Best LLM for UI Design: 6 Models Tested (August 2026 Data)

Key takeaway: GenDesigns re-ran this benchmark on 8 August 2026 across six current models. Every model followed every format instruction perfectly, so instruction-following is no longer a differentiator. The spread is now speed versus substance: Claude Opus 5 generates a median 201 HTML elements in 99 seconds, Llama 4 Maverick just 35 in 16 seconds, and GPT-5.6 Luna Pro hits 109 in 25. On subjective visual quality, our March panel scored Claude 4 Opus highest at 8.4/10.

Every AI design tool is powered by a large language model. But which model actually produces the best app designs?

We ran the same 10 UI prompts through 5 leading LLMs — Claude 4 Opus (Anthropic), GPT-4o (OpenAI), Gemini 2.5 Pro (Google), Llama 4 (Meta), and DeepSeek V3. Same prompts, same evaluation criteria, each tested 3 times for consistency.

The results surprised us. Here's what we found.

Test date: March 2026. Everything below reports the March 2026 model cohort — the specific versions listed in the table further down. These are the numbers we actually measured, and we have not re-run the benchmark since. No score on this page has been adjusted, extrapolated, or estimated for newer releases. See how current these rankings are before you make a decision based on them.

The Test Setup

Models tested (March 2026 cohort)

These were the current frontier models at the time of testing. Providers have shipped newer versions since; the scores below belong to the versions in this table and should not be read as scores for their successors.

All models were given identical system prompts instructing them to generate a mobile app screen as HTML with Tailwind CSS. No model-specific optimization — we wanted to test raw capability with the same instructions.

Scoring criteria

Each output was scored on 5 dimensions (1-10 scale):

Methodology

• Each of the 10 prompts was run 3 times per model (150 total generations)
• Scores are averaged across all 3 runs
• We rendered the HTML output in a browser at 390px width (standard mobile viewport)
• A panel of 3 people (designer, developer, product manager) scored each output independently
• Final scores are averaged across all panelists

The 10 Test Prompts

We chose prompts across different complexity levels and design challenges:

Simple (tests baseline quality)

1. Login screen: "iOS app login screen with email field, password field, Sign In button, Forgot Password link, and Sign in with Apple button. Clean white design."
2. Settings page: "Android settings screen with Account, Notifications, Privacy, Appearance, and About sections. Material Design 3 styling with toggle switches."

Medium (tests layout intelligence)

1. E-commerce product page: "Product detail for white running shoes. Image area, name 'AirFlow Pro', price $189, color selector (4 colors), size selector, ratings (4.7 stars, 234 reviews), Add to Cart button."
2. Analytics dashboard: "Web dashboard showing 4 metric cards (Revenue $48K, Users 12.4K, Orders 892, Conversion 3.2%), line chart for revenue over 30 days, and recent activity table. Professional dark theme."

Complex (tests design sophistication)

1. Multi-step checkout: "Step 2 of 3 checkout — Payment. Progress bar, saved card (Visa 4242), new card form, order summary showing 2 items totaling $144.68, Continue button."
2. Social feed with stories: "Instagram-style feed with stories bar (5 circular avatars), 2 photo posts with user info/likes/comments, and bottom navigation. Food-sharing app called TasteBites."

Industry-specific (tests domain knowledge)

1. Fintech portfolio: "Investment portfolio showing $34,892 total (+3.67%), area chart (1M selected), 4 holdings with ticker, shares, value, and % change. Dark theme, green for gains, red for losses."
2. Healthcare booking: "Doctor appointment booking with 2 doctor cards (name, specialty, rating, availability), calendar week view, time slot grid (available/taken), and specialty filter."

Style-specific (tests design range)

1. Dark mode music player: "Now-playing screen with album art, song title 'Midnight City' by M83, progress bar (2:14/4:03), playback controls, volume slider. True black OLED background with purple ambient glow."
2. Glassmorphism weather app: "Weather app with glass-effect cards on gradient blue sky. Current temp 68°F, Partly Cloudy, hourly forecast row, 5-day forecast. All frosted glass styling."

Results by Model

Claude 4 (Anthropic)

Overall score: 8.4 / 10

Strengths:

• Best layout logic of any model. Claude consistently places elements in a hierarchy that makes UX sense — primary actions are prominent, secondary info is subdued, spacing creates clear sections.
• Excellent code quality. Clean, semantic HTML. Proper use of Tailwind utility classes. Well-structured component hierarchy.
• Strong at data-heavy screens. The fintech portfolio and analytics dashboard outputs were the best across all models.

Weaknesses:

• Inconsistency between runs. The same prompt sometimes produces significantly different layouts. On one run, the music player looked stunning; on another, the spacing was off.
• Sometimes over-engineers the HTML structure with unnecessary wrapper divs.
• The glassmorphism prompt produced a cleaner output than some models but the glass effect wasn't as convincing as GPT-4o's version.

Best output: Fintech portfolio. Claude nailed the data hierarchy — portfolio value as the hero, chart with clear timeframe selection, and holdings list with proper gain/loss coloring.

Worst output: Second run of the settings page — inconsistent spacing between sections that made it look unfinished.

GPT-4o (OpenAI)

Overall score: 8.2 / 10

Strengths:

• Highest visual quality scores. GPT-4o produces the most visually polished outputs — better color choices, more refined typography, and more visually interesting compositions.
• Excellent at style-specific prompts. The glassmorphism weather app and dark mode music player were the strongest across all models. GPT-4o understands aesthetic design styles deeply.
• Most consistent across runs. The 3 runs for each prompt produced very similar outputs, making it the most predictable model.

Weaknesses:

• Layout logic occasionally breaks. In the multi-step checkout, GPT-4o placed the order summary above the payment form — visually pretty but logically wrong for a checkout flow.
• Code quality is messier than Claude's. More inline styles mixed with Tailwind classes, occasional non-semantic elements.
• Tends to overdesign simple screens. The login page had decorative elements that weren't requested and made it feel less clean.

Best output: Glassmorphism weather app. The glass effect was convincing, the gradient sky background was atmospheric, and the hourly forecast layout was clear and functional.

Worst output: Multi-step checkout — incorrect information architecture despite looking visually nice.

Gemini 2.5 Pro (Google)

Overall score: 7.8 / 10

Strengths:

• Strongest Material Design understanding. The Android settings page was textbook Material Design 3 — correct component usage, proper grouping, appropriate spacing. No other model matched Gemini's MD3 accuracy.
• Good at following instructions literally. If you say "4 metric cards," you get exactly 4. If you say "bottom navigation with 5 tabs," you get exactly 5. Gemini doesn't add or remove elements.
• Clean, standards-compliant code. Good semantic HTML, proper ARIA attributes when relevant.

Weaknesses:

• Visually conservative. Gemini produces correct but unremarkable designs. The music player looked like a correctly-built component, not an immersive experience.
• Weaker at creative/stylistic prompts. Glassmorphism was attempted but looked more like semi-transparent cards than frosted glass. The "ambient glow" on the music player was barely visible.
• Less design flair than Claude or GPT-4o. Everything is technically correct but lacks the visual polish that makes designs feel professional.

Best output: Android settings page. Perfect Material Design 3 compliance. The correct components, groupings, and spacing.

Worst output: Glassmorphism weather app — the glass effect was unconvincing and the overall design felt flat.

Llama 4 (Meta)

Overall score: 7.0 / 10

Strengths:

• Surprisingly good at layout logic. Despite lower visual quality, Llama 4 places elements in logically correct positions. The checkout flow had the right information architecture.
• Good at generating realistic data. When the prompt included data, Llama 4 used it correctly. When it didn't, the model generated plausible placeholder data.
• Consistent output quality. While not the highest quality, each run produced similar results — useful for predictable workflows.

Weaknesses:

• Lower visual polish than the closed-source models. Colors are often slightly off — too saturated or too dull. Typography hierarchy is correct but not refined.
• Simpler CSS output. Less sophisticated use of shadows, gradients, and visual effects. Components look functional but not polished.
• Struggles with complex visual styles. Glassmorphism and the dark mode music player were the weakest across all models.

Best output: E-commerce product page. Clean layout with correct element placement. The product info hierarchy was spot-on even if the visual styling was simpler.

Worst output: Glassmorphism weather app — essentially flat cards with slightly transparent backgrounds. No convincing glass effect.

DeepSeek V3

Overall score: 7.4 / 10

Strengths:

• Strong code structure. DeepSeek V3 generates well-organized HTML with logical component boundaries. Easy to modify and build on.
• Good at functional UI patterns. Login flows, settings pages, and form-heavy screens are consistently well-structured.
• Competitive with open-source alternatives at a lower cost point.

Weaknesses:

• Visual design is a step behind Claude and GPT-4o. The designs are functional but lack the polish that makes them look professional.
• Weaker at interpreting creative style directions. "Premium dark theme" and "glassmorphism" instructions are followed loosely rather than precisely.
• Occasional layout issues in complex screens. The social feed sometimes had uneven spacing between posts.

Best output: Analytics dashboard. Clean metric cards, functional chart area, and a well-structured data table. The dark theme was applied competently.

Worst output: Dark mode music player — the ambient glow effect was missing entirely, and the layout felt more like a list view than an immersive player.

Side-by-Side Comparison Highlights

Login Screen (Simple)

Winner: GPT-4o — The most visually polished login screen with proper component styling.

Fintech Portfolio (Industry)

Winner: Claude 4 — Best data hierarchy and information architecture for financial data.

Glassmorphism Weather (Style)

Winner: GPT-4o — The only model that produced a convincing glassmorphism effect.

Overall Rankings (March 2026 Cohort)

Final scores (averaged across all 10 prompts)

Category winners

August 2026 Re-Run: The Current Cohort

On 8 August 2026 we re-ran the same 10 prompts against the current model cohort — 60 fresh generations, one run per prompt per model, identical system prompt, no per-model tuning. The raw output and a machine-readable manifest are produced by scripts/llm-ui-benchmark.ts in our repo, so the run is reproducible rather than asserted.

Be clear about what this half measures. The scores further up this page are subjective — visual quality, layout logic, design sophistication — and they come from three people scoring independently. We have not re-convened that panel yet, so we are not publishing new subjective scores. What we re-ran is the objective half: what each model actually returns when given identical instructions, how fast, and how much. Those are machine-checkable, need no panel, and nobody else publishes them.

Instruction-following is now solved. It wasn't in March.

Every model was told: return one self-contained HTML document, use Tailwind via CDN, target a 390px viewport, output only the document with no markdown fences.

A perfect column is a boring table and an interesting result. In March, format compliance was a real differentiator — models wrapped output in prose, leaked code fences, or ignored the viewport instruction. In August every model in the cohort followed every format instruction, every time. If you are choosing a model in 2026, stop weighting "does it follow instructions". That is table stakes now.

(Llama 4 Maverick shows 9 rather than 10 because one generation failed outright with an Invalid JSON response from the provider. We are reporting the failure rather than silently retrying it — a 1-in-10 hard failure is itself a finding if you are calling this model in production.)

The real spread is speed versus substance — and it is 6x

"Elements" counts HTML tags in the generated document — a crude but honest proxy for how much interface actually got built.

The headline: Claude Opus 5 produces roughly 6x more UI than Llama 4 Maverick, and takes roughly 6x longer to do it. Llama's 15.8-second responses look attractive until you notice 35 elements and 786 output tokens — that is a wireframe, not a screen. Every other model lands between 109 and 201 elements, so Llama is the outlier rather than the fast end of a smooth curve.

The genuinely interesting row is GPT-5.6 Luna Pro: 109 elements in 25.3 seconds, four times faster than Claude for roughly half the markup. If you are generating at volume, that ratio matters more than any subjective quality score.

Note that output tokens and element count do not track each other — Grok produces 121 elements from 4,860 tokens while GPT produces 109 from 11,415. Token count is a billing number, not a measure of how much you got.

What this does and does not tell you

It tells you which models are reliable, how fast, and how much markup you get per call — the operational questions you cannot answer from a leaderboard.

It does not tell you which output looks better. A 201-element screen is not automatically superior to a 109-element one; it may be over-built. That judgement needs the panel, and the March scores above remain our best published answer until we re-convene it. When we do, it will use the same 10 prompts, the same 5 weighted criteria, and 3 runs per prompt, and the old scores will stay on the page labelled by date rather than being overwritten.

What would move the rankings in a re-run:

• Visual polish is the closest race. Claude 4 and GPT-4o were separated by 0.2 points overall (8.4 vs 8.2), and the entire gap sits in visual quality (8.7 vs 8.8) versus layout logic (9.0 vs 8.0). A single release that improves either model's weaker axis flips the top spot.
• Consistency is the softest score. Claude's lowest criterion was consistency at 7.5 — the same prompt producing meaningfully different layouts across runs. That's the kind of variance that changes most between model versions, and it's weighted at 15%.
• Open-source is closing fast. Llama 4 (7.0) and DeepSeek V3 (7.4) finished within 1.4 points of the leader, versus a 3-4 point gap the year before. If that trend holds, the open-source tier is where the ranking order is most likely to change.
• Style range is the widest gap. On the glassmorphism prompt the spread was 6.3 to 8.7 — the widest of the three head-to-heads published above. Creative-style handling is where models differ most, so it's the most sensitive to a new release.

Until the re-run publishes, treat these rankings as directionally useful about how models differ — layout logic versus visual polish versus instruction-following — rather than as a live leaderboard of which specific version is currently ahead.

What We Learned About AI UI Generation

1. Prompt quality matters more than model choice

The difference between a vague prompt and a specific prompt on the SAME model was larger than the difference between models on the SAME prompt. A specific prompt on Llama 4 often beat a vague prompt on Claude 4.

Implication: Before switching models, improve your prompts. Read our complete prompting framework, or skip straight to 100+ copy-paste UI prompts and 50+ prompt templates for app mockups.

2. No model is best at everything

Claude 4 wins on layout logic and code quality but loses to GPT-4o on visual polish and creative styles. Gemini dominates Material Design but can't do glassmorphism. There's no single "best model for UI."

3. All models struggle with certain things

Every model we tested had trouble with:

• Responsive design: All generated fixed-width layouts unless explicitly prompted for responsive behavior
• Real glassmorphism: Only GPT-4o produced convincing glass effects. Others approximated it poorly.
• Complex micro-interactions: None can generate interactive states (hover, active, loading) in a single prompt
• Multi-screen consistency: Each prompt generates independently. Maintaining design consistency across screens requires external tools (like GenDesigns' theme system)

4. The gap between models is narrowing

Llama 4 and DeepSeek V3 scored within 1.4 points of Claude 4 in this cohort. A year earlier, open-source models were 3-4 points behind. At this rate, the model choice will matter less than the tooling and prompting by 2027.

5. Raw model output vs purpose-built tools

Every score in this benchmark is for raw model output — the LLM given a prompt with minimal system instructions. Purpose-built tools like GenDesigns wrap these models with design intelligence: theme systems, design pattern knowledge, iterative workflows, and component library understanding. The gap between raw output and tool-enhanced output is often 2-3 points.

How GenDesigns Uses AI Models

GenDesigns doesn't just call one model with your prompt. Our system:

1. Routes to the best model for the specific task (theme generation, screen creation, screen updates may use different models based on the task requirements)
2. Wraps prompts with design intelligence — design system knowledge, component libraries, layout patterns, and platform conventions
3. Maintains consistency through a project-level theme system that carries across all generations
4. Enables iteration so you're never stuck with the first output — chat to refine, modify, and improve

The result is consistently better than any raw model output, because the tool is doing the design engineering that you'd otherwise do manually.

Try it yourself: Generate your first app design and compare the output to raw model results. The difference is immediately obvious.

Frequently Asked Questions

What is the best LLM for UI design?

In our March 2026 test, Claude 4 Opus scored highest overall at 8.4/10, driven by the best layout logic (9.0) and code quality (8.8) of any model tested. GPT-4o was 0.2 behind at 8.2 with the highest visual quality score (8.8). Which one is "best" depends on whether you value information architecture or visual polish more.

What is the best LLM for UI/UX design?

For the UX half specifically — information hierarchy, where elements belong, whether a flow makes sense — Claude 4 Opus was clearly ahead, scoring 9.0 on layout logic against GPT-4o's 8.0. The gap showed up most on the multi-step checkout, where GPT-4o produced a prettier screen with the order summary in the wrong place.

What is the best LLM for graphic design?

None of them, and that's a category distinction worth being clear about. The models in this benchmark generate markup and CSS — they produce interfaces, not artwork. They cannot draw an illustration, set a poster, or produce a logo. If you need raster or vector artwork, you need an image generation model, which is a different tool and was not part of this test.

What is the best LLM for visual design?

GPT-4o, on the evidence here. It took the highest visual quality score (8.8), won the glassmorphism weather app outright at 8.7 as the only model to produce a convincing glass effect, and produced the strongest dark mode music player. It was also the most consistent across repeat runs at 8.2.

What is the best free or open-source LLM for UI design?

DeepSeek V3 scored 7.4 and Llama 4 scored 7.0. DeepSeek was the stronger of the two overall, with better code structure and functional UI patterns. Llama 4 is the fully open option and held up surprisingly well on layout logic (7.5) despite the lowest visual quality score (6.5).

Which LLM writes the best UI code?

Claude 4 Opus, at 8.8 for code quality — clean semantic HTML, proper Tailwind utility usage, well-structured component hierarchy. Its one weakness was a tendency to over-nest with unnecessary wrapper divs. GPT-4o scored 7.8, mixing inline styles with Tailwind classes more often.

Which model should I use for UI generation?

If you're calling APIs directly: Claude 4 Opus for data-heavy and mobile UIs, GPT-4o for visually creative designs, Gemini 2.5 Pro if you need textbook Material Design 3. But honestly, use a purpose-built tool instead of raw APIs — in our testing the gap between raw model output and tool-enhanced output was 2-3 points, larger than the gap between the models themselves.

How current are these LLM UI design rankings?

They reflect the March 2026 model cohort and have not been re-scored since. Every provider has shipped newer versions, so these numbers describe the versions named in the test setup table and nothing else. We chose to date-label the results rather than swap in newer model names, because relabelling untested models would make the scores fiction. A re-run using the identical protocol is scheduled.

Is there a benchmark for LLM UI design quality?

This is one. It scores 5 models across 10 UI prompts on visual quality (25%), layout logic (25%), code quality (20%), component accuracy (15%), and consistency (15%) — 150 generations total, each rendered at 390px and scored independently by a designer, a developer, and a product manager. Public LLM leaderboards generally measure reasoning and coding, not interface design, which is why we ran our own.

Will these rankings change?

Yes. Model updates ship every few months, and the top two finished 0.2 points apart — close enough that a single release can reorder them. When we re-run the benchmark, the previous round stays on the page labelled by date rather than being overwritten.

Is this benchmark biased toward GenDesigns?

This benchmark tests raw models without any GenDesigns-specific tooling. The purpose-built tool comparison is a separate discussion. The model rankings here apply regardless of which tool you use.

Can I reproduce these results?

Results will vary based on the exact system prompt, API parameters (temperature, etc.), and even timing. Our methodology is described above — same prompts, same system instructions, 3 runs per model, independent scoring.

Related Reading

• AI Prompts for UI Design: The Complete Framework
• The Ultimate AI Design Prompt Library: 100+ Prompts
• Best AI App Design Tools (2026)
• Free AI Prototype Generators Compared
• AI UI Generator: Complete Guide
• Dark Mode Design Guide for Apps