PDF to LLM Optimizer
Document
Drop a PDF here, or click to browse
One file at a time — 200+ page PDFs are fine, up to 300 DPI
.pdf up to 20 MB · stays in your browser · nothing is sent anywhere
Removes headers, footers, page numbers and spacing waste. Keeps every page.
Sample result
Sample result — a 24-page services agreement, Smart Extract, asking “Extract the contract end dates and termination penalties”. Drop your own PDF to replace it.
Before
18,452
tokens
After
4,123
tokens
Saved
78%
$0.4299 · ₹35.89
7 of 24 pages kept — 4, 5, 6, 11, 12, 18, 19 · estimate, ~4 chars per token
Optimize a PDF for ChatGPT, Claude, Gemini, or any LLM
A PDF describes a printed page: where glyphs sit, in what font, at what coordinates. It was never designed to hand a machine a clean stream of text. In a text-based workflow for ChatGPT, Anthropic Claude, Google Gemini, or another large language model, useful words can arrive mixed with whatever a text extractor recovered from the layout.
Most of what comes out is not information. The company name at the top of all forty pages. The footer with the address and the confidentiality notice, forty times. Page numbers. Watermarks. A figure caption left as [Image] where the actual chart used to be. Then there are tables: in the text layer a table is rows of values separated by runs of spaces, which costs tokens for the spaces and still leaves the model guessing which value belongs to which column.
Markdown fixes the last of those outright. A pipe table states the same grid in fewer tokens, and every current model was trained on enough markdown to read it without ambiguity. Headings become ##, bullets become -, and the structure survives instead of being inferred.
PDF-to-LLM workflow
- STEP 1ExtractRead the text layer
- STEP 2CleanRemove repeated noise
- STEP 3SelectKeep relevant pages
- STEP 4StructureBuild Markdown
- STEP 5PromptSend with a clear task
The biggest saving is not cleaning — it is not sending the page at all
If you are asking a forty-page services agreement about termination penalties, thirty-three of those pages are irrelevant to the answer and you are paying for every one. Smart Extract scores each page against the question you say you are going to ask — TF-IDF over the document's own pages, so a term appearing everywhere counts for little and a term appearing on three pages counts for a lot — and keeps the top 30%. That is where a 40% cleaning saving becomes an 80% one.
It is a keyword method, not embeddings, and that is a deliberate trade. Embeddings would handle synonyms better; they would also mean downloading a model or calling a server, and the second of those would break the only promise this tool makes.
One clean output, three major AI assistants
The result is ordinary text with Markdown headings, lists, and tables, so you can paste it into ChatGPT by OpenAI, Claude by Anthropic, or Gemini by Google. It also works as text input to an LLM API. Model limits, file-upload features, and privacy terms differ; the optimizer prepares the text but does not change the destination provider's policies.
Keep the original PDF when charts, photographs, signatures, handwriting, or page layout are evidence. This tool extracts the text layer; it does not reproduce visual meaning and it does not run OCR.
Why on-device matters here more than on most tools
The documents people most want to put through an LLM are the ones they are least able to upload: a client contract, a diligence pack, an unfiled patent, a set of medical results. ChatPDF and PDF.ai both take a copy of your file onto their infrastructure before doing anything with it. This tool reads the PDF with pdf.js inside your own tab and never opens a connection — which is why a lawyer or a founder can use it on a document they could not otherwise paste anywhere.
For the model-by-model decision table, verification checklist, and a reusable analysis prompt, read how to optimize a PDF for ChatGPT, Claude, and Gemini.
Related tools
- Token Counter — count tokens and cost for any text you already have, across nine models.
- PDF to JPG — when you need the pages as images rather than as text.
- Split PDF — over the 20 MB limit? Split it first, then optimize each part.
- Word Counter — words, characters, and reading time for the cleaned text.
- Prompt Enhancer — sharpen the question you are going to put in front of the document.
Read the guide
Frequently asked questions
Why does a PDF use so many ChatGPT tokens?
A PDF is a print format, not a text format. What an extractor pulls out of it carries every running header, every footer, every page number, and tables held together by columns of spaces — all of which are billed as tokens even though none of them answer your question. On a typical 20-page report that furniture and whitespace is a large share of the text, and you pay for it on every single call.
How do I reduce PDF tokens before sending to ChatGPT?
Three things, in order of how much they save: drop the pages that have nothing to do with your question, delete repeated headers/footers/page numbers, and rewrite space-aligned tables as markdown. This tool does all three in your browser — Smart Extract handles the first, and it is by far the biggest of the three.
Does the optimized PDF text work with Claude and Gemini?
Yes. The output is structured plain text and Markdown, so it works as prompt context in ChatGPT, Anthropic Claude, Google Gemini, and other text-capable LLMs. Keep the original PDF when the answer depends on charts, diagrams, signatures, or exact page layout, because this tool extracts the text layer rather than visual content.
Is my PDF private?
Yes. The file is read by your own browser using pdf.js, and every step after that is string processing on your device. There is no upload, no server round trip, and no account — you can confirm it by opening your browser devtools Network tab while you use the tool, or by disconnecting from the internet after the page loads. This is the difference from ChatPDF and PDF.ai, which both upload your document to their servers.
How much money does this actually save?
It depends on the document and the model. A 70% reduction on a 20,000-token document is 14,000 tokens saved per call. At GPT-4 input pricing that is roughly $0.42 a call; if you are asking twenty questions of the same document, the same optimized text is reused each time and the saving multiplies. The Cost Breakdown tab shows your document's real numbers for GPT-4, GPT-4o, and Claude 3.5 Sonnet.
Does it work with a scanned PDF?
No — and it tells you so rather than returning an empty result. A scanned PDF is a picture of a document with no text layer, so there is nothing to extract and nothing to optimize. That needs OCR, which is not part of this tool yet. A PDF exported from Word, Google Docs, or almost any contract or reporting system will work.
Are the token counts exact?
The live figures use the ~4-characters-per-token approximation, which keeps the page fast and the bundle small. Because the headline is a percentage — a ratio between two counts — the approximation largely cancels out. Click "Exact count" to load the real tiktoken encoder and replace both numbers with GPT-4's actual tokenization.