What the model reads.

A language model handed a PDF is not handed its text, and on Claude it is billed twice over for the same page: a page pays twice. These notes measure what conversion to Markdown removes — sixteen public documents, 861 pages, counted with messages.count_tokens — and what it cannot keep.

§1A page pays twice

A PDF is a set of drawing instructions. The text a reader sees is one layer of it — sometimes present, sometimes not, never the whole — which is why a scanned page and a born-digital page cost the same picture and different text, and why the corpus in §3 holds both. Before any of an estate reaches a model one decision comes first: hand the files over as they are, or convert them. The first is what the vendor's own upload exists for.

On Claude that means a document content block carrying the raw bytes. Two sentences of the vendor's description decide the bill: "The system converts each page of the document into an image", and "The text from each page is extracted and provided alongside each page's image".^[1] The page arrives twice — as a picture of itself, and as the text lifted off it — and both are input tokens.

That is a feature before it is a cost — a model shown the picture can be asked about the chart on it — and a bill paid on every pass. The same documentation puts the text alone at 1,500–3,000 tokens a page, depending on content density, with the image cost added on top.^[1]

§2Markdown is larger than a text dump

A paper that opened with the saving would be selling, so start with the unflattering number. Over the same sixteen documents the Markdown this engine produces costs 1,323 tokens a page against 1,239 for a plain text dump — 1.068×, rendered 1.07×. Per document it runs from 0.94× to 1.73×, and the 1.73× is a tax form, where the syntax that rebuilds a grid costs more than the words in it.

So against plain extracted text there is no saving to claim, and none is claimed. What the syntax buys is the grid, the heading tree, the numbering and the lists — a value stays in the row it was printed in. A number that has escaped its row is not a cheaper answer. It is a wrong one that costs less.

For much of an estate the comparison does not arise, because there is no native path to compare against. "Binary formats such as .xlsx or .docx are not supported in document blocks and must be converted to text or PDF first."^[1] PPTX is absent from the types the Files API gives a document block.^[2] There the question is not whether to convert but whether conversion keeps the structure.

§3The measurement, done properly

The corpus is not a sample of anyone's estate: sixteen public PDFs, 861 pages, weighted towards documents hard in different ways and dense-text-heavy, because dense text is where the Markdown is largest and the saving smallest. A corpus of slide decks and scanned forms would have flattered the engine. This one does not.

Each document is named in a manifest by publisher URL, sha256 and page count, and fetched from its publisher rather than redistributed.^[3] Each was counted twice with messages.count_tokens — free, and called an estimate by the vendor itself: a count, not an invoice.^[4] Once as a document block holding the raw PDF, once as a text block holding the Markdown crunch 0.1.0 made from it, scaffolding subtracted — each on Claude Opus 5, Claude Sonnet 5 and Claude Fable 5.1. The three returned identical counts. Not close: identical.

Totals are authoritative rather than rounded per-page figures: 1 − 1,139,102 ÷ 2,404,262 = 52.6%, rendered 53%. The mean is published rather than the median of 55.4%, because it is the lower of the two. Native costs 2,792 tokens a page and the Markdown 1,323, and the range runs from 11.1% to 73.1%: a mean over an arbitrary corpus is not a benchmark result, so the spread travels with it.

§4What this cannot do, stated before anyone else says it

A paper that lists only what a conversion keeps is marketing with a token count attached. These losses are structural rather than temporary:

1. A merged cell is flattened. The GitHub Flavored Markdown table extension defines a header row, a delimiter row and data rows; colspan, rowspan and merged cell do not occur in the specification.^[5] In a PDF a spanning cell keeps its text in the first column it covered and the rest come through blank; in a presentation the covered cells are gone rather than blank. The table is named in a warning, the structure score subtracts, and no completeness claim about tables survives it.
2. A picture is not read. A figure becomes a placeholder holding its position, carrying a caption only where one was found on the page — never one generated on the page's behalf. The dimensions citable out of a drawing are therefore the ones that were in a table, and because a merged cell there is flattened, not even all of those.
3. Some structure is not extracted at all. DOCX headers, footers, footnote and endnote text are not, and the emitter has no footnote syntax on any input path — so nothing here claims footnotes survive as footnotes. Tables are skipped on rotated pages; formula cells with no cached result come through empty. Most of those announce themselves in a warning. The missing footnote syntax does not.
4. A line read off pixels is capped below certain — never certain, and flagged for help at or below guessed — and handwriting is declined rather than guessed: a signature or a margin note comes back as a page named for a person to check rather than as text.
5. On Gemini the sign reverses. Google's documentation states that each document page is equivalent to 258 tokens, and that you are not charged for tokens originating from the extracted native text in PDFs.^[6] Markdown at 1,323 tokens a page therefore costs about 5.1× the native file there. On Gemini, send the PDF.

None of these is a reason not to convert. They are the reasons to describe the conversion accurately.