Comparison
Token vs Tokenization
Token and Tokenization are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Token
Always think in tokens, not characters, when planning prompts, budgets, and context windows.
"Hello, world!" tokenizes to roughly 4 GPT-4o tokens.
When you would reach for Tokenization
Tokenization comes up when the question is fundamentally about inference.
"reading" tokenizes to one token in many tokenizers.
Frequently asked
What is the difference between Token and Tokenization?
Token: A token is the basic unit an LLM reads and writes — usually a word piece (3-4 characters). LLMs are priced and sized by tokens, not words. Tokenization: Tokenization is the process of splitting raw text into the discrete tokens an LLM consumes. Most modern LLMs use a learned byte-pair-encoding (BPE) tokenizer.
When should I use Token vs Tokenization?
Always think in tokens, not characters, when planning prompts, budgets, and context windows. Tokenization applies when you are focused on inference.
Are Token and Tokenization the same thing?
No. Token is inference; Tokenization is inference. They are related but address different parts of the AI stack.