Text Diff Checker

Compare two texts and see exactly what was added or removed — by line or word. Free, in your browser.

Compares two texts line-by-line or word-by-word, entirely in your browser.

Free to use — premium coming soon

FREE
  • Line & word diff
  • Colour-coded changes
  • 100% private
PREMIUM
  • Remove ads
  • Side-by-side & file upload

About the Text Diff Checker

Text Diff Checker compares two blocks of text and highlights exactly what changed between them, line by line and word by word. Paste an original version on one side and a revised version on the other, and the tool marks additions, deletions, and edits in color so you do not have to hunt for them manually. It is built for the everyday moments when two versions look almost identical: a contract that came back with one quietly altered clause, a config file someone edited on the server, or a paragraph a co-writer rewrote. Instead of reading both copies twice and trusting your memory, you see the precise delta in seconds.

Reach for a diff checker whenever the cost of missing a small change is high. Developers use it to review code edits, confirm a copy-paste pasted cleanly, or compare two JSON or API responses to spot an unexpected field. Writers and editors use it to track proofreading changes when no "track changes" mode was on. Legal, finance, and operations teams use it to verify that a revised proposal, policy, or agreement only differs where it should. It is also handy for comparing two log outputs, two translations, or a draft against a published version to audit what an editor actually touched.

Under the hood, line-and-word diffing rests on the longest common subsequence (LCS) problem: finding the largest set of lines that appear in both texts, in order. Whatever is not part of that shared backbone is reported as an insertion or deletion. The classic engine here is Eugene Myers' 1986 O(ND) algorithm, which models the comparison as a shortest path through an edit graph (horizontal moves are deletions, vertical moves are insertions, diagonals are matches). The same family of algorithm powers git diff and GNU diff, so the changes you see here line up with what a reviewer would see in a pull request. You can read results as a side-by-side split view or a compact unified view.

This tool runs entirely in your browser. Both texts are compared locally with JavaScript and are never uploaded to a server, which matters when you are diffing contracts, credentials, internal configs, or anything confidential. Because it works on plain characters, results are exact and deterministic: the same two inputs always produce the same highlighted diff, with no rounding or interpretation. One thing to keep in mind is that a diff shows where text differs, not why; identical-looking changes such as a swapped straight versus curly quote, a trailing space, or a tab-versus-spaces edit are real differences and will be flagged, which is usually what you want when accuracy counts.

Frequently asked questions

What is the difference between a unified and a side-by-side diff?

Side-by-side puts the original and revised text in two parallel columns, which is easiest to read on a wide screen for quick visual comparison. Unified view interleaves both versions in a single column with added lines marked + and removed lines marked -, which is the compact format git and .patch files use and is better for narrow screens.

Does this tool do line-level or word-level differences?

Both. It first aligns the texts by line to show which lines were added or removed, and within changed lines it highlights the specific words or characters that differ, so you can see a single edited word inside an otherwise unchanged sentence.

Is my text uploaded anywhere when I compare it?

No. The comparison runs entirely in your browser using local JavaScript, so neither text leaves your device. That makes it safe for comparing contracts, configuration files, or other confidential content.

Why is the tool flagging changes I cannot see?

Diffing is exact, so invisible differences count: trailing spaces, tabs versus spaces, straight versus curly quotes, or different line endings all register as changes. These are genuine character differences, which is helpful when you need precise verification.

Will the results match what I see in git or a pull request?

Generally yes. The tool uses the same longest common subsequence approach (the Myers O(ND) algorithm family) that powers git diff and GNU diff, so additions and deletions align with what a code reviewer would see in a PR.

From our blog

Which Text Case Should You Use? A Practical Guide for Writers and Developers

By the Super Simple Digital Tools Team · Updated June 2026

Capitalization is not just cosmetic. The case you choose signals context: a marketing headline in Title Case looks authoritative, the same words in all caps read as shouting, and a database field written in Title Case will simply break a query. Picking the right case is about matching the convention your reader, your style guide, or your programming language expects. Once you know which convention applies, converting text to match it should take a single click rather than a careful retype.

For everyday writing, two cases cover most needs. Sentence case is the default for body text, emails, captions and UI microcopy because it reads naturally and is easy to scan. Title Case is reserved for headlines, article titles, book and product names, and table headings. If you have inherited text that arrived in ALL CAPS, the fastest fix is to convert it to lowercase first and then apply Sentence or Title case, which clears out the original capitalization cleanly before reapplying the rule you want.

Developers face a different set of conventions, and mixing them causes real bugs. camelCase is the norm for variables and functions in JavaScript, TypeScript and Java. snake_case is expected for Python identifiers and is the safe choice for SQL column names because it avoids spaces and case-sensitivity surprises. kebab-case is the standard for CSS class names and for URL slugs, where hyphens are preferred over underscores for readability and SEO. PascalCase, a camelCase variant that also capitalises the first word, is typically reserved for class and component names.

The reason consistency matters so much in code is that many systems are case-sensitive. A variable called userId is a different name from UserID or user_id, and a typo'd casing produces an undefined value rather than an obvious error. Likewise, URLs and class names with stray capitals or spaces can fail silently. Converting an entire list of identifiers to one convention in a single pass removes that whole category of mistake and keeps a project readable for the next person who opens it.

The workflow is the same regardless of which case you need: paste the text, choose the target case, copy the result. Because the conversion is rule-based, it scales just as easily to a hundred lines as to one. The only thing the tool cannot decide for you is intent around proper nouns and acronyms, so for headlines and names give the output a final glance. With that one habit, a case converter turns a tedious manual chore into a reliable, repeatable step in your writing or coding routine.

  • To fix text stuck in ALL CAPS, convert to lowercase first, then apply Title or Sentence case so the original capitals do not survive.
  • Use snake_case for database and Python identifiers, but switch to kebab-case for URLs and CSS class names, where hyphens are the expected separator.
  • After running Title Case on a headline, scan for proper nouns, brand names and acronyms like NASA or iPhone, which automatic rules may not capitalise correctly.
  • Pick one style guide for Title Case and stick with it, since AP capitalises four-letter prepositions while Chicago keeps most prepositions lowercase, producing slightly different results.

Read the full guide →

Tool by the Super Simple Digital Tools Team. Reviewed by our editorial team. Free to use, no signup required.

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