LLM benchmarks provide an objective way to evaluate AI language models' capabilities and compare their performance. A benchmark typically consists of a dataset, questions or tasks, and a scoring mechanism. Benchmarks are valuable for organizations, developers, and users as they offer a standardized comparison of LLMs, making it easier to select the best model for specific use cases. The most common benchmarks include ARC, HellaSwag, MMLU, TruthfulQA, WinoGrande, GSM8K, and SuperGLUE, each testing various aspects of an LLM's performance such as knowledge, reasoning, natural language inference, and conversational capabilities. However, there are drawbacks to relying solely on benchmarks, including benchmark leakage, where models may overfit to the specific challenges posed by a benchmark, and limitations in simulating real-world conversations and specialized domains. Despite these limitations, benchmarks remain an essential tool for assessing LLMs' capabilities and comparing their performance, with leaderboards providing a way to evaluate and rank models based on multiple benchmarks.