Which tokenizer does GPT-4o use?

GPT-4o uses the o200k_base byte-pair encoding (BPE), a successor to the cl100k_base vocabulary used by GPT-4 and GPT-3.5-turbo. o200k_base has a larger vocabulary and is more efficient on code and non-English text.

How accurate is this estimate?

This is a heuristic based on the measured average characters-per-token ratio for the o200k/cl100k families. For English prose it is typically within 5-10% of the exact tiktoken count; for code or non-Latin scripts the gap can be larger.

Is my text sent to OpenAI or anywhere else?

No. The estimate runs entirely in your browser. Nothing you paste is uploaded, stored, or logged.

Do both the prompt and the reply count toward the limit?

Yes. Your input tokens plus the model's generated output tokens both consume the 128K context window, and both are billed. Leave headroom for the response.

GPT-4o Token Counter

GPT-4o token counter

Paste any text and instantly estimate how many tokens it uses under GPT-4o’s tokenizer. Tokens — not words or characters — are the unit OpenAI bills and limits on, so knowing the count helps you stay inside the 128K context window and predict cost before you send a request.

How the estimate works

GPT-4o tokenizes with o200k_base, a byte-pair encoding (BPE) vocabulary that splits text into roughly 4 characters per token for typical English. This tool applies that measured ratio, blended with a word-boundary heuristic, so the result tracks the real tiktoken count closely without bundling the full vocabulary in the browser. A system prompt, if you add one, is counted separately and summed into the total request size.

Tips and notes

Code, JSON, and non-Latin scripts pack fewer characters per token — expect a higher count than the English-tuned estimate.
o200k_base is more token-efficient than the older cl100k_base, so the same text often costs slightly fewer tokens on GPT-4o than on GPT-4.
For an exact count before a high-volume production call, run OpenAI’s own tiktoken library with the o200k_base encoding.