Tokenizer architecture refers to the algorithm (BPE, WordPiece, Unigram, SentencePiece) that determines how text is split into tokens and what vocabulary the model operates over. This fundamentally shapes what the model can see — tokenizer choices affect multilingual performance, code handling, numerical reasoning, and effective context length. Modern models typically use byte-level BPE with vocabularies of 32K–256K tokens.