GLM-4.7 vs Claude Sonnet for Vibe Coding: Which Generates Better UI?

When I started comparing GLM-4.7, released on December 21, 2025 by Z.AI, against Claude Sonnet 4.5 from Anthropic, I expected another typical "both are good" scenario. Instead, I discovered something fundamentally different about how AI models approach visual design—what the community is now calling "vibe coding."

What I Actually Tested

Using the same prompt—"Design a sleek SaaS landing page hero for a Notion-style productivity app. Make it feel premium, clean, and a bit playful"—I gave both models identical constraints and timing. The results revealed a fascinating divergence that goes beyond raw code quality.

GLM-4.7 delivered what I'd call a "Dribbble in 2025" aesthetic on first try. Claude Sonnet 4.5 produced technically sound code, but the visual output felt dated—like a well-executed Tailwind tutorial from 2019. Not wrong, just... off.

Understanding Vibe Coding: Why It Matters for Frontend Development

Vibe coding emphasizes describing the desired feeling or outcome of a design, letting AI propose visual solutions rather than manually specifying every element. In practical terms for GLM-4.7 vs Claude Sonnet 4.5, this means:

The Real Test Criteria

Instead of just evaluating "correct HTML/CSS," I assessed:

Whitespace usage and breathing room
Typography confidence (does it look professionally chosen?)
2025 relevance (would this feel current in a real app?)
Client-ready factor (would I show this without apologizing?)

I treated both models like junior designer-developers I'd hired for a day, providing:

Minimal styling guidelines
Brand adjectives ("calm, premium, friendly")
Rough component lists

Then I observed how each filled in the gaps.

Head-to-Head Performance Testing

Round 1: Raw UI Vibe (Minimal Constraints)

Testing across three core scenarios—SaaS landing pages, analytics dashboards, and presentation decks—I scored each on a 1-10 "client-ready" scale:

GLM-4.7 Average: 8/10

Consistently used modern spacing with generous padding
Chose reasonable color palettes without explicit hex specifications
Defaulted to layouts matching current SaaS products

Claude Sonnet 4.5 Average: 6/10

Elements clustered too tightly
Occasional overuse of gradients and shadows
Generic section patterns lacking personality

GLM-4.7's "Vibe Coding" capability significantly improves UI quality, producing cleaner, more modern webpages with better layout accuracy.

Why AI-Generated UIs Often Feel Generic

Both models are pattern machines trained on vast web data. When prompts are vague, they lean on the most common patterns they've encountered. This explains why many AI UIs:

Repeat the same three layouts
Use safe, overused spacing
Feel like copies of copies

GLM-4.7 showed stronger internal priors for current design patterns. When I specified "modern dashboard, minimal, enterprise feel," it naturally:

Used split layouts with clear visual hierarchy
Selected non-cheesy accent colors
Respected typography scales better by default

Claude Sonnet matched this quality only after detailed designer-style prompting like:

"Increase vertical spacing by ~20% in hero section"
"Reduce gradients, use solid colors with subtle opacity"
"Use 2 typographic weights only: regular and semibold"

Model Philosophy and Approach

GLM-4.7: The Bold Visual Designer

GLM-4.7 is Z.AI's flagship model with enhanced programming capabilities and stable multi-step reasoning, featuring a 200K context window. In my testing, it demonstrates:

Design-Forward Characteristics:

Aggressive default inference (colors, spacing, font scales)
Cleaner class structures (especially with Tailwind)
Takes "modern" and "premium" literally

Performance Metrics:

Completed a 700-line Tailwind landing page in ~11 seconds
Required 25-30% fewer manual edits to reach "client-ready" state
Achieved 73.8% on SWE-bench Verified (+5.8% over GLM-4.6)

Claude Sonnet 4.5: The Thoughtful Collaborator

Claude Sonnet 4.5 is the best coding model in the world for building complex agents, showing substantial gains in reasoning and math. It excels at:

Structured Approach:

Reasoning about component responsibilities
Explaining layout decisions
Maintaining consistency across multi-turn sessions

Where It Shines:

Teams with existing design systems
Staying within brand rails
Long-term code maintainability

Claude defaults to safe design choices like Inter fonts and purple gradients without direction, though it's highly steerable with proper prompting.

Real-World Testing: Side-by-Side Comparisons

Test 1: Landing Page Generation

Prompt: "Generate a React + Tailwind landing page for B2B AI analytics. Include hero, social proof, features, pricing, and FAQ. Modern, premium, trust-focused. Avoid cheesy gradients."

GLM-4.7 Results:

Solid left-text, right-graphic hero layout
Well-stacked pricing cards with clear "Most popular" highlight
Authentic-feeling social proof (logo strip + credibility text)
Verbose but logically grouped Tailwind classes
Time to MVP: ~15 minutes of tweaks

Claude Sonnet 4.5 Results:

Structurally sound but template-like
More gradients than requested (2 follow-ups needed)
Pricing section lacked clear plan emphasis
Time to MVP: ~25-30 minutes of tweaks

Test 2: React Dashboard Component

Prompt: "Create React dashboard: left sidebar nav, top header, main analytics with 3 cards and chart. Minimal, enterprise. Use CSS modules."

Here the results flipped slightly:

GLM-4.7:

Strong visual hierarchy out of the box
Excellent card spacing
CSS modules felt utility-ish (like Tailwind in spirit)

Claude Sonnet 4.5:

More conservative but very clean component separation
Semantic CSS modules: .sidebar, .header, .summaryGrid
Easier long-term team maintenance

Verdict: Solo builders prefer GLM-4.7's immediate polish; teams value Sonnet's maintainability.

Test 3: Presentation Slide Deck

Prompt: "Generate HTML/CSS for 10-slide marketing deck: title, problem, solution, features, testimonials, pricing, CTA. Minimal, 16:9, big typography."

This test most clearly showed the vibe difference:

GLM-4.7: Layouts resembling modern Keynote templates—big type, excellent whitespace, obvious visual rhythm (Score: 7.5/10)
Claude Sonnet 4.5: Closer to decent Google Slides templates, usable but I'd still open Figma after (Score: 5.5/10)

Beyond Aesthetics: Code Quality Analysis

Responsive Design Handling

Stress-testing with "Make this work on 375px mobile and 1440px desktop without horizontal scroll":

GLM-4.7: Better mobile-first behavior, naturally using responsive Tailwind classes (md:, lg:) correctly 80-85% of the time
Claude Sonnet 4.5: More cautious, sometimes under-used breakpoints, required explicit follow-ups

Once corrected, Sonnet maintained patterns very reliably across subsequent prompts—crucial for longer workflows.

Accessibility (a11y)

Testing proper heading levels, ARIA labels, and color contrast:

Claude Sonnet 4.5 was more verbose about a11y decisions, often adding ARIA roles proactively
GLM-4.7: Complied when prompted but volunteered fewer details

If accessibility is non-negotiable, Claude Sonnet 4.5 has a slight edge as a "does the right thing by default" partner.

Component Architecture

Requesting "clean, reusable React components":

GLM-4.7: Good at creating presentational components with props, sometimes over-abstracted initially
Claude Sonnet 4.5: Strong at naming and layering components, especially with design system mentions

For long-term frontends, Sonnet's codebase felt more maintainable. For "need a strong starting point today," GLM-4.7 won on time-to-nice-output.

Multi-Turn Refinement and Context Management

Handling "Make It More Modern"

This deliberately vague instruction revealed different interpretations:

GLM-4.7 (70% success rate):

Increased spacing slightly
Smoothed borders and radii
Refined button states (ghost/outline variants)
Adjusted toward neutral grays + one accent

Claude Sonnet 4.5:

Introduced gradients or shadows
Adjusted typography weights
Required more specific clarification

Once I clarified ("By modern I mean flatter, less decoration, more white space"), Sonnet followed that definition almost perfectly in subsequent iterations.

Long-Session Memory (6-8 Turn Projects)

GLM-4.7: Great short-term context within single sessions, occasional regression after major structural changes
Claude Sonnet 4.5: Slightly better at maintaining long conversational trails and design principles

Claude Sonnet 4.5 is designed for strong reasoning stability and predictable execution in multi-file logic and backend systems.

Cost Efficiency for UI-Heavy Workflows

Testing typical indie-creator workloads (3-5 landing pages, 1 dashboard, 1 deck) averaged 25-35k tokens per project:

GLM-4.7: Produced slightly shorter, more direct code outputs (~10-15% lower token usage)
Claude Sonnet 4.5: Pricing starts at $3 per million input tokens and $15 per million output tokens, with verbose but helpful explanations

For UI-heavy workflows on tight budgets, that 10-15% efficiency can be the difference between unlimited iteration and stopping prematurely.

When to Use Each Model

Choose GLM-4.7 When You Need:

✅ Fast, high-vibe starting points for landing pages, dashboards, slide layouts

✅ Modern aesthetics right now over cleanest component architecture

✅ Solo/indie creator workflows shipping without looking like default templates

✅ Better cost efficiency on high-volume UI generation

Best for: SaaS landing pages, simple dashboards, presentation-style UIs

GLM-4.7 ranks first among open-source models on Code Arena with millions of global users in blind tests.

Choose Claude Sonnet 4.5 When You Need:

✅ Existing design systems with brand guidelines to respect

✅ Deep explainability and a11y by default

✅ Complex multi-page apps where component boundaries matter

✅ Thoughtful junior engineer with good long-term habits

Best for: Production applications, team environments, complex refactoring

Claude Sonnet 4.5 is state-of-the-art on SWE-bench Verified and leads OSWorld at 61.4% on computer use tasks.

The Hybrid Approach: Best of Both Worlds

For solo builders, my honest suggestion after extensive testing:

Use GLM-4.7 to rough in layout, hero, sections, and general vibe
Hand to Claude Sonnet 4.5 to refactor components, improve accessibility, clean structure

This combo has gotten me closest to "barely touched Figma this week and still shipped UIs I'm proud of."

This hybrid workflow is also how we build things at Macaron. We generate mini-apps every day — from calorie trackers to travel planners — and one thing became obvious very quickly: a working app that looks off still feels broken to users.

That’s why we obsess over the design layer just as much as the model layer, treating “vibe” as a first-class constraint, not a nice-to-have.

If you’re curious what that looks like in real shipped mini-apps, Macaron is a good place to peek.

If choosing only one: For indie creators and marketers working heavily in browsers, GLM-4.7 vs Claude Sonnet 4.5 tips toward GLM-4.7 for frontends—unless your main pain is long-term maintainability, where Sonnet still earns its spot.

The Future of Vibe Coding

Vibe design raises the bar for design quality and frees designers to tackle more complex experiential challenges, making every product team member capable of incorporating UX thinking.

The emergence of vibe coding represents a fundamental shift in how we approach UI development. Rather than starting with blank code editors, designers can now describe what they want and get functional websites through AI-powered tools.

Final Recommendation

Don't just read benchmarks and specs. Throw your weirdest landing page brief at both models, watch which one feels closer to your taste, and build your own vibe coding stack from there.

The revolution isn't about which model is "better"—it's about matching the right tool to your specific workflow, timeline, and aesthetic standards.

Key Takeaways

Vibe coding prioritizes design feel over technical specs
GLM-4.7 excels at modern aesthetics and speed-to-market
Claude Sonnet 4.5 wins on maintainability and structure
The hybrid approach leverages both models' strengths
Cost efficiency matters for high-volume UI work

Authoritative Resources

Article based on hands-on testing conducted December 2025. Model capabilities and pricing subject to change. Always verify current specifications before implementation.