GPT Image 2 is designed for text rendering, realistic scenes, structured compositions, and reference-led edits across ads, infographics, interface mockups, and other visual work.
Explore prompt-driven outputs that highlight realistic scenes, text rendering, structured layouts, and image editing workflows powered by GPT Image models.
Teams evaluating GPT Image 2 usually care about four things most: prompt fidelity, cleaner text rendering, more grounded realism, and better handling of complex visual information.
Handle headlines, labels, captions, and UI copy with much cleaner edges and stronger spelling accuracy.
Better semantic understanding helps with maps, interfaces, diagrams, game-like scenes, and context-heavy prompts.
Outputs land closer to believable photography and polished screenshots, with less of the filtered AI look.
Detailed instructions translate into cleaner layout decisions, stronger object relationships, and more usable first drafts.
Useful for infographics, ad creatives, interface mockups, and information-dense visuals where layout matters.
Reference-led edits stay within the same create surface, making it easier to refine scenes instead of starting over.
GPT Image 2 is most useful when the task needs both visual quality and semantic precision, especially in layouts, text-heavy visuals, and reference-led editing.
Generate campaign visuals with readable copy, clearer hierarchy, and fewer throwaway drafts.
Create charts, labels, callouts, and structured information graphics that stay legible and on-prompt.
Produce dashboard-like screens, app concepts, and interface-style compositions with stronger layout logic.
Simulate screenshot-style visuals, overlays, and world-aware scene details with more believable consistency.
Create launch graphics, hero visuals, packaging concepts, and product-led compositions for websites and campaigns.
Upload source images, describe the change, and keep iterating inside the same GPT Image workspace.
The create flow stays simple: choose a model version, write a clear prompt, optionally upload a reference image, and generate or refine the result.
Start with text-to-image for a fresh concept or image-to-image when you want to transform a reference.
Describe the subject, layout, on-image text, visual style, and scene logic you want the model to follow.
Run a first draft, review the composition, then iterate with more precise instructions or a stronger reference image.
The experience stays centered on GPT Image 2, and the create page makes the live generation path explicit before you continue.
The create page, examples, and defaults are all organized around GPT Image 2 so the product experience stays consistent.
Before any generation switches to GPT Image 1.5, the UI shows a confirmation step so you can see exactly what will handle the run.
You keep the same prompt flow, editing surface, and create entry point instead of bouncing between separate tools.
These answers cover product fit, text rendering, editing potential, and the current runtime status.