Technical Context
I've been closely following the feedback on ChatGPT image gen 2 because this kind of update has immediate practical implications. If a model can reliably handle text and style, it's no longer a toy but a viable layer for AI automation in design.
Here are the facts I've gathered. Since ChatGPT switched from DALL·E 3 to its native GPT Image model, the quality of in-image text has genuinely improved. It's not just "sometimes it gets it right," but rather that short labels, buttons, headlines, and simple UI elements have become noticeably more consistent.
This is more important than it seems. Previously, I would almost automatically exclude image models from tasks that required assembling a screen with multiple controls in a unified visual rhythm. Now, you can get a draft that's respectable enough to share in Figma as a reference, not a meme.
I haven't seen official benchmarks on UI consistency across multiple generations. But based on ChatGPT's current capabilities, the picture is clear: conversational editing, rapid iterations, natural adjustments without masks, and decent handling of text within the image. This is already enough for quick concepts.
The "nanobanana" comparison is, of course, more anecdotal than scientific. But I get the point: if one model can assemble a set of controls in a consistent style while another falls apart on every other element, the first one wins in real-world applications, even without fancy charts.
What This Changes for Business and Automation
The first to benefit are teams that need to test UI hypotheses quickly: landing pages, admin panels, onboarding screens, and ad creatives with an interface vibe. In these cases, speed is more important than pixel-perfect design.
The second point is about AI implementation. If the model can write text directly in the layout more effectively, it becomes faster to build internal pipelines for previewing banners, cards, stories, and simple product screens without needing a designer's input at every step.
Those who try to build a production process based on a single generation will lose out. Final UI still isn't a matter of "generate and hand off to development." But as a layer for AI integration in prototyping, it's already a very powerful tool.
I would use this precisely where speed, variability, and a unified visual tone are needed, rather than a perfect design system from the first attempt. If these tasks are already bogging down your team, we can analyze your workflow together. At Nahornyi AI Lab, we specialize in building AI solution development tailored to real processes, so tools like ChatGPT save hours instead of creating chaos.