Getting Started with GPT Image 2.5 — A Practical Guide to Coloring-Ready Line Art

TLDR GPT Image 2.5 supports text-to-image and image-to-image workflows with prompts up to 20,000 characters, resolutions from 1K to 4K, and 12 named aspect ratios. For coloring pages, begin with a structured black-and-white line-art brief, use references when consistency matters, and refine one change at a time.

Key Takeaways

• Flare is the documented default option for lower-latency generation; Sunburst targets more polished creative and editing workflows.
• Text-to-image exposes prompt, aspect_ratio, and resolution; image-to-image adds input_urls.
• The image reference form accepts JPEG, PNG, WEBP, and JPG files, with up to 16 files at 30MB each.
• Four narrow aspect ratios support 1K only: 27:16, 16:27, 9:8, and 8:9.
• Community testing found strong subject preservation across seven edits, but text and unreferenced image quality remain caveats.
• A coloring-page prompt should define open spaces, clean contours, a white background, and the details to exclude.

What GPT Image 2.5 Exposes for This Workflow

GPT Image 2.5 is presented on Kie.ai as an image generation and editing model from OpenAI. Its documented surface covers text-to-image and image-to-image workflows, with controls for the prompt, aspect ratio, resolution, and optional reference images. To inspect those controls directly, open gpt-image-2-5 and select the relevant input form.

The text-to-image form has three expected fields: prompt, aspect_ratio, and resolution. The image-to-image form adds input_urls, represented as an array. Both forms offer auto, 1:1, 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 narrow formats at the end support 1K only. Other listed ratios support 2K and 4K as well.

That range matters for coloring pages because the intended use affects the page shape. A portrait worksheet usually benefits from 2:3, 3:4, or 9:16. A square activity card can use 1:1. Landscape scenes may fit 3:2 or 16:9. Choose the ratio before refining the prompt so later edits do not require a different composition.

The prompt field allows up to 20,000 characters. A long prompt is not automatically a better prompt, though. For line art, a short hierarchy is easier to inspect: scene first, subject second, style details third, and constraints last.

Step 1: Choose Flare or Sunburst

The model page lists two GPT Image 2.5 options.

GPT-Image-2.5 Flare is described as the default choice for most applications and the lower-latency option. It is positioned for creator content, rapid prototyping, visual search, product experiences, and higher-volume generation.

GPT-Image-2.5 Sunburst is positioned for premium visual workflows. The documented examples include refined branded visuals, production-ready campaign assets, and polished product imagery.

For a coloring-page workflow, start with Flare when you are exploring several scene ideas. Move to Sunburst when the composition is already established and the priority is a more controlled refinement process. This is a workflow choice based on the documented positioning, not a measured timing comparison.

Community evidence supports caution around the latency language. On September 8, 2026, @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 50% as a product claim until your own usage data confirms it.

Step 2: Write a Coloring-Page Prompt

A useful prompt separates what should appear from what should remain blank. GPT Image 2.5 is documented as handling complex instructions involving layout, style, text, transparent backgrounds, and multiple visual requirements. That does not remove the need to describe the coloring-page constraints explicitly.

Try this text-to-image prompt:

Create a black-and-white coloring page for children ages 6–8: a friendly red panda sitting beneath a large oak tree, centered composition, bold clean outer contours, simple interior lines, large open areas for coloring, clear separation between the animal, tree, grass, and clouds, white background, no gray shading, no gradients, no crosshatching, no color, no border text, no watermark.

The prompt uses several practical controls:

• Subject: red panda and oak tree.
• Composition: centered.
• Line treatment: bold outer contours and simple interior lines.
• Coloring constraints: large open areas and clear object separation.
• Exclusions: gray shading, gradients, crosshatching, color, text, and watermarks.

For a more detailed page, add structure without filling every surface:

Black-and-white printable coloring-page line art of a small lighthouse on a rocky coast, portrait 2:3 composition, lighthouse in the upper middle, three large foreground rocks, calm waves drawn with a few separated contour lines, five open clouds, thick outer outlines, restrained interior detail, spacious regions for crayons, white background, no solid black fills, no gray tones, no realistic texture, no lettering.

The second example uses 2:3 because the ratio is directly available in the documented form and suits a portrait layout. Select 2K when you need a larger working image and do not need the 1K-only narrow ratios. Select 4K only when the workflow benefits from the highest listed resolution; the facts provided here do not establish a universal print requirement for any particular resolution.

Keep text out of the first generation unless it is essential to the composition. Community testing found that GPT Image 2.5 could produce error-free rendered text in one product-focused test, but separate testing reported broken text after a successful seven-edit room-preservation sequence. For a coloring page, unnecessary lettering creates another failure point.

Step 3: Add Reference Material When the Subject Must Stay Consistent

The image-to-image form accepts reference files in JPEG, PNG, WEBP, and JPG formats. It supports a maximum of 16 files, with a maximum file size of 30MB per file. The documented input is an input_urls array, so the reference workflow is distinct from a prompt-only request.

Use one reference for a basic shape or character direction. Use multiple references when the page needs several visual anchors, such as:

1. A character reference for face and body proportions.
2. A location reference for the main scene layout.
3. A pose reference for movement or posture.
4. A prop reference for a recognizable object.

The model page describes stronger reference fidelity and more consistent preservation of recognizable subjects. Community testing gives that claim useful context. On September 9, 2026, @exploraX_ began with one phone photo and made seven prompt edits. The sofas, rug, curtains, and floor seam reportedly survived each edit, and the room was relit when a pendant light changed to track lighting.

That observation comes from one community workflow, not a guaranteed result for every subject. For coloring pages, write preservation instructions directly into the prompt:

Keep the same character face, head shape, body proportions, pose, and camera framing. Convert the existing image into clean black-and-white coloring-page line art. Remove color, shading, gradients, and surface texture. Preserve the character’s recognizable features and leave broad open areas for coloring.

Reference material can also reduce the default AI look in an unreferenced generation. On September 9, 2026, @Mho_23 recommended supplying inspiration photos for color grading and creative direction, while also reporting that unreferenced results could still look noticeably artificial. For this use case, a reference can guide proportions, composition, or visual direction even when the final page removes color.

Related reading on another model’s prompt structure may help when you want to compare approaches: Seedream 5.0 Pro Tutorial: Turning Cinematic Prompts Into Coloring-Ready Line Art. The useful connection is prompt organization, not an assumption that the two models share the same controls.

Step 4: Refine One Change at a Time

The model page emphasizes targeted edits and multi-turn refinement. It describes workflows that change backgrounds, products, text, colors, materials, or individual objects while preserving surrounding elements. For line art, the same approach can help isolate problems such as a closed coloring region, an overly detailed background, or a missing object.

Begin with a preservation clause:

Lock the existing frame, character proportions, line weight, camera angle, and white background. Change only the tree canopy by reducing interior lines and leaving larger open spaces for coloring.

For an object replacement, be equally specific:

Keep the same face, pose, size, angle, lighting direction, and shadow placement. Replace only the ball with a kite. Preserve the rest of the composition and keep the result as black-and-white line art.

The community recommendations from @eng_khairallah1 and @cgtwts follow this pattern: name what must stay fixed, then identify one requested change. @Mnilax also recommends splitting edits into separate changes rather than combining several modifications in one instruction.

Inspect each revision for four items:

• Did the main subject retain its recognizable features?
• Did the requested object change occur without moving nearby objects?
• Are the coloring spaces still open?
• Did text, texture, or shading appear when it was not requested?

The seven-edit preservation observation is encouraging, but the reported text failure shows why each revision needs a visual check. A stable room layout does not guarantee stable lettering or clean coloring regions.

Step 5: Select Resolution and Aspect Ratio Deliberately

The available resolutions are 1K, 2K, and 4K. The available aspect-ratio controls include auto plus 12 named ratios. The four formats 27:16, 16:27, 9:8, and 8:9 support 1K only, while 2K and 4K are available for the other listed ratios.

Use this simple selection guide:

Coloring-page use	Suggested ratio	Resolution decision
Square activity card	1:1	Start at 1K or 2K
Standard portrait scene	2:3 or 3:4	Use 2K for a larger working image
Tall worksheet	9:16	Use 1K, 2K, or 4K
Wide landscape scene	3:2 or 16:9	Use 2K or 4K when supported
Narrow banner composition	27:16 or 16:27	1K only

The table is a planning aid, not a claim about print dimensions. The documented controls specify resolution labels rather than physical paper sizes. If the page will be printed, inspect the exported image and the final page layout separately.

Documented Strengths and Practical Caveats

The model page describes sharper detail, richer textures, natural lighting, stronger reference preservation, and more accurate handling of complex visual instructions. Some community observations align with those claims. On September 9, 2026, @noclipepe compared GPT Image 2.5 and Nano Banana 2 with the same four prompts. They reported that GPT Image 2.5 completed all four and understood the requests better in that comparison.

Other reports were less positive. @Waguri_Kaoruko8 wrote on September 8, 2026, that their own tests found GPT Image 2.5 worse than GPT Image 2.0 for style generation, rendering quality, and unwanted visual artifacts. @Mho_23 described the improvement over 2.0 as slight without reference images. These conflicting observations argue against treating a single sample as proof of consistent superiority.

For character-oriented pages, @thehypedotnews reported finer details in matched character sheets, including a more intricate mouth grille, finger-joint rings, and segmented forearm details. @renoiseai separately described consistency across tested movements as impressive. Those details may help reference-driven character work, but they can also add unwanted complexity to a coloring page. State the desired line density instead of assuming more detail is better.

API cost claims also need care. On September 9, 2026, @kr0der estimated that GPT Image 2.5 could cost roughly 75% less than GPT Image 2.0 for medium and high quality because it uses fewer output tokens, while saying the price per million tokens was the same. That was an estimate, not an independently verified benchmark. The supplied facts do not provide a price table, so do not use the estimate as a budget guarantee.

Access was inconsistent in early community reports. On September 9, 2026, @koltregaskes said they did not yet have access and questioned whether availability was limited in the UK. On September 8, 2026, @blue_pen5805 reported that ChatGPT metadata still identified generated images as 2.0 and that the API model was not yet appearing. Check the current model availability shown in the interface before planning a production pipeline.

A Repeatable Evaluation Plan

Before adopting GPT Image 2.5 for a coloring-page series, run a small controlled set instead of relying on one attractive image.

1. Write four prompts using the same scene structure: subject, composition, line style, open areas, and exclusions.
2. Generate each prompt with Flare at 1K.
3. Repeat the set with Sunburst at 1K.
4. Test one reference-driven version using a JPEG, PNG, WEBP, or JPG image.
5. Apply three single-change edits to each selected result.
6. Inspect subject preservation, line clarity, open coloring regions, text behavior, and unwanted shading.
7. Repeat the best prompt at 2K and 4K where the chosen aspect ratio supports them.
8. Record the variant, ratio, resolution, prompt revision, and any failed constraint.

This plan does not claim a particular output or latency. It creates comparable evidence for your own line-art requirements. Keep a failed sample in the record, especially when it reveals text breakage, closed spaces, subject drift, or extra texture.

Final Prompt Checklist

Before selecting Run, confirm that the prompt includes:

• The subject and its position in the frame.
• The intended aspect ratio, such as portrait 2:3.
• Black-and-white line-art instructions.
• Bold outer contours and restrained interior lines.
• Large open areas suitable for coloring.
• A white background.
• Explicit exclusions for gray tones, gradients, crosshatching, and solid black fills.
• Preservation instructions when using input_urls.
• One requested edit at a time.
• No unnecessary text elements.

GPT Image 2.5 offers a broad documented image surface, but coloring pages require narrower constraints than general image generation. Start with a clear 1K composition, add a reference when subject fidelity matters, and use 2K or 4K only after the line structure works. That sequence keeps the workflow measurable while leaving room for controlled refinement.