Why does code tokenize differently from prose?

Code is dense in punctuation, operators, indentation, and short identifiers, which BPE tokenizers split into more tokens per character than English prose. As a rule of thumb, code runs around three characters per token versus roughly four for prose, so the same character count costs more.

Do comments and whitespace count?

Yes. Every character you send is tokenized, including comments, blank lines, and indentation. Minifying or stripping comments before sending can meaningfully cut tokens on large files, though it can also hurt the model's understanding.

How accurate is the estimate?

It uses per-language character-per-token densities calibrated to modern tokenizers. It is a close planning estimate; exact counts vary by tokenizer and by how unusual your identifiers are, so verify against the provider's tokenizer for large runs.

Is my code sent anywhere?

No. All tokenization and cost maths run locally in your browser. Your code never leaves the page.

What is the Code Token Estimator?

Paste source code to get token counts and cost estimates with per-language tokenization density, so you know before you call whether a file fits the context window and what it costs. Runs entirely in your browser. It runs free in your browser on Gera Tools, with nothing uploaded.

Code Token Estimator

Name: Code Token Estimator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Code token estimator

Sending code to an LLM is more expensive than sending prose of the same length — operators, indentation, and short identifiers all fragment into extra tokens. This estimator measures your code with per-language density and tells you the token count, how much of a context window it eats, and the cost, before you spend a single API call discovering the file was too big.

How it works

The tool applies a character-per-token density tuned for each language: dense languages like JSON and minified code pack the fewest characters per token, while comment-heavy prose-like code sits closer to natural text. It estimates tokens from your pasted code, prices the input against your chosen model, and shows the fraction of a typical context window the file occupies. Everything runs in your browser.

Why code tokenises differently from prose

Modern LLMs use byte-pair encoding (BPE), which learns token boundaries from training data. Common English words often become a single token. Code is different — it is dense with punctuation (brackets, semicolons, arrow operators), short variable names that the tokeniser has not seen before, and repeated indentation characters. These patterns all produce more tokens per character than typical English prose, which is why a 500-character Python function might cost more tokens than a 500-character paragraph.

Rough rule of thumb: English prose runs about 4 characters per token; code typically runs about 3 characters per token, though highly punctuated languages like Lisp or Haskell can go lower.

Practical planning guide

Scenario	What to do
File is under 20% of the context window	Paste it directly
File is 20–70% of the context window	Consider what surrounding prompt you need alongside it
File is over 70% of the context window	Chunk it, summarise non-essential sections, or strip comments
Same file sent on every request	Ask whether your provider supports prompt caching — repeated prefixes can become nearly free

Tips and notes

For large codebases, the practical question is usually “does this fit?” — the context window bar answers it at a glance and tells you whether to chunk or summarize. If you are sending the same files repeatedly (for example a fixed framework header on every request), prompt caching can make the repeated portion nearly free. Stripping comments and dead code cuts tokens but can degrade the model’s reasoning about the code, so trim carefully. Treat the count as a close estimate and confirm with the provider’s tokenizer before sizing a large automated run.