How many tokens is 1,000 words? A conversion cheat sheet for LLM prompts

If you only want the number: 1,000 words of ordinary English is roughly 1,300 tokens. Going the other way, 1,000 tokens is roughly 750 words, or about 4,000 characters.

That is the whole answer for estimating. The rest of this post is the table, the cases where the ratio breaks, and how to get an exact count when an estimate is not good enough.

For plain English prose, using the common rule of thumb that 1 token is about 4 characters and about 0.75 words:

And in reverse, which is the direction you usually need when you are staring at a model's context limit:

Those numbers are for prose. Tokenizers split on statistical frequency, not on words, so anything unusual costs more:

So: rules of thumb are fine for "will this roughly fit". They are not fine for a hard limit or a cost estimate you are going to rely on.

For OpenAI models, tiktoken is the direct route:

import tiktoken

enc = tiktoken.get_encoding("o200k_base")
print(len(enc.encode("your prompt here")))

Anthropic and Google both expose token-counting endpoints in their APIs, which is the better option when you want the count for the exact model you are about to call rather than an approximation of it.

Most of the time you are not in a script. You have a block of text in front of you and you want to know whether it fits before you paste it. That is the case I built iLostCount for: paste the text, read the token, word and character counts as you type. No signup, and the text is not uploaded anywhere, since the counting runs in the page.

A cheap habit that saves all three: count a long document before it goes into a prompt. If a 40-page PDF turns into 30,000 tokens, you know to chunk it or summarise it first, instead of finding out from a 400 error or an invoice.

Disclosure: this post is from the iLostCount project. The tool is free and the source is public at github.com/ahmad-almazeedi/token-counter.