Phrases such as “5 min read” that you see at the top of a blog post or news story are not numbers pulled out of thin air. That estimate is a simple but useful calculation obtained by dividing the number of words in the text by an average reading speed. In this article we look at how that calculation is done, what a good text statistics tool should offer beyond reading time, and where estimates of this kind can go wrong.
How is a reading time estimate calculated?
The method is quite simple: the total number of words in the text is divided by an average reading speed (words per minute, abbreviated WPM). For example, for someone who reads 200 words per minute, an 800-word article takes roughly 4 minutes. Fractional results are usually rounded up to the next whole minute; that is, a calculation of 3.2 minutes is shown as “4 min”.
The average reading speed used here is an assumption that can vary by source and by language; there is no single “correct” number valid for everyone. KEYDAL's Text Statistics tool calculates reading time based on an average of roughly 200 words per minute and presents it as a rough guide rather than an exact duration.
| Word count | Estimated reading time at ~200 words/min |
|---|---|
| 200 words | 1 min |
| 500 words | 3 min |
| 800 words | 4 min |
| 1,200 words | 6 min |
| 2,000 words | 10 min |
This table shows how the same formula applies to texts of different lengths: as the word count rises, the estimated time rises linearly. In practice, tools usually round the result of this division up to the next whole minute, so that instead of a meaningless result such as “0 minutes” at least “1 min” is shown.
Speaking time: another use of the same method
The same logic also works for estimating how long a text will take when read aloud or used in a presentation — but the average speed here is lower, because reading aloud is slower than following written text silently. As a general assumption for speaking speed, 130 words per minute is used. This estimate is useful for people preparing video scripts, writing podcast copy, or wanting a rough idea of how long a presentation will run on stage.
What should a good text statistics tool include?
Reading time on its own is not a sufficient indicator; to understand the structure of a text, several different measurements need to be presented together. A comprehensive text statistics tool should calculate at least the following values:
- Word count — the total number of words in the text
- Character count — two separate values, with and without spaces
- Sentence count — the number of sentences parsed by full stops, exclamation marks and question marks
- Paragraph count — the number of paragraphs separated by blank lines
- Average word length — the average length of the words in the text, in characters
- Average sentence length — the average number of words per sentence
These last two values are not a scoring system; they merely offer simple indicators about the structure of the text. Even so, a very high average sentence length can suggest the sentences may have a complex structure that is hard to follow, and it earns the text another pass.
Why do these calculations matter to bloggers and editors?
A reading time estimate sets an expectation for the reader about how much time to set aside before starting an article. Seeing “12 min read” at the top of a piece helps the reader decide whether to read the content now or leave it for later; this is a common practice, especially on long guide content and news sites.
Editorially, measurements such as word count and sentence length are used to check whether a text fits the intended format — for example whether a blog post stays within a set length range, whether a press release is kept short, or whether a piece of content is comprehensive enough for search engines. In review steps like these, word count is a practical tool for quickly assessing content length.
A common misconception on the SEO side is that search engines “reward” a particular word count. There is no such fixed threshold; genuinely covering a subject thoroughly and completely often naturally produces a longer text, but length is a by-product of comprehensive content, not a target. A word counter is used here not to hit a target but to notice how long a piece has grown and to trim it where needed.
Similar logic applies to newsletters and email campaigns: knowing how long an email will take gives the sender quick feedback on whether the content should be kept short and to the point or a long piece should be split up.
The limits of estimates based on a simple word count
Reading time estimates based on word count are a simplified model of real reading behaviour. A reader may read a text carefully, line by line, or skim it quickly by glancing at headings and bold phrases; those two behaviours correspond to very different durations for the same word count.
The type of content also affects the calculation. A text full of technical terms, or one unfamiliar to the reader, is read far more slowly than ordinary prose. What is more, although elements such as code blocks, tables and images are included in the word count, they are not “read” like an ordinary sentence — a code block is usually examined, copied or skipped rather than read line by line. So in technical articles containing this kind of content, the reading time estimate can deviate from the real duration.
To see the word count, character count and estimated reading time of your own text, you can paste it into the tool below; the results update live as you type.