We’ve been prompting GPT Image 2.5 all wrong

I’ve spent the last couple of weeks hammering the new GPT Image 2.5 API for a production workflow, trying to get consistent multi-turn edits and text rendering. The biggest takeaway? All our old prompting habits—keyword stuffing, vague adjectives like "epic masterpiece 8k"—actually degrade the output on these new models. The fundamental shift with 2.5 is that generation and editing are now entirely dictated by a strict "Change vs. Preserve" rule. In the past, if you wanted to alter an image, you'd just say "change the background to a beach." If you do that in 2.5, the model will often hallucinate a slightly different subject. You have to explicitly name the one target to change, and exhaustively list what must not change (e.g., "Replace only the background. Preserve the exact identity, pose, clothing, camera angle, and existing lighting direction"). I learned the hard way that if you drop that preservation list on turn 3 of an editing loop, the details instantly drift. This strictness also applies to how it handles multiple reference images. If you just pass an array of images to the endpoint, the model blends them into an unpredictable hybrid. What actually works is assigning rigid "jobs" to each image in the prompt. You have to explicitly tell the model: "Image 1 is strictly for the subject's identity, Image 2 is only for the concrete background texture, match the lighting of Image 1". From an infrastructure standpoint, adjusting to the two-model split (Flare vs. Sunburst) took some testing. Flare is noticeably faster for everyday generation, while Sunburst is much better at retaining exact details during complex edit sequences. Since they both cost the same token rate, deciding which one to use is purely a latency vs. precision trade-off. To make A/B testing easier without rewriting our client logic, we just shoved an aggregator proxy in front of our pipeline (using CometAPI for staging right now). It lets us swap between gpt-image-2.5-flare and sunburst just by changing the model string in the standard OpenAI client, rather than juggling different provider endpoints when evaluating quality. One last tip if you are building automated generation pipelines: stop trying to fix bad outputs by bumping the API quality parameter to 'max'. The quality tier only affects final refinement and cost; it cannot rescue an underspecified prompt. Get your constraints right on Flare, and only route to Sunburst when your multi-turn edits start losing fidelity. Has anyone else found a better way to enforce pixel-perfect local inpainting without having to composite the API output back into the original master image on the backend? submitted by /u/asdfghmfker [link] [comments]