LLMVal 🚀
A clean, elegant dashboard for discovering the smartest, highest-value LLMs across OpenRouter, ranked against live Artificial Analysis benchmark evaluations (General Intelligence, Coding, Agentic, and Speed).
✨ Features
- General-Purpose Model Discovery: Live intelligence benchmarks paired with real-time OpenRouter pricing.
- Deep Benchmark Dimensions:
- General Intelligence (AA): Aggregate multi-domain intelligence index.
- Coding Index (AA) & LiveCodeBench: Real-world programming and contamination-free contest coding.
- Autonomous Agents & Tool-Use: TerminalBench (CLI agents) and Tau-bench (multi-turn workflows).
- Math & Quantitative Reasoning: Math 500 and AIME Olympiad logic.
- Frontier Reasoning: GPQA (PhD-level hard science) and HLE (Humanity's Last Exam).
- Instruction Following: IFBench strict formatting & JSON adherence.
- Speed & Latency Telemetry:
- Throughput (Tokens/Sec): Real generation speed.
- Time to First Token (TTFT): Snappiness for interactive chat and completion.
- Creator & Lab Filtering: Filter instantly by lab (e.g. Anthropic, OpenAI, DeepSeek, Alibaba / Qwen, Google, Meta, Mistral).
- Curated Recommendations:
- 🏆 Best Value: Highest quality-per-dollar among paid models.
- 🧠 Smartest in Budget: Highest scoring model under your price ceiling.
- ⚡ Fastest Generation: Top throughput model passing your quality threshold.
- 🎁 Best Free Model: Top-scoring 100% free model.
- Batch API Toggle: Optional toggle to include 50%-discounted asynchronous batch endpoints.
- Quota-Protected Disk Caching: OpenRouter prices cache for 30 minutes; Artificial Analysis benchmarks cache for 24 hours into
benchmarks.json. - Zero External Dependencies: Powered strictly by Python's standard library.
🚀 Installation & Usage
Method 1: Install via pip (Recommended)
From the project directory:
pip install .
Or for local development (editable mode):
pip install -e .
Once installed, simply run anywhere from your terminal:
llmval
Method 2: Run directly without installation
# Direct python run:
python main.py
# Or as a module:
python -m llmval
Your default browser will automatically open to http://localhost:8000.
⚙️ Options & Flags
# Custom port
llmval --port 9000
# Headless / server mode (do not open browser automatically)
llmval --no-browser
# Custom Artificial Analysis API Key
llmval --api-key your_api_key_here
Environment variables are also supported:
PORT=8000AA_API_KEY=your_keyNO_BROWSER=1
🛠️ Custom Model Matching & Overrides
1. Aliases (aliases.json)
If an OpenRouter model slug doesn't automatically match an Artificial Analysis entry, map it manually:
{
"anthropic/claude-3.7-sonnet": "claude-3-7-sonnet",
"meta-llama/llama-3.3-70b-instruct": "llama-3-3-70b-instruct"
}
2. Manual Benchmarks (benchmarks.json)
You can supply your own score overrides for any OpenRouter slug:
{
"custom/my-fine-tuned-model": 48.5
}
📁 Project Structure
llmval/
├── llmval/
│ ├── __init__.py
│ ├── __main__.py
│ └── app.py # Core engine, API fetchers & Web UI
├── main.py # Top-level runner
├── pyproject.toml # Package & metadata definition
├── requirements.txt # (Standard library only)
├── .gitignore # Ignores cache files & bytecode
├── .env # API keys and server configuration
├── aliases.example.json # Example alias mapping
└── benchmarks.example.json # Example benchmark overrides
👨💻 Creator
Created by Hariharen.
📄 License
MIT License.
Metadata
Release files for llmval 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llmval-1.0.1.tar.gz | 47.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llmval-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 94.2 kB
Release files / llmval-1.0.1.tar.gz
| Download URL | llmval-1.0.1.tar.gz |
|---|---|
| Size | 47.9 kB |
| Tags | Source |
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Release files / llmval-1.0.1-py3-none-any.whl
| Download URL | llmval-1.0.1-py3-none-any.whl |
|---|---|
| Size | 46.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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