Chunk Size Calculator
Document size, chunk size and overlap in; chunk count, total tokens stored, the storage amplification and the embedding cost out.
usage object on any completion response — prompt_tokens and completion_tokens are what you are billed on, they cost nothing to read, and they settle the question for the model you are actually calling.57,104 tokens embedded in total — 1.14× the document, because each chunk repeats 64 tokens of the one before it.
- Document size
- 50,000 tokens
- Stride (chunk − overlap)
- 448 tokens
- Chunks
- 112
- Tokens stored across all chunks
- 57,104
- Duplicated by overlap
- 7,104 tokens
- Storage amplification
- 1.14×
- Size of the final chunk
- 272 tokens
- Overlap as a share of each chunk
- 12.5%
- One-off embedding cost
- $0.0057
- The same document without any overlap
- 98 chunks · $0.0050
What the overlap is buying, and what it costs
Overlap exists because a fixed-size window cuts wherever the token count says to, not where the meaning ends. A definition split across the boundary is retrievable from neither half; repeating the last few dozen tokens of each chunk at the start of the next makes sure that any short span of text appears intact somewhere. That is the whole argument for it, and it is a good one.
The price is visible above as the amplification factor. At a 512-token chunk with 64 of overlap you store about 14% more tokens than the document contains; at 128 of overlap on the same chunk size it is about 33%. You pay that premium three times — once to embed, once in vector storage, and once again on every re-index when you change model or chunking strategy. Push overlap much past a quarter of the chunk size and you are mostly paying to store the same sentences repeatedly.
Chunk size itself is the more consequential dial and it is not a cost question. Small chunks retrieve precisely and lose context, so the model gets the right sentence without the paragraph that made it mean something. Large chunks carry context but dilute the embedding, so retrieval gets vaguer as the chunk covers more topics. There is no universal answer; there is only the answer for your corpus, which you find by fixing an evaluation set of real questions and trying three sizes against it. Use this calculator to know what each option costs before you run that test, not instead of running it.