Text Diff Checker

A text diff checker is a tool that compares two versions of a text and highlights exactly what changed — which lines were added, which were removed, and which stayed the same. Whether you are reviewing code changes, proofreading a revised document, or tracking edits in a configuration file, a diff checker saves you from scanning through hundreds of lines manually.

This online text diff checker processes both inputs entirely in your browser. Paste your original text on the left and your modified text on the right, and the tool instantly shows the differences color-coded: green for added lines and red for removed lines. Unchanged lines appear in the default foreground color so you can focus on what actually changed.

The diff result is presented in two display modes. Side-by-side view places the original and modified texts in parallel columns, making it easy to see the context around each change at a glance. Unified view shows a single stream of lines prefixed with + (added) or - (removed), similar to the output of the Unix diff command or a Git pull request diff panel.

Above the diff output, four summary statistics update in real time: the count of added lines, removed lines, unchanged lines, and total lines in the merged result. These counts give you an at-a-glance measure of how extensive the changes are before you dive into the details.

This text diff checker uses the Longest Common Subsequence (LCS) algorithm — the same fundamental approach used by the Unix diff utility, Git, and most professional code-review tools. Understanding LCS helps you interpret the diff output accurately and predict how the tool handles complex edits.

The algorithm first splits each text into an array of lines. It then builds a two-dimensional dynamic programming table dp of size (m+1) × (n+1), where m is the number of lines in the original text and n is the number of lines in the modified text. Each cell dp[i][j] stores the length of the longest common subsequence of the first i lines of the original and the first j lines of the modified text.

Once the table is filled, the algorithm backtracks from dp[m][n] to dp[0][0] to reconstruct which lines are shared (unchanged), which appear only in the original (removed), and which appear only in the modified text (added). The backtrack follows a greedy path: prefer a diagonal move (matching line) over a left or up move (insertion or deletion).

The result is a sequence of diff items, each tagged as unchanged, added, or removed. Statistics are derived by filtering: added is the count of items with type added, removed is the count with type removed, unchanged is the count with type unchanged, and total is the full length of the result array.

The diff output uses a consistent color scheme to communicate change types at a glance. Green-highlighted lines (prefixed with + in unified view) are lines that exist in the modified text but not the original — they were added. Red-highlighted lines (prefixed with -) exist in the original but not the modified text — they were deleted. Unstyled lines are identical in both texts and provide context around the changes.

In side-by-side view, the original and modified columns are aligned row-by-row according to the LCS result. When a line is removed, the corresponding row in the modified column appears as a faded blank, preserving the visual alignment. Similarly, added lines show a faded blank in the original column. This alignment makes it easy to see whether a change is a simple replacement (a red line directly next to a green line) or a pure insertion/deletion.

In unified view, all lines appear in a single column in their logical order. This view is more compact and matches the standard patch format used in version-control systems. Use unified view when you want to copy the diff output or share it as a text snippet.

The summary statistics at the top are useful for quantifying the scope of changes. A document with 2 added and 2 removed lines out of 50 total is a very light edit; a document with 30 added and 25 removed lines out of 60 total represents a near-complete rewrite. Use these numbers to set expectations before reviewing the detailed diff.

Text diff tools are indispensable across many fields. Here are the most common scenarios where an online diff checker adds real value:

• Code review: Compare a function before and after a refactor to confirm only the intended lines changed. Paste code snippets directly — this tool handles any plain-text programming language.
• Document proofreading: When a colleague returns an edited version of a report or article, paste both versions to see every changed sentence without reading the whole document twice.
• Configuration file auditing: Server configs, .env files, and YAML/JSON configs are all plain text. Diff two config versions to verify a deployment change introduced only the expected lines.
• Legal and contract review: Compare two drafts of a contract to find added or removed clauses. Even a single changed word in a legal document can have significant implications.
• Academic writing: Track revisions between drafts of an essay, thesis chapter, or research abstract to ensure the substance of your argument was preserved through editing.
• Localization strings: Compare a base locale file against a translated version to find missing or extra keys in JSON or PO format translation files.
• Database query comparison: Paste two SQL queries to spot the exact line where a WHERE clause or JOIN condition differs.

Because the tool runs entirely in your browser, sensitive content — source code, personal documents, or confidential contracts — is never sent to a server. You can diff private content safely and offline once the page is loaded.

The four summary statistics shown above the diff output each measure a distinct aspect of the comparison. The table below explains what each counter means and how to use it to gauge the magnitude of changes between two texts.

A high Unchanged count relative to Total Lines means only a small portion of the document changed — a targeted edit. A low Unchanged count relative to Total Lines suggests a major revision or near-complete replacement. When Added equals Removed and both are non-zero, you likely have a line-by-line replacement, such as a wording change throughout the document.

Getting accurate and readable diff output depends on how you prepare and paste your text. Here are practical tips for using the text diff checker effectively across different scenarios.

Line-ending consistency: This tool compares line by line. If one version of your text uses Windows-style CRLF line endings and another uses Unix LF, every single line may appear as changed even if the visible content is identical. Most browsers normalize line endings when you paste from a text editor, but if you see unexpected differences, try copying from a plain-text source.

Trailing whitespace: Because the comparison is an exact string match at the line level, a trailing space on one line that is absent on the corresponding line in the other text will flag that line as changed. Strip trailing spaces with your editor's "Trim trailing whitespace" feature before pasting if you want to focus on meaningful changes only.

Large texts: The LCS algorithm has O(m × n) time and space complexity. For very long texts — thousands of lines each — the diff may take a moment to compute. For the best performance, trim your texts to the sections that differ rather than pasting entire multi-thousand-line files.

Side-by-side vs. unified view: Use side-by-side when you need context — it shows what surrounded a change. Use unified view when you want to count exact line changes or copy the diff as a patch. Both views are generated from the same LCS result.