What counts as invalid?

Inconsistent column counts, unclosed quotes and malformed structure.

Does it tell me which row is broken?

Yes, every error includes its row number.

Can it fix the file too?

It reports problems; use the CSV Formatter to normalise the file.

Does it check data types?

No — it checks structure, not whether a number column contains numbers.

CSV Validator

Check that every row has the same number of columns.

Reading time:: 2 min
Last updated:: 5 August 2026
Price:: Free, no signup

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Results (live)

Type or paste text to see the result.

What the CSV Validator does

Paste a CSV file and find out whether it's actually valid: every row checked for the right number of columns, unclosed quotes flagged, and the first broken row named so you know where to look.

Instead of a vague 'import failed' from your target system, you get 'row 42 has 5 columns, expected 7'.

Why the CSV Validator is useful

CSV imports fail silently or loudly, but rarely helpfully. Validating before import turns a frustrating guessing game into a specific to-do list.

Because validation is local, you can check exports containing customer data without sending them to a third party.

Also known as: csv validator, validate csv, csv checker, free csv validator, online csv validator, csv validator online free, csv validator tool.

How to use the CSV Validator

1
Paste the CSV
The whole file or a suspicious section.
2
Read the report
Row count, column consistency and any errors listed.
3
Fix the flagged rows
Correct the source and validate again.

Key features

Column-count consistency check
Unclosed quote detection
Row numbers for every error
Private, in-browser validation

Benefits

Know exactly which row breaks an import
Check files containing private data safely
Validate before uploading, not after failing

Real-life use cases

Debugging a failed import

A developer validates a 5,000-row export and finds row 2,847 has an unclosed quote.

Checking a supplier file

A buyer validates the monthly price list before feeding it to the purchasing system.

Verifying a hand-edited file

An analyst confirms that manual edits didn't break the structure.

Tips for the best results

The header row sets the expected column count — make sure it's correct first.
One unclosed quote can corrupt every row after it; fix quotes first.
After fixing, run the CSV Formatter for a clean final version.

Conclusion

Find the exact row that breaks your CSV before your import does.

Frequently asked questions

What counts as invalid?: Inconsistent column counts, unclosed quotes and malformed structure.
Does it tell me which row is broken?: Yes, every error includes its row number.
Can it fix the file too?: It reports problems; use the CSV Formatter to normalise the file.
Does it check data types?: No — it checks structure, not whether a number column contains numbers.
Is my data private?: Yes, validation runs locally.
Does it support semicolon-delimited files?: It expects standard comma CSV.

Related tools you may need

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TSV to CSV Converter — Convert tab-separated values to comma-separated.
CSV to TSV Converter — Convert comma-separated values to tab-separated.
CSV to JSON Converter — Turn CSV rows into a JSON array of objects.
JSON to CSV Converter — Turn a JSON array of objects into CSV rows.
JSON Diff Checker — Compare two JSON documents and list every difference.
HTML Beautifier — Pretty-print minified HTML with 2-space indentation.

Keep exploring

Browse the full tools directory, filter by category, or read guides on the Text Tools Hub blog.