LLM Value Rankings

Find the best value AI models — intelligence per dollar, ranked daily.

🌐 Live site: https://yyh-001.github.io/llm-value-rankings/

English · 简体中文

🌐 Live Site · 🐛 Report Bug · ✨ Request Feature

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Overview

LLM Value Rankings compares 300+ LLMs by combining:

• Intelligence — Artificial Analysis Intelligence Index (OpenRouter embedded benchmarks)
• Speed — output tokens per second
• Price — blended input/output cost from OpenRouter

The result is a daily-updated leaderboard that answers one question: how much AI capability do you get per dollar?

Static site · zero backend · deploy with GitHub Pages in one minute.

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Features

	Feature	Description
📊	Smart ranking	f(Intelligence) × Speed^0.8 / Price, min score 25
📈	Day-over-day delta	See rank changes vs yesterday (↑2 / ↓1 / NEW)
🏅	Top 3 podium	Highlight the best value models on the homepage
🔍	Live search	Filter by model name or ID
🌍	Bilingual UI	Chinese / English toggle
🌙	Dark mode	System-aware theme with manual override
⭐	GitHub Star	One-click star from the header
📱	Responsive	Card layout on mobile, table on desktop
🤖	Auto-updated	GitHub Actions fetches fresh data daily

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Quick Start

Preview locally

git clone https://github.com/yyh-001/llm-value-rankings.git
cd llm-value-rankings

python -m http.server 8080
# or: npx serve .

Open http://localhost:8080.

Deploy to GitHub Pages

1. Fork this repository
2. Go to Settings → Pages
3. Source: Deploy from a branch
4. Branch: main · Folder: / (root)
5. Visit https://<your-username>.github.io/llm-value-rankings/

Update data manually

pip install -r scripts/requirements.txt
python scripts/fetch_data.py
git add data/models.json data/rank_history.json
git commit -m "chore: update model data"
git push

Or trigger the Update Model Data workflow from the Actions tab.

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Methodology

Value score

Value = f(Intelligence) × Speed^0.8 / Price

f(x) is a nested square transform around the mean intelligence score:

f(x) = (avg + (x - avg)²)²          if x ≥ avg
f(x) = (avg - (avg - x)²)²          if x < avg  (excluded when inner ≤ 0)

This rewards models above the average more than a plain square, without the harsh spread of a pure 4th power. The UI shows the raw intelligence score; ranking uses the transformed value.

Metric	Source	Notes
Intelligence	OpenRouter embedded AA intelligence_index	Artificial Analysis Intelligence Index, 0–100
Speed	OpenRouter Endpoints API + RSC page scrape	Best provider p50 throughput (matches OpenRouter header)
TTFT	OpenRouter Endpoints API	Time-to-first-token p50 in seconds
Price	OpenRouter	Uptime-weighted effective price: 3:1 input/output token mix, 70% cache-hit on input ($/1M)

Score scale (0–100)

After computing the raw value above, scores are normalized to a 0–100 scale. The top-ranked model is always 100; others are proportional.

Exclusion rules

Models are excluded from ranking when:

• Raw intelligence score is below 25
• The transformed capability score is ≤ 0 (inner term below zero for below-average models)
• The model name contains distill

Ranking rules

• Text-output LLMs only (image-generation models excluded via OpenRouter output_modalities)
• Requires both intelligence score and pricing data
• Models with distill in the name are excluded
• Sorted by value score descending

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Project Structure

llm-value-rankings/
├── index.html                  # Entry page
├── css/style.css               # Styles (light / dark)
├── js/
│   ├── app.js                  # Core logic
│   └── i18n.js                 # Internationalization
├── data/
│   ├── models.json             # Model data (auto-updated)
│   └── rank_history.json       # Daily rank snapshots
├── scripts/
│   ├── fetch_data.py           # Data fetcher & rank calculator
│   └── requirements.txt
└── .github/workflows/
    └── update-data.yml         # Daily cron job

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Data Sources

Data	Provider	Method
Pricing & model list	OpenRouter API	REST
Intelligence	OpenRouter embedded AA benchmarks	intelligence_index field on model objects
Speed & TTFT	OpenRouter	Endpoints API + RSC page scrape

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Tech Stack

Layer	Stack
Frontend	HTML · CSS · Vanilla JS
Data	Static JSON
Pipeline	Python 3.11 · GitHub Actions
Hosting	GitHub Pages

No build step. No framework. No database.

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Contributing

Contributions are welcome! Here are good starting points:

1. Add model mappings in scripts/fetch_data.py
2. Improve UI / i18n copy
3. Refine the ranking algorithm
4. Fix bugs via Issues

# Fork → branch → commit → pull request
git checkout -b feat/your-feature

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License

MIT © 2026