Comparison
Token Count vs Tokenization
Token Count 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 Count
Token Count comes up when the question is fundamentally about inference.
"Hello, world!" = ~4 tokens (GPT-4o).
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 Count and Tokenization?
Token Count: Token count is the number of tokens in a piece of text under a specific tokenizer. The unit of LLM pricing, context limits, and rate limits. 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 Count vs Tokenization?
Token Count is the right concept when you are focused on inference. Tokenization applies when you are focused on inference.
Are Token Count and Tokenization the same thing?
No. Token Count is inference; Tokenization is inference. They are related but address different parts of the AI stack.