Predict the token count difference between 'def fibonacci(n):' and 'def f(n):' in cl100k_base.

In `cl100k_base`, 'def fibonacci(n):' is about five to six tokens, while 'def f(n):' is about four. The difference is the identifier name. A tokenizer does not store every possible word. It stores common pieces. The keyword 'def' is so frequent in code that it is a single token. A one-letter name like ' f' (with its leading space) is also a single token. But 'fibonacci' is long and relatively rare, so it has no single entry. The tokenizer breaks it into familiar fragments, something like ' fib' plus 'on' plus 'acci', which adds up to two or three tokens just for that one name. The punctuation '(n):' takes one or two tokens in both cases. So the real gap comes entirely from how the function name is spelled. The lesson: token count tracks the rarity and length of your identifiers, not the number of words you typed.

**'def fibonacci(n):'** lands around five to six tokens; **'def f(n):'** around four. The gap is the identifier. Walk it through. 'def' is one token because it is a ubiquitous keyword. The signature '(n):' is one or two tokens. Those parts are identical in both snippets. The only thing that changes is the name. Here ' f' is a single token, but ' fibonacci' fragments into roughly two or three subword pieces. It is a long, uncommon string with no dedicated merge. The principle is that **BPE** gives single tokens to frequent patterns and splits rare ones into known subwords. Identifier length and rarity, not word count, drive the cost. The production implication is subtle but real. Verbose, descriptive naming, which is good engineering practice, costs more tokens than terse naming. Across a large prompt full of long camelCase or snake_case names, that adds up. It does not justify writing cryptic code, but it does mean token budgets for code generation should be measured on real identifiers rather than estimated from character or word counts.