A comprehensive overview of Large Language Model (LLM) classifications, explaining the diverse ways these advanced AI systems are categorized. It outlines classification axes based on training paradigms (e.g., Base, Instruction-Tuned, RLHF, Constitutional AI), core capabilities (e.g., Reasoning, Tool-Using, Multimodal, Specialized), architectural designs (e.g., Decoder-Only, Encoder-Decoder, Mixture of Experts), and model scale (SLMs, general LLMs, Frontier Models). The text also explores advanced/hybrid types like RAG and Agent models, highlighting the increasing overlap and synergy between classifications in modern LLMs. Finally, it discusses the challenges in evaluating these diverse models and anticipates future trends in LLM development and their potential impact on classification frameworks.