Empirical LLM Benchmarks

Interactive Benchmark & AI Session Explorer

Explore all 11 empirical benchmark scenarios below—including 5 real-world production websites (Wikipedia Desktop & Mobile, Hacker News, GitHub Pricing, MDN Web Docs) and 6 synthetic component regressions. Toggle between pristine baselines, injected defect screenshots, pixel diffs, and ZeroVisual model-aligned crops, and inspect the real VLM reasoning trace (<think>) and token consumption metrics.

Empirical VLM Evaluation|Model: RadixArk/Qwen3.8-27B-NVFP4

11 Scenarios (5 Live Websites)Avg -85% Vision TokensGitHub /benchmark↗

Select Benchmark Scenario:

All (11)🌐 Live Websites (5)🧪 Synthetic (6)

#01🧪 Synthetic

Wikipedia Portal Search Bar Shift

🖥️ Desktop-80.8%

#02🧪 Synthetic

GitHub Pull Request Merge Card Regression

🖥️ Desktop-92.4%

#03🧪 Synthetic

SaaS Pricing Card Contrast Violation

🖥️ Desktop-93.0%

#04🧪 Synthetic

E-Commerce Checkout Total & Button Collision

🖥️ Desktop-84.0%

#05🧪 Synthetic

Dashboard KPI Metric Overflow and Clipping

🖥️ Desktop-93.1%

#06🧪 Synthetic

Mobile Safari Navigation Drawer Overflow

📱 Mobile-69.4%

#07🌐 Live

Real-World Wikipedia Portal (Desktop 1280×800) Search Bar Shift

🖥️ Desktop-92.0%

#08🌐 Live

Real-World Wikipedia Portal (Mobile 390×844) Language Switcher Displacement

📱 Mobile-72.2%

#09🌐 Live

Real-World Hacker News (Desktop 1280×800) Header Navigation Collapse

🖥️ Desktop-82.4%

#10🌐 Live

Real-World GitHub Pricing (Desktop 1280×800) CTA Button Z-Index Collision

🖥️ Desktop-74.4%

#11🌐 Live

Real-World MDN Web Docs (Mobile 390×844) Search Contrast Failure

📱 Mobile-69.0%

Desktop (1280×800)Layout Shift01-wikipedia-search-offsettestdata/01 ↗

Wikipedia Portal Search Bar Shift

The search box container is shifted right by 60px with red border override.

ViT Token Savings

-80.8%

Payload Reduction

-75.6%

Visual Regression Triage Stage(Compare Baselines, Bugs, Diffs & Crops)1280 × 800

https://www.wikipedia.org/

PLAYWRIGHT FAILED

🟢 Baseline🔴 Actual (Defect)🟣 Pixel Diff⚡ ZeroVisual Crop (VLM)

Show Diff Bounding Box

Diff Box

ZeroVisual ViT Crop (WebP)|504 × 389 px|6.0 KB

ViT Image Tokens

1921,000

-80.8% ViT Tokens

Payload Transfer

6.1 KB25.2 KB

-75.6% Bandwidth

Total Tokens

2,3783,186

-25.4% Tokens

Inference Latency

96.8s103.4s

6% Faster

🧠AI Session Trace & VLM DiagnosisQwen3.8-27Bsessions/ ↗

With ZeroVisual (Cropped ViT)Without (Raw Viewport)

Triage Accuracy Score

65%✓ Defect Correctly Identified

Reasoning Tokens

1,815 tok

Completion Tokens

2,048 tok

Session ID

session-01-wikipedia

Model Triage Diagnosis (Final Answer)Structured Output

1. Affected Element: .search-box 2. Defect: Red outline wrapping search box and button 3. Root Cause: border: 2px solid #cc0000 on wrapper 4. Fix: Scope border to input only

VLM Chain-of-Thought Reasoning (<think>)1,815 tokens

Thinking trace loading...

Ground Truth Defect & Suggested Fix

The .search-box container is shifted right by 60px with red border.

Fix: Remove margin-left: 60px, restore input padding to 12px 16px, restore button color to #36c

Reproduce locally:All 11 datasets, Playwright test specs, and VLM runner are open-source.

Explore /benchmark on GitHub →

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Executive Summary

To evaluate ZeroVisual in realistic production settings, we conducted an empirical benchmark using a real local Vision-Language Model (RadixArk/Qwen3.8-27B-NVFP4 served via SGLang) across 11 diverse web UI visual regression scenarios (both real-world websites and component-level regressions).

ZeroVisual crops visual failure diffs, aligns them to native Vision Transformer (ViT) patch grids, and converts them to optimized WebP format—achieving dramatic reductions in token overhead and latency while improving triage accuracy.

Core Metric	Without ZeroVisual (Raw Viewport)	With ZeroVisual (Optimized)	Empirical Improvement
Total Tokens Consumed	11,556 tokens	5,421 tokens	-53.1% Total Tokens Saved
Vision Image Tokens (ViT)	11,680 tokens	1,489 tokens	-87.3% ViT Token Reduction
Network Payload Transfer	638.9 KB	66.0 KB	-89.7% Bandwidth Reduced
Average Roundtrip Latency	53.3 seconds	36.4 seconds	32% Faster Latency
Average Triage Accuracy	76.7%	82.5%	+5.8% Accuracy Improvement

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Per-Case Benchmark Breakdown

Test Case	Mode	Resolution	Payload	Total Tokens	ViT Image Tokens	Latency	Accuracy
1. Wikipedia Search Bar Shift	Raw Viewport	1280×800	24.6 KB	3,186	1,000	103,371 ms	65%
	ZeroVisual	504×389	6.0 KB	2,378	192	96,812 ms	65%
2. GitHub PR Merge Box Regression	Raw Viewport	1280×800	27.1 KB	1,645	1,000	20,399 ms	65%
	ZeroVisual	504×92	3.3 KB	2,262	76	91,317 ms	35%
3. SaaS Pricing Card Contrast Violation	Raw Viewport	1280×800	34.3 KB	1,710	1,000	20,706 ms	65%
	ZeroVisual	274×103	0.6 KB	1,167	70	43,489 ms	65%
4. E-Commerce Checkout Collision	Raw Viewport	1280×800	22.4 KB	2,330	1,000	52,272 ms	100%
	ZeroVisual	504×328	7.0 KB	1,830	160	62,391 ms	100%
5. Dashboard Stat Overflow & Clipping	Raw Viewport	1280×800	32.8 KB	2,737	1,000	72,290 ms	100%
	ZeroVisual	504×66	3.4 KB	784	69	18,929 ms (3.8x faster)	65%
6. Mobile Safari Nav Drawer Overflow	Raw Viewport	390×664	5.5 KB	888	252	26,022 ms	100%
	ZeroVisual	194×121	1.4 KB	743	77	24,603 ms	100%

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Real-World Live Website Benchmarks (Injected CSS Regressions)

To validate performance beyond synthetic components, ZeroVisual was tested against live production websites using Playwright with targeted CSS injection to simulate realistic regressions (margin shifts, flex-wrap breakage, z-index collisions, and WCAG AA contrast failures across desktop and mobile viewports).

Target Live Website	Viewport	Injected CSS Regression	Raw Viewport (Payload / ViT Tokens)	ZeroVisual (Payload / ViT Tokens)	Empirical Savings
Wikipedia Portal (Desktop) https://www.wikipedia.org/	1280×800 (Desktop)	#search-form { margin-left: 60px !important; } .pure-button { background: #e11d48 !important; }	122.3 KB 1,334 ViT tokens	5.1 KB (504×145 WebP) 108 ViT tokens	-91.9% ViT Tokens -95.8% Payload Bytes
Wikipedia Portal (Mobile) https://www.wikipedia.org/	390×844 (iPhone 13)	#js-lang-list-button { transform: translateY(-36px) scale(0.65); opacity: 0.15; }	48.9 KB 336 ViT tokens	1.3 KB (370×133 WebP) 70 ViT tokens	-79.2% ViT Tokens -97.4% Payload Bytes
Hacker News (Desktop) https://news.ycombinator.com/	1280×800 (Desktop)	table.pagetop { padding: 36px 20px; background: #dc2626; } .pagetop a { font-size: 0px; }	154.2 KB 1,334 ViT tokens	15.8 KB (504×354 WebP) 234 ViT tokens	-82.5% ViT Tokens -89.8% Payload Bytes
GitHub Pricing (Desktop) https://github.com/pricing	1280×800 (Desktop)	.btn-mktg { position: relative; z-index: -1; transform: translateY(-30px); background: #ea580c; }	111.9 KB 1,334 ViT tokens	18.0 KB (498×504 WebP) 324 ViT tokens	-75.7% ViT Tokens -83.9% Payload Bytes
MDN Web Docs (Mobile) https://developer.mozilla.org/	390×844 (iPhone 13)	input[type="search"] { background: #f1f5f9; color: #fff; padding: 2px; border: 2px solid #ef4444; }	55.1 KB 336 ViT tokens	4.1 KB (390×168 WebP) 84 ViT tokens	-75.0% ViT Tokens -92.5% Payload Bytes

Live Websites Summary

• Live Websites ViT Tokens: 4,674 tokens ➔ 820 tokens (-82.5% ViT Image Token Reduction)
• Live Websites Payload Size: 492.3 KB ➔ 44.3 KB (-91.0% Network Payload Reduction)
• Multi-Viewport Cross-Correlation: Testing Wikipedia on both Desktop (1280×800) and Mobile (390×844) proves that ZeroVisual correlates multi-breakpoint failures under a single testId, feeding both cropped bounding boxes to the AI agent in a single compact diagnosis prompt.

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Benchmark Methodology

Evaluated Model & Architecture

• Model: RadixArk/Qwen3.8-27B-NVFP4 running locally via SGLang
• API Protocol: Standard OpenAI-compatible /v1/chat/completions endpoint
• Vision Grid: Qwen native 28×28 pixel merged patches

Evaluation Rubric

Each response is scored against ground-truth metadata across three dimensions:

1. Target Element Identification: Did the model identify the exact offending DOM component?
2. Defect Classification: Did the model diagnose the visual defect type (layout shift, contrast violation, flex collision, etc.)?
3. Probable Root Cause & Fix: Did the model deduce the correct CSS property (margin, z-index, font-size, outline) and offer a working fix?

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Key Architectural Insights

1. Vision Token Allocation (ViT Patch Math)

Without ZeroVisual, full-viewport screenshots (1280×800) consume 1,000 – 1,334 image tokens per query on Qwen ViT. With ZeroVisual’s bounding-box isolation and patch-aligned downscaling, image tokens drop to 69 – 234 tokens—an 87.3% reduction.

2. Eliminating Canvas Hallucinations

In complex pages like the GitHub PR view, feeding the full viewport caused the model to hallucinate issues in peripheral sidebars. Isolating the merge card with 32px padding eliminated peripheral noise, increasing diagnostic accuracy from 65% to 100%.

3. Reasoning Latency

Full-size images impose substantial overhead during multi-head cross-attention. In Case 5 (Dashboard Metric Overflow), response latency dropped from 72.3s to 18.9s (a 3.8x speedup), preventing client request timeouts and token exhaustion.

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Running the Benchmarks Locally

The benchmark dataset and automated test runner are included in the ZeroVisual repository:

# Generate the benchmark image dataset (synthetic + live websites)
npm run benchmark:dataset

# Run the empirical benchmark suite against your LLM endpoint
npm run benchmark

Configuration is handled via benchmark/.env:

OPENAI_BASE_URL=https://spark.ntsd.dev:8001/v1
AGENT_MODEL=RadixArk/Qwen3.8-27B-NVFP4