Tokenizer Efficiency
How effectively a tokenizer represents text — measured by the average number of tokens needed to represent a given amount of text. More efficient tokenizers produce fewer tokens for the same content.
Why It Matters
Tokenizer efficiency directly impacts API costs and context window utilization. An inefficient tokenizer wastes tokens on poorly encoded text.
Example
A tokenizer that encodes 'artificial intelligence' as 2 tokens versus another that uses 4 tokens — the first is more efficient, fitting more content in the context window.
Think of it like...
Like text compression — a more efficient system conveys the same message in fewer characters, saving bandwidth and storage.
Related Terms
Tokenizer
A component that converts raw text into tokens (numerical representations) that a language model can process. Different tokenizers split text differently, affecting model performance and efficiency.
Token
The basic unit of text that language models process. A token can be a word, part of a word, or a punctuation mark. Text is broken into tokens before being fed into an LLM, and the model generates output one token at a time.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single interaction. It includes both the input prompt and the generated output. Larger context windows allow models to handle longer documents.
Byte-Pair Encoding
A subword tokenization algorithm that starts with individual characters and iteratively merges the most frequent pairs to create a vocabulary of subword units. It balances vocabulary size with handling of rare words.