How many tokens are in a typical PDF page?

It depends on words per page and layout. A rule of thumb is about 200–400 words per page for dense text; at ~1.3 tokens per word that’s roughly 260–520 tokens per page. The calculator uses your pages and words per page to estimate total tokens.

What is a good RAG chunk size?

Chunk size is a tradeoff: larger chunks preserve context but use more tokens per retrieval; smaller chunks are more precise but can fragment meaning. The calculator suggests a range based on your document size; typical choices are 256–512 tokens for technical docs or 512–1024 for narrative.

How is embedding count estimated?

Embedding count is the number of chunks you will embed for RAG. The calculator divides estimated token count by the suggested chunk size to approximate how many vectors you will store and query.

What if my PDF has lots of images?

Images don’t contribute tokens in the same way as text. The calculator lets you set image density (e.g. image-heavy) to adjust the effective text volume and token estimate.

Should I use this or the Text to Token Calculator?

Use the PDF Token Size Estimator when you’re planning from page and word counts (e.g. whole documents, RAG pipelines). Use the Text to Token Calculator when you have actual text or a snippet and want a count by model family.

PDF Token Size Estimator

Estimate how many tokens are in your PDF and get chunk size and RAG architecture recommendations. Enter pages, words per page, and image density to see approximate token count, embedding count, and suggested setup. Built for AI engineers, chat-with-PDF startups, and knowledge base builders.

Document size

Enter pages, average words per page, and image density. We’ll show approximate token count, chunk recommendation, embedding count, and a suggested RAG architecture.

PDF dimensions

Pages

Number of pages in the PDF (1–100,000).

Avg words per page

Typical word count per page (0–5,000). ~250–400 for dense text.

Image density

How image-heavy the PDF is (affects token estimate for vision-style encoding).

NoneLowMediumHigh

Approx token count

3.9ktokens

Text: 3.9k · Images: 0

Chunk recommendation

512 tokens, 25 overlap

Use this chunk size for embedding and retrieval. Overlap helps avoid cutting context at boundaries.

Embedding count estimate

Approximate number of vectors to store for this document.

Suggested RAG architecture

Single embedding model + vector store

512-token chunks work well for this size. Cosine or dot-product similarity; no overlap needed for short docs.

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About this calculator

This calculator helps you size RAG and chat-with-PDF pipelines. Enter the number of pages, typical words per page, and how image-heavy the PDFs are. You get an approximate token count, a suggested chunk size for retrieval, an embedding count, and high-level RAG architecture guidance. Use it to plan context windows, vector store size, and API usage.

For token count from raw text (e.g. a prompt or snippet), use the Text to Token Calculator. For document processing cost (including parsing and chat), use the Document Processing Cost Calculator.

Inputs and what they mean

Input	Description
Pages	Number of pages in the PDF or typical document. Used to scale token and chunk estimates.
Words per page	Approximate word count per page (dense text is often 300–400). Affects total tokens and chunk count.
Image density	How much of the document is images vs text. Image-heavy PDFs have fewer text tokens per page.

Frequently asked questions

Related calculators

Text to Token Calculator — Token count from raw text by model family.
Document Processing Cost Calculator — Monthly cost for parsing and document chat.

About this calculator

For token count from raw text (e.g. a prompt or snippet), use the Text to Token Calculator. For document processing cost (including parsing and chat), use the Document Processing Cost Calculator.

Inputs and what they mean

Input	Description
Pages	Number of pages in the PDF or typical document. Used to scale token and chunk estimates.
Words per page	Approximate word count per page (dense text is often 300–400). Affects total tokens and chunk count.
Image density	How much of the document is images vs text. Image-heavy PDFs have fewer text tokens per page.