Understand how text becomes tokens, and why token boundaries explain model behaviour on numbers, code, and non-English text. Use when a model behaves strangely on specific strings or costs more than expected.
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---
name: tokenization
description: Understand how text becomes tokens, and why token boundaries explain model behaviour on numbers, code, and non-English text. Use when a model behaves strangely on specific strings or costs more than expected.
---
# Tokenization
Models do not see characters or words; they see tokens produced by a
learned vocabulary. A surprising amount of model behaviour, including
arithmetic errors and cost differences between languages, follows
directly from where the boundaries fall.
## Method
1. **Know that tokens are subwords, not words.** Common words are one
token, rare words split into several, and the split is learned rather
than linguistic.
2. **Expect worse behaviour on badly tokenized content.** Numbers split
inconsistently, which is part of why arithmetic is unreliable, and
unusual identifiers fragment into many tokens.
3. **Account for language cost differences.** Text in languages
under-represented in the vocabulary uses several times more tokens
for the same content, affecting both cost and effective context (see
character-encoding).
4. **Measure context in tokens, not characters.** A character estimate
is a rough approximation that fails exactly when the content is
unusual (see context-window-management).
5. **Watch whitespace and leading spaces.** They are part of tokens, so
a word with and without a preceding space are different tokens, which
affects prompting and matching.
6. **Understand vocabulary boundaries in fine-tuning.** Domain terms
that fragment badly can justify vocabulary extension, at the cost of
retraining embeddings.
7. **Inspect tokenization when debugging.** Looking at the actual token
split explains behaviour that reasoning about the text cannot.
## Boundaries
Tokenizers differ between model families, so token counts and behaviour
do not transfer. Tokenization is fixed at training time and cannot be
changed for an existing model. It explains some failures and is not the
cause of most reasoning errors.