Learning Resources
Everything you need to understand and effectively use AI benchmark data
Essential Resources
Understanding AI Benchmarks
GuideComprehensive guide to how AI benchmarks work, why they matter, and how to interpret scores.
Papers With Code
DatabaseExplore the latest research papers with code implementations and benchmark results.
HuggingFace Leaderboards
PlatformInteractive leaderboards for various AI benchmarks with model comparisons.
Stanford AI Index
ReportAnnual report tracking AI progress across multiple dimensions including benchmarks.
Benchmark Categories Explained
Understanding the different types of benchmarks and what they measure
Benchmarks evaluating general knowledge, logical reasoning, and problem-solving abilities.
Evaluating code generation, debugging, and software engineering capabilities.
Testing mathematical reasoning from elementary to competition-level problems.
Benchmarks requiring vision-language understanding and cross-modal reasoning.
Evaluating autonomous agents, API usage, and tool manipulation abilities.
Testing models on processing and reasoning over extended text sequences.
Guides & Tutorials
How to Read Benchmark Scores
Learn what benchmark scores mean and how to compare models effectively.
- •Understanding percentage scores and their meaning
- •Comparing models across different benchmarks
- •Recognizing benchmark saturation
- •Interpreting human baseline comparisons
Choosing the Right Model
Use benchmark data to select the best model for your specific use case.
- •Mapping your task to benchmark categories
- •Balancing performance vs. cost
- •Understanding model strengths and weaknesses
- •When to prioritize different capabilities
Benchmark Limitations
Critical understanding of what benchmarks don't measure.
- •Training data contamination concerns
- •Gap between benchmark and real-world performance
- •Benchmark gaming and overfitting
- •What scores don't tell you about usability