The biggest headache in graphic design is getting AI to place text in images. No matter what model I used before, anything with slightly dense text would turn into gibberish. The biggest difference I’ve found with GPT Image 2 is that it has a “thinking” step before generating—it plans the composition, checks it, then iterates, instead of just firing off a single shot.
The result is that stuff that’s always been a mess—dense text, small UI elements, icons, infographics, maps, slides, even comic panels—comes out clean and usable. I tested it with a pretty tricky prompt: a poster with a centered seven-item bullet list in 11pt Helvetica, and the output was actually usable typography, not a pile of blurry letters.
Two other things are really practical for me. First, local editing: pixels outside the edited area stay stable, so you don’t end up with the model redrawing someone’s face when you just wanted to tweak the sky. It goes up to 2K. Second, you can generate eight images from one prompt while keeping characters and objects consistent—no more grinding over locked seeds for character three-views or product variations. Its role is to be the text-heavy workhorse in a mixed pipeline: let it handle the main visuals with heavy text, then pass it back to local models for upscaling and stylization.