The benchmarking landscape for Large Language Models (LLMs) is complex, with various testing styles and metrics used to evaluate their performance. The MMLU benchmark tests models across multiple subjects, including humanities, STEM fields, and medicine, while the BIG-Bench Hard test assesses reasoning capabilities on challenging tasks. The DROP test evaluates discrete reasoning over paragraphs, and HellaSwag tests common sense reasoning through sentence completion tasks. Math benchmarks like GSM 8k and MATH assess reading comprehension and logical problem structuring, with scores varying widely depending on the model's performance. Code benchmarks like HumanEval evaluate LLMs' coding capabilities, while leading models in each benchmark consistently outperform others, with some showing significant gaps in their performance. Understanding these benchmarks is crucial to making informed comparisons and optimizing model selection for specific use cases.