GPT Image 2 vs GPT Image 1.5: What the Evidence Says for UI Mockups

Those points matter because they confirm the models and workflows exist. But they do not prove that GPT Image 2 produces better app screenshots, cleaner UI mockups or more realistic desktop interface scenes than GPT Image 1.5.

The evidence gap: UI quality has not been publicly proven

To support a claim like “GPT Image 2 is more natural for UI mockups,” you would need more than a model page. You would need direct, UI-focused comparisons.

So the more accurate conclusion is not “GPT Image 2 has no improvements.” It is: based on the public documentation reviewed here, there is not enough evidence to say GPT Image 2 is reliably better than GPT Image 1.5 for app screenshots, UI mockups or desktop interface scenes.

“More natural” needs a scorecard

For UI imagery, naturalness is not just whether the picture looks attractive at first glance. A glossy mockup can still fail if the text is garbled, the icons are fake, the layout is inconsistent or the browser frame looks wrong.

A useful evaluation should break “natural” into separate criteria:

That distinction matters because the same model may be strong for a polished marketing hero image and weaker for a dense analytics dashboard full of numbers and small UI elements.

A practical A/B test before you switch

OpenAI’s Cookbook includes material on image evaluations for generation and editing use cases, which can help teams think about evaluation design; it is not, however, a GPT Image 2 vs GPT Image 1.5 UI benchmark.

A lightweight product-team test could look like this:

1. Build a fixed prompt pack. Include a mobile dashboard, settings screen, SaaS onboarding modal, analytics web app, desktop browser scene and App Store-style promotional screenshot.
2. Use the same inputs for both models. Keep the prompt, reference image, aspect ratio and constraints identical.
3. Anonymise the outputs. Reviewers should not know which model produced which image.
4. Score with a fixed rubric. Rate layout, text legibility, component consistency, screenshot realism, prompt adherence and visible errors.
5. Separate results by use case. Do not rely on one average score; app screenshots, desktop scenes and marketing mockups may behave differently.
6. Log failure modes. Track issues such as fake icons, garbled labels, drifting button styles, unrealistic device frames or odd browser chrome.

Adoption advice: treat GPT Image 2 as a candidate upgrade

If you are choosing today between GPT Image 1.5 and GPT Image 2 for UI mockups, the conservative call is to treat GPT Image 2 as a candidate upgrade, not a publicly proven replacement.

Upgrade if your own blind test shows GPT Image 2 consistently wins on the criteria that matter to your workflow: cleaner layouts, readable small text, stable components and more convincing screenshot realism. If results are close—or if GPT Image 1.5 is steadier on dense UI details—staying with GPT Image 1.5 for those use cases is still a rational choice.

Bottom line: OpenAI’s docs confirm relevant GPT Image models and image generation/editing workflows, but they do not provide enough public evidence to prove GPT Image 2 is automatically more natural for app screenshots, UI mockups or desktop interface scenes.