Paste a list, log output, tags, or any line-based text. This tool removes duplicates while keeping the first occurrence.
Input
Three toggle options give you control over how duplicates are detected. These options exist because real-world data is inconsistent — the defaults handle most cases, and you adjust only what your specific data requires.
Removes leading and trailing whitespace from each line before comparing it. This means “ apple “ and “apple” are treated as the same line. Without trimming, extra spaces from copy-paste or spreadsheet exports would cause identical items to be counted as different lines.
When disabled, “Apple”, “apple”, and “APPLE” are all treated as the same line. Enable it when case differences are meaningful — for example, when deduplicating programming identifiers, file names, or tags where capitalization matters to downstream systems.
By default, blank lines are removed along with duplicates. Enable this when your list has blank lines that serve as separators between groups, and you want the structure preserved after deduplication rather than collapsed into a flat list.
The tool keeps the first occurrence of each line and removes subsequent duplicates while maintaining the original sequence. This is the expected behavior for most cleanup tasks, where the first time an item appears is the authoritative entry and later occurrences are the noise.
A statistics line above the output shows how many lines went in, how many unique lines came out, and how many were removed. This is useful for quickly gauging how much duplication was present in your original data before deciding whether to keep or adjust the options.
Copy the clean output to clipboard for immediate use, or download it as a plain text file for archiving or importing into another system. The download button produces a file named “deduped.txt” that you can open in any text editor or import into spreadsheet tools.
Duplicate lines appear most often when data is assembled from multiple sources. Merging two exported spreadsheets, combining two scraped lists, or appending new entries to an existing file all create overlap that is difficult to spot visually in long lists.
Copy-paste behavior is another frequent source. When you paste the same block of text twice by mistake, or when different sections of a document repeat the same bullet points or tags, duplicates accumulate invisibly. They only become obvious when you count lines or try to import the list into another system and notice unexpectedly inflated numbers.
Form responses and user input often produce duplicates because people submit the same entry more than once, or because the same concept is expressed with slight capitalization or spacing differences. This is exactly the scenario the Trim and Case Sensitive options are designed for: normalizing the differences so the comparison step works correctly.
Use these examples to see where duplicate lines create cleanup work in lists, exports, research notes, and copied data.
Before importing a list of email addresses or names into a mailing tool, deduplication removes repeated contacts that would otherwise receive the same message twice. Running with trimming and case-insensitive matching catches variations that a simple sort-and-scan would miss.
Tag lists collected from multiple sources frequently contain the same keyword in different forms. Pasting all tags here and running case-insensitive deduplication gives you a clean, unique set ready to apply to content without redundancy.
Export a single column from a spreadsheet, paste it here, remove duplicates, then paste the result back. This is faster than using spreadsheet deduplication functions for a quick one-off cleanup where you do not want to edit formulas or change your source file.
Repeated log entries or debug output lines can be deduplicated here to focus on unique events. Enable case-sensitive matching when log messages have meaningful case differences, such as different log levels expressed as WARNING vs warning.
Lists of URLs collected from multiple crawls or research sessions frequently have repeats. Deduplicating before further processing saves time when checking links, building sitemaps, or auditing backlink profiles.
When compiling a reference list from multiple documents, the same source often appears multiple times. Running your raw list here before formatting a bibliography saves the tedious step of hunting for duplicates manually in a long list.
This section explains how duplicate detection works so users know what will be removed and what will be preserved.
The tool works line by line. It reads the input from top to bottom, normalizes each line according to your settings (trimming whitespace if enabled, lowercasing if case-insensitive), and then checks whether that normalized form has been seen before. If it has, the line is dropped. If it has not, it is added to the output in the same position it appeared in the original.
This first-occurrence-wins approach preserves your original ordering, which matters when the sequence of your list conveys priority or meaning. If you need the opposite — keeping the last occurrence instead of the first — paste the list in reverse order, deduplicate, and then reverse the output manually.
The statistics line reports the total input line count, the unique output line count, and the number of removed lines. If the removed count is zero, your list was already unique. If it is close to the total, your source data had significant redundancy. Both are useful signals before you proceed to the next step in your workflow.
Deduplication is typically the first cleaning step before any formatting or import. Paste your raw list, check the statistics to understand how much duplication exists, adjust the options if needed, and copy the clean output.
After deduplication, the most common next steps are: formatting the list with the List Maker or adding it to a document in the notepad, importing the unique lines into a spreadsheet or database, or further processing such as sorting, categorizing, or wrapping in additional structure using a macro template.
Download the result when you need a portable record. The file contains one plain-text item per line, ready for another tool that accepts line-based imports.
If you deduplicate similar lists regularly — weekly tag exports, monthly contact lists, recurring keyword research — storing a Markdown template as a macro makes the post-processing step faster. Deduplicate the raw data here, then insert the template macro in the notepad and paste the clean lines into the appropriate section.
This keeps a consistent structure across reports and documents that use the same data format, without manually reformatting the output each time you run the cleanup.
These answers cover exact matches, case sensitivity, ordering, blank lines, and exporting cleaned text.
Use the list maker or notepad to format and export your deduplicated result into a final deliverable.