_ reported successful room preservation across seven edits but said the model still failed on text. Another community comparison by @aresotik described error-free rendered text and cleaner product detail in their Higgsfield tests. These reports conflict, so the responsible workflow is verification. Render the image, zoom into every word, and keep a manual correction path for important labels or headlines.

For a production asset, treat generated lettering as provisional until checked against the exact copy. A visually strong image can still fail a brand requirement if one character, spacing relationship, or product label is incorrect.

6. Evaluate Flare and Sunburst with a controlled test

A useful comparison should hold the prompt, reference images, aspect ratio, and resolution constant. Change only the model variant. Use at least four prompt categories:

• A text-to-image product scene
• A reference-based product edit
• A layout with exact headline text
• A multi-object composition with lighting and material constraints

Record subject preservation, composition changes, text accuracy, material detail, and unwanted additions. Do not infer a winner from one image.

Community testing offers mixed signals. @thefinnmckenty reported on September 9 that Flare and Sunburst performed well in Flora examples, with Flare slightly better in those tests. @Waguri_Kaoruko8 reported the opposite direction in their own testing, finding 2.5 worse than 2.0 for style generation, rendering quality, and unwanted “slop.” @DeepBlueX0 described a substantial improvement over 2.0 in image quality, noise, and prompt comprehension, while acknowledging the judgment came from the shown image.

These results are useful signals, not a substitute for testing your own image categories. The model page positions Flare for lower-latency, higher-volume work and Sunburst for premium visual control. The community evidence does not establish a universal quality ranking.

Latency needs the same caution. @bridgemindai repeated a claim that Flare has 50% lower latency than Image 2, but said testing was still underway. @pbbakkum also described Flare as the lower-latency variant without reporting a measured generation time. Treat the percentage as an unverified community claim, not an application-level service guarantee.

7. Plan an API workflow around the documented fields

For text-to-image, the request model exposes:

• prompt: a string with a 20,000-character limit
• aspect_ratio: one of the listed ratios or auto
• resolution: 1K, 2K, or 4K

For image-to-image, add:

• input_urls: an array of reference image URLs

The page labels the output type as an image. It also provides a JSON editor and expected-field view, which can help catch unsupported parameter values before integration.

A practical implementation sequence is:

1. Validate the prompt is present and within the 20,000-character limit.
2. Confirm every reference file uses JPEG, PNG, WEBP, or JPG.
3. Reject files over 30MB and requests containing more than 16 files.
4. Check whether the selected aspect ratio allows the requested resolution.
5. Store the original prompt, variant, aspect ratio, resolution, and references for reproducibility.
6. Send later edits as separate instructions that name preserved elements.
7. Review generated text and recognizable subjects before publishing.

Community discussion also raises a cost-planning consideration. @kr0der estimated that GPT Image 2.5 could cost roughly 75% less than 2.0 for medium and high quality because it uses fewer output tokens, despite the same price per million tokens. That was presented as an estimate, not an independently verified benchmark. The target page facts supplied here do not provide a definitive price table, so confirm current billing details before making budget commitments.

8. Use this final checklist before publishing

Before treating a GPT Image 2.5 output as a finished AI art asset, check the following:

• Is the chosen variant appropriate for iteration or premium control?
• Does the prompt identify the scene, subject, details, and constraints?
• Is the aspect ratio compatible with the required 1K, 2K, or 4K resolution?
• Are all reference files within the 30MB limit and the 16-file maximum?
• Did each edit preserve the elements that the prompt asked to lock?
• Is every word in the image accurate?
• Did the model add objects, marks, or background details that were not requested?
• If the asset is branded, does the product remain recognizable after refinement?

GPT Image 2.5 offers a compact workflow for generation, references, and iterative editing. Its strongest documented use case is controlled visual development: establish the subject, define the frame, provide references when needed, then refine one variable at a time. The main caveats are equally practical. Text still requires inspection, community quality reports conflict, and latency or cost claims should be measured in the environment where the model will run.