how good are small local LLMs at generating interfaces instead of text?

with chatgpt recently launching intelligent ui, i've been looking into the different approaches to generative ui and wondering how well smaller local models would actually handle this.

for anyone unfamiliar with the concept, the idea is pretty straightforward. instead of an llm responding with a wall of text or markdown, it generates actual interface components like charts, tables, forms, and interactive elements depending on what the user asks.

there are a few projects working on this, like vercel's json-render, google's a2ui, and openui (which coincidentally just launched its 1.0 version).

they have slightly different approaches to how the interfaces are generated, but the underlying idea is similar.

what i'm particularly curious about is where smaller models start falling apart.

getting an llm to produce valid json or some other structured format is one thing, but generating a useful interface feels like a different problem altogether.

say you ask a model to analyze your monthly expenses. it needs to figure out that a chart would be more useful than a table for showing spending trends, decide which data belongs on which axis, and maybe include a few filters to let you explore the results.

and all of that needs to happen without hallucinating data, picking the wrong components, or generating an interface that technically renders but makes absolutely no sense.

i've been looking into how different projects tackle this. json-render and a2ui rely on structured json-based representations, while openui uses its own DSL called OpenUI Lang, which is specifically designed for generating interfaces with llms.

according to OpenUI's benchmarks, their approach uses around 67% fewer tokens than comparable json-based formats. which makes me wonder whether reducing the amount of structure a model needs to generate would make a meaningful difference for smaller local models, especially on consumer hardware.

another interesting thing i came across is that the OpenUI team released **OUI-1**, a dedicated open-weight model for generative ui based on diffusiongemma, with 4b active parameters.

they report a 71.7% score on their generative ui benchmark, although i'd take any project-maintained benchmark with a grain of salt until there's more independent testing.

but it raises an interesting question: would a smaller model specifically trained to generate interfaces outperform a much larger general-purpose chat model on this particular task?

and if so, would it make more sense to have one model handle the actual reasoning and tool calls, while a smaller dedicated model handles turning the results into an interface?

for example, you could have your main agent analyze a dataset and return structured information, then pass that to a smaller locally running model whose only job is deciding how that information should be presented.

i wonder how much this would reduce latency, and whether the additional model call would even be worth it.

the other question is what would actually be the best way to evaluate this.

most benchmarks i've seen focus on whether the generated output is valid and renders successfully. but a model could generate a perfectly valid chart that represents the data incorrectly, or choose a table when a simple form would have been far more useful.

so how do you meaningfully measure whether a generated interface is actually good?

some links for anyone interested:

* **OpenUI 1.0:** [https://github.com/thesysdev/openui](https://github.com/thesysdev/openui)
* **OUI-1 (4B active parameters):** [https://huggingface.co/thesysdev/OUI-1](https://huggingface.co/thesysdev/OUI-1)
* **Generative UI Bench:** [https://www.openui.com/benchmarks](https://www.openui.com/benchmarks)
* **json-render:** [https://github.com/vercel-labs/json-render](https://github.com/vercel-labs/json-render)
* **A2UI:** [https://github.com/a2ui-project/a2ui](https://github.com/a2ui-project/a2ui)

curious if anyone here has experimented with this using local models.

if you were