Why the Same Prompt Costs More Tokens on Some AI Models Than Others

When using large language models (LLMs), users are billed by tokens — subword text chunks — rather than by words or characters. Nearly all major models, including GPT, Claude, and Gemini, use a method called Byte-Pair Encoding (BPE), which splits text based on statistical frequency patterns learned during training. Because each model is trained on different data with a different vocabulary size, the same sentence can produce varying token counts across models. This also explains why non-English languages, complex JSON, or rare words tend to cost more tokens than plain English prose. Understanding how tokenizers work allows developers to estimate feature costs accurately and optimize prompts before deployment.