CSV is one table and nothing else, with no formulas and no cell formatting. TXT is plain text, with no markup of any kind. pandoc sits between the two, and this page says exactly what happens to a CSV file on the way to becoming a TXT one.
What runs when a CSV file becomes TXT
pandoc in two passes, on a machine we rent and watch. Your CSV file is uploaded once, pandoc runs once, the TXT comes back, and neither file is kept. CSV to TXT is one of the 3649 pairs that engine was probed on with a real file, which is why it has a page here and why the pairs the probe could not prove do not.
What survives from the CSV into the TXT
Lists from the CSV file, nesting included, plus code blocks, block quotes and links, all re-expressed in TXT.
Images: pandoc extracts them out of the CSV file into a working directory and re-embeds them in the TXT, so nothing ends up pointing at a file that no longer exists.
What CSV to TXT costs you
Tables. The TXT writer cannot carry one, and the probe caught this the hard way: a CSV file, which is nothing but a table, came back as a well formed and completely empty TXT with a zero return code. The engine now refuses that case outright rather than handing you the emptiness.
Where CSV and TXT files come from
CSV. Everything writes it and nothing agrees on it. There is an RFC, 4180, and it describes common practice rather than commanding it: quoting, escaping and line endings all vary by producer. A CSV is the lowest common denominator, which is both why it is everywhere and why it surprises people.
If a model is going to read the TXT
Structure is what survives from the CSV file, and structure is what a model needs. CSV in, TXT out, with the heading tree intact instead of flattened into one long paragraph.
CSV to TXT, measured rather than promised
Running CSV to TXT against the real engine with a real file, pandoc wrote 114 bytes of TXT in under a tenth of a second, machine otherwise idle. That is one CSV file on one day and not an average, which is why the number is given together with the file that produced it.
The CSV to TXT verdict was reached by reading the text back, meaning the output decodes as readable text and still carries the witness word, which is the only check a plain text target allows. Which check was used matters, because they do not all prove the same thing, and a status code of 200 proves nothing whatsoever about whether the TXT file has anything inside it.
The same CSV file, sent somewhere else
Other outputs the probe measured out of a CSV file, so the cost of choosing TXT can be read against something. Sizes do not compare across engines, because each family was probed with its own CSV witness file.
CSV to PDF: 12,680 bytes in 1.0 seconds, verified by a format signature.
CSV to MD: 114 bytes in 0.1 seconds, verified by a pandoc round trip.
CSV to DOCX: 9,945 bytes in 0.1 seconds, verified by a pandoc round trip.
CSV to EPUB: 4,913 bytes in 0.1 seconds, verified by a pandoc round trip.
CSV to HTML: 3,894 bytes in 0.1 seconds, verified by a pandoc round trip.
CSV to RTF: 779 bytes in under a tenth of a second, verified by a pandoc round trip.
Other ways into TXT, and what they measured
Among the published routes into TXT, CSV is the number 11 largest output of the 11 measured. The witness files differ, so this ranks the probe run and not your document.
HTML to TXT: 270 bytes in under a tenth of a second.
JSON to TXT: 264 bytes in under a tenth of a second.
MD to TXT: 264 bytes in under a tenth of a second.
MOBI to TXT: 219 bytes in 0.5 seconds.
ODT to TXT: 264 bytes in 0.1 seconds.
What we will not pretend about CSV to TXT
A CSV file over 25 MB is refused before the upload finishes rather than after it, so you do not wait for a rejection.
A CSV to TXT run that passes 60 seconds is killed, and the pandoc process is killed with it. A run left behind would sit on one of the machine's two cores until somebody noticed.
32 of the 935 pairs probed in the family that serves CSV to TXT failed, and this pair is not one of them. They fail for reasons worth knowing: a writer that cannot carry what the document is made of, or output the engine could not read back. They are counted here rather than hidden, because a pair that fails quietly is worse than one that fails loudly.
pandoc never writes the TXT through a TeX engine here, and it never writes PDF at all: the eleven engines it would need are not on this machine and a TeX distribution weighs several gigabytes. Anyone who wants a PDF out of a CSV file goes through LibreOffice or calibre, both of which genuinely can.
One CSV file at a time, chosen in the browser. There is nothing else to set up and nothing else on offer.
CSV to TXT: what people ask
What actually converts my CSV file to TXT?
pandoc does it, in two passes, on a machine we rent and watch. Not a browser trick and not somebody else service: the CSV file is uploaded once, pandoc runs once, the TXT comes back, and neither file is kept afterwards.
How long does CSV to TXT take?
On the file the probe used, pandoc took under a tenth of a second and wrote 114 bytes of TXT. That is one real measurement on one real CSV file, not an average and not a promise about yours: a larger CSV takes longer, and past 60 seconds the run is stopped.
What do I lose going from CSV to TXT?
The one to know about first: Tables. The TXT writer cannot carry one, and the probe caught this the hard way: a CSV file, which is nothing but a table, came back as a well formed and completely empty TXT with a zero return code. The engine now refuses that case outright rather than handing you the emptiness.
Are the tables in my CSV file still tables in the TXT?
No, and that is a property of TXT rather than a defect of this path. TXT cannot hold a table, so a CSV file built around one is the wrong candidate for this pair.
Is CSV to TXT free?
There is a free allowance every month, and one CSV to TXT conversion costs half a credit against it. When the allowance runs out the tool says so and stops, rather than quietly handing you a worse TXT.