Open Source Methods You Can Trust
Automated testing platform that runs every model through rigorous evaluations across core skills and benchmarks. Get comprehensive insights into model performance, reliability, and capabilities.
Measuring The Right Way
Comprehensive evaluation across key performance areas that matter most for real-world applications
Core Skills
Skill system that measures real-world usefulness with clear, everyday tasks instead of academic benchmarks.
Benchmarks
Unified and repeatable framework to test AI on a large number of different benchmarks.
Driven by Innovation
Evaluation happens in authentic settings, charting skills as interconnected learning pathways.
How We Evaluate Models
Rigorous, automated testing process ensures consistent and reliable performance metrics
1. Automated Testing
Every model is automatically tested across our standardized benchmark suite with consistent prompting and evaluation criteria
2. Statistical Analysis
Statistical methods ensure test reliability, account for variance, and provide confidence intervals for all scores
3. Continuous Updates
Daily re-evaluation ensures scores reflect the latest model versions and performance improvements
Evaluation Platform Features
Comprehensive tooling for transparent and reliable model assessment
Continuous Model Monitoring
Real-time tracking of model performance changes, version updates, and capability improvements across all connected models.
Confidence Scoring System
Advanced statistical confidence intervals and reliability metrics provide context for every benchmark score.
Performance Analytics Dashboard
Interactive visualizations and detailed analytics help you understand model capabilities and make informed decisions.
Generative Benchmarks
Custom generative benchmarks that change periodically to avoid contamination.
Evaluation by the Numbers
Comprehensive testing scale and statistical rigor ensure reliable benchmarks
Scientific Methodology
Our evaluation platform follows rigorous scientific standards with peer-reviewed methodologies and statistical best practices.
Standardized Protocols
Consistent testing environments, prompt formatting, and evaluation criteria across all models and benchmarks.
Statistical Rigor
Proper sample sizes, confidence intervals, and significance testing ensure reliable and reproducible results.
Transparency Approach
To ensure fairness and transparency in measurement, all code is open source and fully reproducible.
Integration Example
Use your existing OpenAI SDK with Pnyx's intelligent routing by simply changing the base URL:
Ready to Explore Model Performance?
Dive into comprehensive benchmarks and performance analytics. Compare models, track improvements, and make data-driven decisions.