Word Frequency Counter: How It Compares to the Alternatives
Reviewed by the FreeOnline.fyi team · Updated 2026-09-16
Why paste-and-count beats installing software
We've tried most of the ways to get a frequency breakdown out of a block of text: desktop concordance software, browser extensions, spreadsheet formulas built from SPLIT and COUNTIF, and the occasional twenty-line Python script. Each one works, but each asks for something first — an install, an account, a file upload, or a fair amount of setup before you see a single number.
The Word Frequency Counter takes the opposite approach. You paste text into the textarea and counting starts immediately; there is no submit button, and nothing leaves your browser. The input handles roughly two million characters, which covers a long essay, a full subtitle file, or a sizeable scrape of page copy.
Privacy is the practical difference here rather than a slogan. Because the analysis runs client-side, unpublished drafts, interview transcripts, and client documents never touch a server. That is the same reasoning behind the rest of the free online tools on the site, including the World Time Meeting Planner — small jobs that shouldn't require a login.
What the ranked table actually shows
Above the results you get a single line: "Top 20 of N words · M unique words". N is the total number of tokens that survived your filters, and M is how many of them were distinct. Those two numbers alone often tell you something — 4,000 words with only 350 unique ones reads very differently from 4,000 words with 1,600 unique ones.
Each row shows rank, the word, its count, and its percentage of all counted words, with a thin bar scaled against the top entry so the drop-off is visible at a glance. If your text has fewer than 20 distinct words, the empty rows are omitted instead of padding the table with blanks.
One detail worth internalising: the percentage is a share of counted words after your filters, not of the raw character count. Turn on stopword removal and "the" disappears from the denominator too, which pushes every remaining word upward. That is correct behaviour, but it means two runs with different settings are not directly comparable.
Case mode and stopwords change everything
Case-insensitive matching is the default, so "The", "the" and "THE" collapse into one row. That is almost always what you want for prose. Switch to case-sensitive when capitalisation carries meaning — brand names versus common nouns, for instance, or checking whether a style guide's rule about "internet" versus "Internet" is actually being followed.
The stopword checkbox is on by default and removes the usual suspects: the, and, of, to, a, in, and their relatives. Leaving it on gives you the content-bearing vocabulary, which is what most people mean by "what is this text about". Turning it off is instructive in its own right — the top of the list fills with function words, which is the pattern behind Zipf's law, where the most common word tends to be roughly twice as frequent as the second.
If you are chasing a specific keyword, leave stopwords on and scan the ranked rows. If a term you care about is missing from the top 20, download the CSV and search the full list rather than toggling settings and guessing.
Exporting to CSV without spreadsheet pain
The Download CSV button produces word-frequency.csv entirely in your browser using a blob and an object URL — no round trip to a server. You can export the full ranked list or just the visible top 20, whichever suits; the columns are rank, word, count, and percentage.
Two details matter when the file lands in Excel or Google Sheets. Fields containing commas, quotes, or line breaks are properly quoted and escaped, and the file is written with a UTF-8 byte-order mark so accented and non-Latin words open correctly instead of turning into mojibake. That escaping follows the RFC 4180 rules for CSV.
From there you can sort, pivot, or chart the list. A quick scatter of rank against count is a decent visual check for whether a text follows the usual distribution or whether one term is being hammered unnaturally hard.
Where this tool falls short
There is no stemming or lemmatisation. "Run", "runs" and "running" are three separate entries. If you need them merged, export the CSV and group them yourself; the tool will not guess at word families.
Phrase counting is out of scope too. Bigrams and trigrams aren't measured, so "climate change" appears as two words in two separate rows. And the visible table caps at 20 rows by design — the full ranking lives in the CSV, so download it if you need row 47.
Treat the numbers as a starting point rather than gospel. Tokenisation depends on how punctuation and hyphenated words are split, and different tools draw those lines differently. For anything you plan to publish or cite, spot-check a handful of counts against your source text.
Practical uses for students, writers and SEOs
Students: paste an essay and look for a word you've leaned on too heavily — if "therefore" or "significant" sits in the top five, a reader will notice. Writers: run a chapter through it before editing; the ranked list is a fast tell for repetitive sentence openers and crutch words.
SEO and content work: keyword density checks in seconds. If your target term shows up 40 times in 900 words, that's over 4% and probably reads as stuffing. Compare it against a competing page's visible copy to see the difference in vocabulary breadth rather than just the head term.
Journalists and researchers: subtitle and transcript files are plain text, so dropping an interview into the box shows which themes recur without a single manual tally. Save the CSV alongside your notes so the analysis is reproducible if anyone asks how you arrived at it.