ChatGPT Images 2.5 Prompt Guide: A Practical Method for Controllable Image Generation and Editing

Effective prompting for ChatGPT Images 2.5 is not about stacking impressive adjectives or searching for hidden magic keywords. OpenAI’s current image prompting guide emphasizes a simpler process: define the result, describe the subject and composition, specify the visual details, and state what must change or remain unchanged during an edit.[^1]

It is also useful to distinguish between two related names:

• ChatGPT Images 2.5 is the image creation experience in ChatGPT.

• GPT-Image-2.5 Flare and GPT-Image-2.5 Sunburst are the corresponding image models available through the OpenAI API.

OpenAI positions Flare for faster everyday generation and Sunburst for workflows that require higher-quality output and more precise editing. These choices should be validated against your own prompts and reference images rather than assumed from the model names alone.[^2][^3]

Choose the model before refining the prompt

Flare: when speed matters

Start by testing gpt-image-2.5-flare when you need fast drafts, rapid visual exploration, or everyday production.

Typical use cases include:

• Social media assets;

• Product concepts;

• Poster drafts;

• Creative exploration;

• High-volume iteration.

Sunburst: when precision matters

Test gpt-image-2.5-sunburst when you need stronger control over edits, subject preservation, complex layouts, or final production quality.

It is a better candidate for:

• Campaign creative;

• Detailed product imagery;

• Multi-region edits;

• Identity and product preservation;

• Text-heavy posters and diagrams.

OpenAI’s prompting guide recommends keeping the prompt, references, dimensions, and quality settings stable while comparing models. Evaluate instruction following, identity or product preservation, text accuracy, unwanted changes, consistency, latency, failures, retries, and accepted-image cost. Do not infer that a faster model is automatically cheaper. Some workloads use different token amounts even when model pricing rates are similar.[^1]

A maintainable prompt structure

A strong image prompt usually answers these questions:

1. What is the image for?

2. What is the main subject?

3. What is the subject doing?

4. Where does the scene take place?

5. How should the image be composed?

6. What should the lighting, materials, and color palette look like?

7. What text must appear exactly?

8. What must remain unchanged?

9. What should not appear?

OpenAI Academy notes that a good image prompt does not need to be long. One to three clear sentences may be enough to begin. The important details are the purpose, subject, action, setting, style, and constraints—not the number of adjectives.[^4]

A practical template looks like this:

Goal:
State the intended use and final deliverable.

Subject:
Describe the main person, product, object, or scene.

Action and relationships:
Explain what the subject is doing and how elements relate to one another.

Environment:
Describe the location, time, and background.

Composition:
Describe the aspect ratio, viewpoint, framing, placement, and negative space.

Lighting and materials:
Describe light direction, colors, materials, texture, and medium.

Text:
Put required wording in quotation marks and specify placement, size, color, and typography.

Preserve:
List identity, geometry, layout, lighting, labels, or other elements that must remain fixed.

Avoid:
List unwanted text, logos, watermarks, people, objects, or visual changes.

This is an organizational format, not a required syntax. OpenAI’s guide explains that natural paragraphs, structured text, JSON-like formats, instructions, and tags can all work. Choose the format that makes the requirements easiest to read and maintain.[^1]

Example: a product hero image

Create an e-commerce hero image for a premium coffee brand.

Show a glass of iced coffee on a pale stone table.
Use a three-quarter side view. Place the glass on the right side of the frame,
leaving clean negative space on the left for marketing copy.
Use soft window light to reveal the glass reflections, ice cubes, and coffee texture.
Keep the result realistic, restrained, and suitable for commercial product photography.
Do not add people, extra text, logos, or watermarks.

This prompt is effective because it specifies the product, camera relationship, placement, lighting, intended use, and exclusions without relying on vague style language.

Describe people and actions concretely

“Someone standing in a street” leaves too many decisions open. Add details such as:

• Full body, half body, or close-up framing;

• Whether the feet should be visible;

• Gaze direction;

• Hand-to-object interaction;

• Relative positions;

• Subject scale within the frame.

For example:

Create a realistic street photograph.

A young woman in a dark green jacket stands outside an old bookshop,
turning slightly toward the right. She holds an open book in her right hand
and looks down at the page.
Use a medium shot with the subject visible from head to knees.
Place the bookshop sign in the left background.

Instructions such as “full body visible, feet included” or “looking down at the open book” communicate a visible action more reliably than “natural pose” or “dynamic expression.”[^1]

Describe composition and light as visible properties

Words such as “cinematic,” “premium,” or “epic” are too broad by themselves. Specify:

• Eye-level, overhead, or low-angle viewpoint;

• Close-up, medium shot, or full-body framing;

• Subject placement;

• Negative space;

• Foreground and background relationships;

• Light direction;

• Shadow softness;

• Materials and surface texture.

Use an eye-level medium close-up.
Place the subject on the right side of the frame and leave roughly one-third
of the image empty on the left.
Soft side light enters from the upper left.
Preserve natural skin texture and keep the blurred background recognizable
as an indoor room with plants and wooden shelves.

Camera specifications can act as visual cues, but OpenAI’s guide cautions against treating them as guarantees of exact physical simulation. Describe the resulting appearance as well as any camera reference.[^1]

Treat image text as its own requirement

OpenAI recommends putting required copy in quotation marks and describing its placement, typography, color, size, and number of appearances. Always inspect spelling and legibility in the result.[^1][^4]

Create an urban coffee advertisement for young professionals.

The main headline must read exactly:
“MONDAY, MADE BETTER”

Render the headline once at the top center in a bold sans-serif typeface,
white, high contrast, and clearly legible.
Do not add any other text, logos, watermarks, or random letters.

For unusual words or brand names, spelling them explicitly can help. The more text an image contains, the more important it becomes to reduce decorative clutter and review the output carefully.

Separate changes from preservation constraints

For an edit, do not write only “change the jacket to black.” State the allowed change and the protected elements:

Change only the color of the person’s jacket to deep red.

Keep the face, hairstyle, skin tone, body proportions, pose, expression,
background, camera angle, composition, lighting, and shadows unchanged.
Do not add text, logos, accessories, or other people.

The official guide recommends explicitly naming what should change and what must remain fixed, including identity, geometry, layout, lighting, and labels.[^1]

Prompting alone cannot guarantee pixel-identical preservation. If a region must remain exactly unchanged, use deterministic compositing or another post-processing method rather than relying only on a natural-language instruction.[^1]

Assign roles to multiple reference images

Do not simply say “use these reference images.” Give every image a role:

Image 1 is the final scene and composition reference.
Image 2 is the clothing reference.
Image 3 is the product packaging reference.

Keep the spatial layout and camera angle from image 1.
Use the clothing from image 2.
Place the package from image 3 on the table in the right foreground.
Do not change the background structure or lighting direction from image 1.

OpenAI Academy recommends referring to images by order and using spatial language such as left, right, foreground, and background. More references are not automatically better; a small set with clearly defined roles is often easier to control.[^4]

Use sketches to preserve layout and intent

A sketch does not need to be polished. It should communicate:

• Subject placement;

• Object scale;

• Perspective;

• Major spatial divisions;

• Horizon or architectural structure;

• Direction of attention.

Turn this sketch into a realistic architectural interior image.

Preserve the exact room layout, wall positions, window positions, and perspective.
Use natural daylight, pale wood, and white walls.
Do not add rooms, people, text, or decorations that are not present in the sketch.
Remove all hand-drawn line artifacts.

The goal is to preserve the drawing’s spatial intent and add plausible materials and lighting, rather than asking the model to redesign the scene freely.[^1]

Keep API parameters separate from the prompt

The prompt describes creative intent. API parameters control the output. Common GPT Image 2.5 settings include:

ParameterPurposemodelChoose gpt-image-2.5-flare or gpt-image-2.5-sunburstqualityauto, low, medium, high, xhigh, or maxsizeChoose an aspect ratio or custom dimensionsbackgroundauto, opaque, or transparentoutput_formatSelect the output file formatoutput_compressionCompression for JPEG or WebP

Flare and Sunburst add the xhigh and max quality levels. Lower quality is useful for drafts and exploration; higher quality is appropriate for final assets when it actually improves the acceptance criteria. A higher setting is not guaranteed to improve every prompt.[^1][^2]

Common sizes include:

• 1024x1024 for square images;

• 1536x1024 for landscape images;

• 1024x1536 for portrait images;

• 2048x2048 for a 2K square;

• 3840x2160 for a 4K landscape image;

• 2160x3840 for a 4K portrait image.

Custom dimensions must satisfy the documented constraints: each edge must be no larger than 3840 pixels, both edges must be multiples of 16, the aspect ratio cannot exceed 3:1, and the total pixel count must fall between 655,360 and 8,294,400. Outputs above 3,686,400 pixels are considered experimental.[^1]

For transparent assets, set background="transparent" explicitly and use PNG or WebP. Verify the actual alpha channel after generation; a checkerboard pattern drawn into the image is not transparency.[^1]

Make one major change per editing turn

A controlled multi-turn workflow is:

1. Create an initial image with the correct subject and composition.

2. Pass the previous image into the next edit.

3. Change one major condition.

4. Repeat the most important preservation constraints.

5. Inspect the result before continuing.

First request:
Create a realistic bathroom product advertisement with a white shampoo bottle
on a pale gray stone counter, a green plant on the right, and soft morning light.

Follow-up:
Keep the bottle, counter, composition, and lighting unchanged.
Change only the background lighting to a warm winter evening.

Follow-up:
Keep everything else unchanged.
Move only the plant from the right side of the counter to the left.

OpenAI’s guide notes that repeated edits can still change details that were meant to stay fixed. If the image drifts, restate the critical constraints instead of adding unrelated detail.[^1]

Migrating from GPT Image 2 to GPT Image 2.5

Replacing the model name is not a sufficient migration plan. A reliable comparison should:

• Save representative production prompts;

• Save their reference images;

• Keep the original dimensions, quality, format, and prompts fixed;

• Test Flare or Sunburst;

• Compare subject preservation, text accuracy, editing scope, and transparency;

• Measure latency, failures, retries, and cost per accepted image;

• Roll out gradually and retain the previous model for rollback.

If GPT Image 2 already meets the quality requirement, the official guide recommends testing Flare first for a possible latency improvement. If GPT Image 2 is insufficient for a complex editing workflow, establish that Sunburst meets the quality target before testing Flare for speed. A workload-specific benchmark is more reliable than a general performance claim.[^1]

OpenAI’s announcement says ChatGPT Images 2.5 can reduce image-generation latency by up to 50% compared with Images 2.0, but this is not a fixed guarantee for every prompt or workload. The official guide recommends measuring the actual workflow.[^3][^1]

Common mistakes

Stacking style adjectives

“Cinematic, epic, ultra-detailed, premium, stunning” does not replace subject, composition, lighting, and material descriptions.

Combining conflicting requirements

For example, asking for an extremely minimal layout while adding many decorative objects, or asking to change only the background while also redesigning the subject’s clothing and pose.

Treating camera settings as absolute guarantees

Camera language is a visual cue. Describe the resulting framing, distance, perspective, and background behavior as well.

Relying on secret syntax

The current OpenAI guide does not require a fixed weighting syntax, tag system, or separate “negative prompt” field. Write exclusions as part of the prompt’s constraints.

Changing too many variables at once

If weather, clothing, background, typography, camera angle, and pose all change together, it becomes difficult to understand why the output changed. One focused revision at a time is easier to evaluate.

Final checklist

Before submitting a prompt, check:

• Is the intended use clear?

• Is the subject and action specific?

• Is the setting and time defined?

• Is the composition described?

• Are lighting, materials, and color direction clear?

• Is required text quoted and positioned?

• Are preservation requirements explicit?

• Are unwanted text, logos, and watermarks excluded?

• Does every reference image have a defined role?

• Are model, quality, and size configured separately?

• Does each edit change one main condition?

• Will you inspect text accuracy, subject preservation, and unexpected changes?

The strongest ChatGPT Images 2.5 prompts are not the longest ones. They are the ones that make the desired result, visual structure, and acceptance criteria clear. Start with the subject and composition, then add style, text, and constraints only where they help control the image.

Sources

[^1]: OpenAI Developers, Image prompting. GPT Image 2.5 model selection, prompt structure, references, editing, iteration, and migration guidance.
[^2]: OpenAI Developers, Image generation. Image-generation API parameters, dimensions, quality, background, output format, and request examples.
[^3]: OpenAI, Introducing ChatGPT Images 2.5. Product capabilities, API models, and release information.
[^4]: OpenAI Academy, Creating images with ChatGPT. Prompt fundamentals, text, references, iteration, and safe-use guidance.
[^5]: OpenAI Developers, GPT-Image-2.5 Sunburst. Sunburst capabilities, input modalities, quality settings, and API details.
[^6]: OpenAI Developers, GPT-Image-2.5 Flare. Flare capabilities and API details.