Masking protects your data. Cleaning the file cuts your tokens.
When you hand a document to an LLM, two things happen that are easy to miss. Something leaves your machine, and something gets billed. Velum changes both, but for different reasons, and the honest version of the story keeps them apart.
The short version: feeding a model the clean, masked Markdown Velum produces, instead of the original PDF, costs roughly 65 to 90 percent fewer input tokens. That saving is real, and it is worth understanding, but it comes from the file format, not from the masking. The masking is what keeps your personal data off a third party's servers. Both matter. Neither needs the other to be true.
How an LLM bills a PDF
Hand a PDF to a modern multimodal model (Claude, GPT, Gemini) and it does not treat it as text. The provider processes every page two ways and charges for both:
- The extracted text from the page's text layer, tokenised normally.
- A rendered image of the page, tokenised as vision input.
That second charge is the one people forget. A full A4 or Letter page, sent as an image, lands at roughly:
| Resolution | Image tokens per page |
|---|---|
| Standard vision | ~1,600 |
| High-resolution vision | up to ~4,784 |
Every page you send carries that image cost on top of its text. Velum's Markdown has no page images, so that column drops to zero.
A worked example
Take a real two-page municipal vehicle-tax receipt. Velum's masked Markdown output measures 6,478 characters and 878 words, which is about 1,700 input tokens.
Sending the same document as the raw PDF:
| Component | Standard vision | High-res vision |
|---|---|---|
| Extracted text (both pages) | ~1,700 | ~1,700 |
| Page images (two pages) | ~3,200 | ~9,568 |
| Total input tokens | ~4,900 | ~11,270 |
So the same two pages cost about 4,900 tokens as a standard-resolution PDF, or 11,270 at high resolution, against roughly 1,700 for the masked Markdown. That is a 65 to 85 percent reduction for this document, and it holds up as a headline of 70 to 80 percent for ordinary text PDFs.
Why it scales the way it does
The saving is dominated by the per-page image charge, and page images grow linearly with page count. A rough model:
raw PDF tokens ~ text + pages x image_per_page
masked Markdown ~ text (no page images)
- More pages, bigger saving. A two-page receipt saves around 70 percent. A fifty-page contract is almost all page-image tokens, so the saving climbs toward 85 to 95 percent.
- Scanned or photographed PDFs save the most. Their text layer is nearly empty, so sending them directly is almost pure image tokens. Velum reads them into clean text and ships text only, and the saving passes 90 percent.
- Dense text pages save the least, still comfortably over 50 percent, because their extracted text is a larger share of the total to begin with.
The honest part: this is the format, not the masking
It would be easy to write "Velum saves you tokens" and leave the impression that masking does it. It does not. Replacing a name or an IBAN with a short placeholder like ⟦PERSON_1⟧ neither adds nor removes a meaningful number of tokens. Where a long value repeats, a short placeholder can even shave a little, but the effect is small. Masking is roughly token-neutral.
The token reduction above comes entirely from converting a PDF into clean Markdown text. You would get the same reduction from a plain conversion with no redaction at all. We are saying this out loud because a privacy tool that inflates one claim has spent the trust it needs for the claim that actually matters.
So there are two wins, and we state them separately:
- Format conversion turns a PDF into clean text and drops the page images, cutting input tokens by 65 to 90 percent.
- Masking keeps your personal data on your machine, so the model works on protected text and never sees the real values.
What you actually get
The document you send an LLM through Velum is clean, masked text. It is cheaper to send because it is text rather than a stack of page images, and it is safe to send because the personal data has been replaced with reversible placeholders that only your machine can restore. One is a cost saving. The other is the reason Velum exists. You get both, and you do not have to take either on faith: the numbers above are estimates you can reproduce, and the masking runs on your own device with no network calls at runtime.