Text Diff Checker

What Does This Tool Do?

A diff checker compares two pieces of text and highlights exactly what changed between them, word by word. Words removed from the original appear struck through in red; words added in the new version appear highlighted in green. Everything that stayed the same is left as plain text, so you can scan a long passage and immediately spot what's different, instead of re-reading both versions line by line. The comparison updates automatically about a third of a second after you stop typing in either box, so you can paste a new version, watch the highlights update, and keep refining either side without ever clicking a separate "compare" button.

Common Uses

• Comparing two drafts of an article, script, or essay to see what an editor changed
• Checking the difference between two versions of a contract or agreement
• Spotting accidental changes when copy-pasting text between documents
• Reviewing translated or paraphrased text against the original
• Verifying that a "find and replace" pass across a document only changed what was intended
• Comparing AI-generated or rewritten text against a source draft to see exactly what was altered

A Worked Example

The two sentences preloaded into the boxes above illustrate exactly what this tool detects. The original reads "The quick brown fox jumps over the lazy dog" and the changed version reads "The quick brown fox leaps over the sleeping dog." Running the comparison produces exactly 2 words removed ("jumps" and "lazy") and 2 words added ("leaps" and "sleeping"), while every other word, "The," "quick," "brown," "fox," "over," "the," and "dog", is recognized as unchanged and left as plain text. Notice that "jumps" isn't shown as unrelated to "leaps," the algorithm doesn't try to guess that one word was "replaced" by another conceptually similar word, it simply recognizes that "jumps" no longer appears at that position and "leaps" is new, which is why a single word swap always displays as one deletion plus one insertion rather than a single "changed" highlight.

How the Diff Algorithm Works

This tool finds the Longest Common Subsequence (LCS) between the two texts, the largest set of words that appear in the same relative order in both versions, and treats everything outside that shared sequence as either removed or added. It builds this comparison using dynamic programming: a grid is filled in comparing every word of the original against every word of the changed text, tracking the length of the longest matching sequence achievable from each position, then a final pass walks back through that grid to reconstruct which specific words were kept, removed, or inserted. This is the same fundamental approach used by the Unix `diff` command and by Git's file comparison view, both trace back to an efficient LCS-based algorithm published by computer scientist Eugene Myers in 1986, which remains the standard technique behind virtually every text-comparison tool in use today, including this one.

Word-Level vs Character-Level vs Line-Level Diffing

Diff tools generally operate at one of three granularities, and each is suited to a different kind of content. Character-level diffing highlights individual letter changes, useful for spotting a single typo fix but noisy and hard to read across a full sentence rewrite. Line-level diffing, the style used by most code diff tools and version control systems, treats each full line as the unit of comparison, ideal for source code where a line typically represents one logical statement. Word-level diffing, what this tool uses, sits in between, it's the most readable option for prose, since it highlights exactly which words changed within a sentence without burying the result in individual character-level noise or collapsing an entire paragraph into one "changed line."

Common Mistakes and Limitations

Expecting moved text to be flagged as "moved." If a sentence is cut from one part of a document and pasted into another, the diff shows it as a deletion in the old spot and an insertion in the new one, not as a single relocation, since the algorithm compares sequences rather than tracking meaning.

Overlooking case-sensitivity. A word that only changed in capitalization, like "Report" becoming "report," still registers as a full removal and insertion, which can make a diff look busier than the actual substance of the edit.

Comparing text with inconsistent whitespace or line breaks. Since whitespace characters are tokenized just like words, a paragraph that's wrapped differently, or uses a line break instead of a space in one version, can generate small spurious differences unrelated to the actual wording.

Reading a Diff Result Effectively

The most efficient way to read a word-level diff isn't to scan every highlighted word individually, it's to look for clusters. A single isolated red-and-green pair in the middle of an otherwise unchanged paragraph usually represents a small, deliberate word choice edit, easy to evaluate at a glance. A dense run of several consecutive highlighted words, on the other hand, usually signals that an entire clause or sentence was rewritten rather than lightly edited, and is worth reading as a whole rather than word by word, since the individual word-level highlights inside a full rewrite don't carry much independent meaning. Getting a feel for this distinction, isolated edits versus rewritten clusters, makes reviewing a long document's diff dramatically faster than treating every highlighted word as an equally important change.

Diff Tools in Software Development

Anyone who has used Git or another version control system has already used a form of diffing, just applied to code rather than prose. A code diff typically operates at the line level, comparing entire lines between two file versions and marking whole lines as added or removed, which works well for code because a single line usually represents one complete statement or expression. Some more advanced code review tools add word-level or character-level highlighting within a changed line as a secondary layer, similar in spirit to what this tool does for full paragraphs of text, to make it easier to spot exactly which part of a long line actually changed. The underlying algorithm in both cases traces back to the same LCS-based approach described above, the difference is really just what unit, lines versus words, the comparison operates on.

Why Very Large Texts Get Rejected

The dynamic programming approach behind this tool builds a comparison grid sized by the number of tokens in each text multiplied together, so comparing two 1,000-word passages means evaluating roughly a couple million grid cells, still fast in a browser, but comparing two much longer documents grows that grid quickly since the cost scales with the product of both lengths, not their sum. This tool caps that grid at 4 million cells and shows a "texts are too large to compare" message beyond that point, protecting the browser tab from freezing on an enormous computation rather than silently taking a very long time. In practice this comfortably covers anything from a sentence up to several pages of text, for genuinely book-length comparisons, breaking the text into chapter- or section-sized chunks and comparing each piece separately avoids the limit entirely while still catching every real change.

Real-World Use Cases

Editorial and content review. Writers and editors use a word-level diff to see precisely what an editor changed in a manuscript, without having to manually re-read both versions side by side to spot every small revision.

Contract and legal document review. Comparing a "redlined" version of an agreement against its original is one of the most common professional uses of a diff tool, since even a single changed word or number in a contract can carry real legal weight.

Verifying AI-assisted or automated rewrites. When text has been run through a paraphrasing tool, translation, or AI rewrite, a diff check shows exactly how much was actually changed versus how much of the original wording survived, useful for understanding how significant an automated edit really was.

Frequently Asked Questions

Does this compare word-by-word or character-by-character?

This tool compares text word by word, which is usually more readable than a character-level diff for prose and paragraphs.

Is there a limit to how much text I can compare?

Very large texts (several thousand words on each side) may take a moment to process since the comparison runs entirely in your browser. For best performance, compare a paragraph or a few pages at a time.

Does this tool detect moved or reordered text?

Not as a distinct "moved" category, if a sentence is relocated to a different part of the text, it will typically show up as a deletion at its old position and an insertion at its new one, since the underlying algorithm matches text in sequence rather than tracking blocks that changed position.

Is the comparison case-sensitive?

Yes, "Word" and "word" are treated as different tokens and will show up as a removal and an addition even if the only change is capitalization. Keep this in mind when comparing text where case may have shifted but the wording is otherwise identical.

Can I compare code snippets instead of prose?

You can paste code in, but this tool is optimized for word-level prose comparison, not line-level or syntax-aware code comparison. For reviewing code changes, a dedicated code diff tool or your version control system's diff view will give more useful, line-anchored results.

Does whitespace or line break formatting affect the result?

Yes, whitespace is tokenized alongside words, so if the same words are separated by a space in one version and a line break in the other, that whitespace difference can show up as a small change even though the words themselves didn't change.