How Tokenization Shapes AI Costs and Reasoning in Large Language Models

Large language models process text as tokens — compressed chunks that can represent full words, word fragments, punctuation, or whitespace — rather than individual letters or complete sentences. Tokenization is built using Byte Pair Encoding, a statistical method that merges frequently occurring character pairs into reusable vocabulary units, making models more efficient across diverse text types. Because AI APIs charge per token rather than per word, formatting choices such as using pretty-printed versus minified JSON can inflate or reduce costs by 30–40% at scale, with output tokens typically priced higher than input tokens due to greater compute demands. This token-based architecture also explains why models sometimes fail at letter-counting tasks — a word like 'strawberry' is split into chunks like 'straw' and 'berry', making individual characters invisible to the model's reasoning process. Understanding tokenization is therefore essential for both managing AI infrastructure costs and anticipating the inherent limitations of language model reasoning.