GPT Image 2.5 Guide: Models, Parameters, and Prompting Techniques

OpenAI's ChatGPT Images 2.5 addresses a common frustration with AI image editing: changing one element often alters others unintentionally. When you ask to swap a garment, the subject's face may change; when you edit text, the original composition may shift. The update focuses on preserving the people and objects in reference photos, so that changing the scene, composition, or visual style keeps the original subjects recognizable. Lighting and material textures have also been improved. Beyond preservation, the release highlights better handling of complex instructions, visual style, text rendering, layout, and transparent backgrounds. OpenAI reports that users create more than 3 billion images per week through ChatGPT Images and the GPT-Image API models. Generation latency is reduced by up to roughly 50% compared with Images 2.0, though actual speed depends on prompt length, reference images, size, and quality settings. The features are rolling out across ChatGPT, ChatGPT Work, and Codex on desktop, mobile, and web, with availability varying by plan. OpenAI also notes that repeated edits can still alter details you wanted to keep, so pixel-exact regions may require compositing confirmed edits back onto the original image.

ChatGPT Images 2.5 is the image creation and editing experience inside ChatGPT. For developers, two API models are available: GPT Image 2.5 Flare and GPT Image 2.5 Sunburst. Both support generation, editing, and transparent backgrounds, with improved precise editing and subject preservation. Flare is positioned as the default choice for most applications, prioritizing speed while maintaining quality comparable to GPT Image 2. Sunburst is oriented toward fine-grained editing and control in longer workflows. The announcement and the prompting guide describe Flare's quality comparison slightly differently; the guide frames it as comparable quality with a speed priority. A practical approach is to test Flare first using the same prompt, references, and output settings as Sunburst, and switch only if quality remains acceptable and the speed gain is real. Faster generation does not automatically mean lower cost, so pricing should be checked separately. The release also introduces C2PA metadata and invisible watermarking to help identify image provenance.

A prompt is the instruction you give the model for generation or modification. The official guide recommends starting with the outcome you want, then describing the subject, composition, style, and constraints. When editing, state clearly what should change and what must stay the same. First, state the outcome: name the subject and purpose, such as a product photo, advertisement, or diagram, and specify composition, aspect ratio, and key element placement. Second, choose readable formats: short sentences, paragraphs, structured lists, or JSON-like layouts all work, and no special syntax is required. Third, describe visible details: specify materials, lighting, colors, and the rendering style; asking for a 'photorealistic' result helps, and camera parameters can suggest appearance without guaranteeing a true simulation. Fourth, describe people and actions: indicate framing such as full body including feet, gaze direction, and interactions with objects. Fifth, provide exact text: put the wording in quotes, specify placement and typography, spell out uncommon words, and request no additional text. Sixth, separate changes from preservation: state what to modify, then list features, structures, layouts, lighting, or labels to keep, and what not to add. Seventh, assign roles to reference images by number, explaining whether each provides the subject, style, clothing, or background. Eighth, iterate turn by turn: use the previous output as the next input, make one change at a time, and restate key constraints when results drift.

The official guide provides examples across photography, infographics, advertising, logos, historical scenes, comics, interface mockups, educational graphics, and presentation slides. Photorealistic portrait: an aged sailor on a small fishing boat, with visible skin texture, weathered tattoos, a dog on deck, shot as a 35mm film look with a 50mm lens, soft coastal daylight, shallow depth of field, and explicit avoidance of heavy retouching. Infographic: an explanation of how a fully automatic coffee machine works, covering bean hopper, grinder, weighing, water tank, and boiler, with both technical and visual clarity. Advertising with text: a streetwear brand 'Thread' poster showing friends gathered with the tagline 'Yours to Create.' appearing exactly once, with no watermarks or extra text. Logo design: an original, simple, flat logo for the bakery 'Field & Flour', with a fully transparent background, centered composition, and clear legibility at small and large sizes. Historical scene: a realistic outdoor crowd at Bethel, New York, on August 16, 1969, with period-accurate clothing and staging. Four-panel comic: a pet left alone, reacting, exploring, and greeting the owner's return, with each panel describing a specific action. Application interface: a realistic phone mockup for a local farmers market with a header, vendor list with photos, a 'Today's specials' section, and hours information. Scientific diagram: a high school graphic titled 'Cellular Respiration at a Glance' showing glycolysis, the Krebs cycle, and the electron transport chain with labeled molecules, requiring verification of scientific accuracy before classroom use. Pitch slide: a 'Market Opportunity' slide with TAM/SAM/SOM concentric circles, bar chart, and footnotes; the guide notes that the figures and citations are fictional design inputs to be replaced with verified data.

Editing examples require input images. Translating an infographic while preserving everything else: 'Translate the text in the infographic into Spanish. Do not change any other part of the image.' Style transfer: using a pixel-art reference to create a new subject, such as a man riding a motorcycle on a white background, while keeping the style. Outfit change with identity preservation: using a person's photo and clothing references, the prompt instructs not to alter the face, features, skin tone, body shape, pose, or identity, to replace only the clothing with natural fit and matching lighting, and to leave background, camera angle, and image quality unchanged. Reference compositing: placing a dog from a second image into a street scene beside a woman, keeping the lighting, composition, and background consistent. Transparent-background extraction: isolating a product centered on a fully transparent background with clean edges, no shadows or backdrops, preserving label legibility; this requires PNG or WebP output and the transparent background parameter. Sketch-to-photo: converting a drawing into a photorealistic image while preserving layout, scale, and perspective, adding plausible materials and light without new elements or text. Object removal: removing the flower from a man's hand while changing nothing else. Scene insertion: placing a woman from a museum photo into a candid, unposed scene of fleeing a bear at Yosemite at dusk, with natural lighting and no cinematic stylization.

Multi-turn workflows build on previous outputs. In the advertising example, a shampoo billboard at sunset is generated first with the exact text 'Fresh and clean' appearing once, bold sans-serif, high contrast, and no watermarks or logos. The next turn changes only the condition with a single short instruction, such as making it look like a snowy winter evening; the guide shows that brief follow-ups can rely on context, but if drift occurs, key constraints should be restated. For character consistency, the guide demonstrates generating a children's book hero, a young woodland figure with a green hooded tunic, brown boots, a small pouch, and a little wooden bow used only to help. The style is soft watercolor with proportions suited to picture books, no text, and no watermark. The next scene reuses the same character, rescuing a startled squirrel after a winter storm, with explicit consistency requirements for the same tunic, facial features, proportions, palette, and temperament, and an instruction not to redesign the character.

The guide also covers everyday and commercial uses. Furniture replacement: swapping only a white chair for a wooden one in a room photo while preserving camera angle, indoor lighting, floor shadows, and surroundings, with photorealistic contact shadows and fabric texture. Holiday greeting card: a nostalgic Christmas scene with an old teddy bear in a memory box beside a window with falling snow, warm soft cinematic light, shallow depth of field, and the exact card text 'Merry Christmas — some memories never fade.' Collectible product design: a retro toy propeller airplane in a blister pack, with rounded wings, worn paint, studio lighting, clear label printing, and the packaging text 'Christmas Memories Edition' appearing exactly as written. In each case, the prompts emphasize original designs, avoidance of trademarks and watermarks, and precise text rendering.

The guide's examples are paired with API settings passed separately from the prompt. Custom dimensions must have each side at most 3,840 pixels, both sides as multiples of 16, an aspect ratio no greater than 3:1, and a total pixel count between 655,360 and 8,294,400; outputs above 3,686,400 total pixels are experimental. The recommended workflow is to choose the model first, then adjust quality: test higher quality if results fall short, and reduce settings afterward to balance speed and cost. Higher effort levels such as xhigh or max should be used only when they measurably improve results and acceptable wait times. Transparent images must be saved as PNG or WebP with the alpha channel intact; output_compression applies only to JPEG or WebP, not PNG. Developers should set background to transparent explicitly, since writing 'transparent' in the prompt alone does not configure the parameter. Checkerboard patterns drawn into an image are not real transparency.

After generating or editing, verify the following. Confirm that the requested generation or modification was completed. Check that text is accurate, legible, and free of extra copy. Confirm that people's features, product shapes, labels, and reference details were preserved. Ensure that unspecified areas were not altered. In infographics and diagrams, verify numbers, labels, arrows, and factual relationships. Confirm that transparent backgrounds genuinely exist and remain intact after multiple edits. Because repeated edits can drift, pixel-critical regions may need manual compositing back onto the original image.

ChatGPT Images 2.5 improves preservation, editing precision, text and layout handling, and speed, and OpenAI's prompting guide turns these capabilities into a repeatable method: state the outcome, describe visible details, specify exact text, separate changes from preserved elements, assign reference roles, and iterate one change at a time. The 24 official examples provide adaptable starting points across generation, editing, multi-turn consistency, and practical applications. Verify every output carefully, especially text, scientific facts, and transparency. For code integration, model migration, and pricing, consult OpenAI's API documentation and the official prompting guide directly.