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RAG

Token & Chunk Estimator

Paste any content to estimate tokens, how many chunks it becomes, and what embedding it will cost.

Est. tokens
0
Words
0
Characters
0
Chunks (~500 tok)
0
How it chunks

~500 tokens each with 50-token overlap.

Paste content to see its chunks.
One-time embedding cost
text-embedding-3-small
$0.0000

What this estimator calculates

Paste any content — a page, a doc, an article — and this tool estimates the three numbers that decide what it costs to train a RAG assistant on it: tokens, chunks, and the embedding cost.

It mirrors a real pipeline: ~4 characters per token, ~500-token chunks with a 50-token overlap, and embedding priced like text-embedding-3-small at $0.02 per million tokens. The result is an order-of-magnitude estimate, not an invoice — but it's accurate enough to plan a build with no surprises.

Why tokens and chunks decide answer quality

Chunking is the unglamorous step that makes or breaks retrieval. Chunking 101 explains how splitting your content well keeps answers precise, and what a RAG chatbot is shows where embeddings fit in the retrieve-then-generate flow. Prefer to skip the math? Start free and let the assistant chunk, embed, and store your content for you.

Token & chunk estimator FAQ

What is a token?

A token is the unit language models read and bill in — roughly ¾ of a word in English, or about 4 characters. Models and embedding APIs price per token, so estimating tokens is how you estimate cost. This tool uses the same ~4-characters-per-token heuristic the indexer uses.

What is a chunk in RAG?

A chunk is a small passage your content is split into before it's embedded and stored. This estimator assumes ~500-token chunks with a 50-token overlap — a common sweet spot that keeps ideas intact without embedding whole pages as one blurry vector. The chunk count is roughly your token total divided by the step size.

How much does embedding content cost?

Embedding is cheap. The estimate here uses text-embedding-3-small at about $0.02 per million tokens, so even a large site usually costs a few cents to embed once. Answering questions later uses a separate chat model, priced separately.

Why do chunk size and overlap matter?

Chunks that are too big blur multiple topics into one vector and retrieve imprecisely; chunks that are too small lose the context needed to be useful. A modest overlap stops ideas from being cut mid-thought at chunk boundaries. Chunking 101 covers the trade-offs in depth.

Is this estimator free?

Yes. Paste any text and it estimates tokens, chunks, and embedding cost instantly in your browser — no sign-up and nothing leaves the page.

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