You see it constantly, and its worse with GPT than Gemini models, but they both do it. They seem to think they need to tell not show. They cant not annotate their thoughts into the image. A lot of the time you see either random signs on posts added, tshirts with text describing the story, or hand written annotation over the image.
There's really no reason this should be a problem anymore on default settings, or that everybody should still be getting the exact same style of edgy poster out of GPT with icons for bullets, and lists that add nothing jammed into the image. They need to have some kind of seed library of templates they start from, and higher temperature on selecting different styles.
"subjects": [
{
"description": "A transparent header spans the top of the teal hero. The white ANNUI wordmark sits at the upper left, followed by Product, For Agents, Resources, and Company links. For Agents is underlined as the active page. Sign in appears near the upper right beside a large white rounded Get started button.",
"position": "top edge, full width, approximately 8% of viewport height",
"subject": "Navigation Header"
},
{
"description": "Left-aligned hero content includes the spaced eyebrow 'FOR AGENTS', a large cream serif headline reading 'Your clients’ brighter tomorrow.', supporting copy about modern financial software, real support, and helping independent agents grow their annuity business, plus a white pill-shaped 'Build with Annui' button and a circular play control labeled 'Watch for Agents (1:30)'.",
"position": "left half, upper-middle through lower-middle, approximately 46% width and 58% viewport height",
"subject": "Hero Copy and Actions"
},
{
"description": "A monumental classical female statue dominates the center-right of the hero. The figure has a pale blue-green face, dark teal hair, a radiant gold helmet and laurel details, and layered gold and blue garments. Architectural columns and cloudy sky fill the background, creating a sculptural financial-services editorial scene.",
"position": "center-right, extending from the top edge to the bottom of the hero, approximately 55% width and 85% viewport height",
"subject": "Classical Statue Hero Artwork"
},
{
"description": "A narrow text panel overlays the far-right architectural columns with vertically spaced uppercase text reading 'INDEPENDENT ADVICE A BRIGHTER TOMORROW', a short horizontal divider, and 'BUILT TOGETHER'.",
"position": "right edge of hero, upper-middle through lower-middle, approximately 15% width and 42% viewport height",
"subject": "Vertical Message Panel"
},
{
"description": "A full-width warm off-white strip beneath the hero contains four evenly spaced benefit columns separated by thin vertical rules. Each column has a pale beige circular icon and dark text: 'Streamline Sales' with quote and compare copy, 'Serve Clients Better' with carrier and education copy, 'Grow Your Practice' with tools and marketing support copy, and 'Stay Independent' with client, advice, technology, and support copy.",
"position": "below the hero, full width, approximately 18% viewport height",
"subject": "Agent Benefits Strip"
}
],
Just for context, I ran design systems at Figma for 5 years / led their first prompt->design stream before quitting to make my own startup in this space.
Image models are trained on visual creativity, color usage, whitespace, etc - all the ingredients you need for good design principles. Where they struggle is often with information density and text rendering, which LLMs excel at. You can use the best parts of both of them though - use an image model for the initial design and an LLM to convert it to code.
One thing that's very telling is with openai's sites feature, in its chain of thought it actually renders out an image first as a design reference, then builds towards that.
You just gotta use the best parts of all of them :)
And benchmarks do not account for it sadly
Also, sometimes you want to do “ugly” things on purpose or add imperfections like film grain and such
This is pretty trivial with an extra sentence added to your prompt, for all the models. Getting them to make normal looking people is trickier.
And honestly, the whole thing looks like an ad-for-an-ad.
yes on gpt-image 2.5 being great at times, at almost 1 order of magnitude more of the cost per image and way slower tho
EDIT: do you find that a lot of people use diffusion models for UI mockups? i’ve always been surprised that people wouldn’t use LLMs exclusively for that instead
Not yet, but it's what I quit my role at Figma to make a bet on for my startup. In my experience diffusion is a far, far better creative designer than LLMs. LLMs are trained for consistent output, which makes sense for a billing function, but leads to some very obvious smell when doing frontend work (take claude design as an example - it's often very obvious what comes out of it). Image models have a bit more creativity baked in, and are trained on color usage, whitespace, and visual balance a lot more. I've found the image->html approach leads to much better results, basically using an image model as an architect.
Would love to be connected with the team for when you all are ready to finetune it. My email is j@diffui.ai
However feels like LLMs such as Opus 5.5 in particular are surprisingly adept at generating UI mockups right off the bat these days. And since they’re already code, they’re much easier to modify and tweak.
We are also seeing it with people using Opus 5.5 to code shaders, motion graphics, and websites more as opposed to using, say, after effects.