# RAG Chunking Calculator

- **URL:** https://codeasystem.com/calculators/developer/rag-chunking-calculator/
- **Category:** developer
- **Description:** Work out how many chunks an overlapping-window chunking strategy produces, plus stored-token overhead and embedding cost.
- **Primary output:** Chunks produced: 23 chunks

## Inputs
- Document size (name: `documentTokens`, type: number, example: 100000)
- Chunk size (name: `chunkSize`, type: number, example: 512)
- Overlap (name: `overlapTokens`, type: number, example: 64)
- Embedding price (name: `embeddingPricePerMtok`, type: number, prefix: $, example: 0.02)

## Outputs
- Chunks produced: 23 chunks
- Stride (new tokens per chunk): 448 tokens
- Total stored tokens (incl. duplication): 11,776
- Duplication overhead: 17.8%
- Embedding cost @ $0.02/Mtok: $0.000236

## Formula / methodology
```
chunks = ⌈(doc − overlap) ÷ (chunk − overlap)⌉
stride = chunk − overlap   stored = chunks × chunk
```

Overlapping windows advance by the stride (chunk minus overlap) each step, so a document of D tokens with chunk C and overlap O yields ceil((D − O) ÷ (C − O)) chunks. Every chunk stores its full size including duplicated context, and multiplying stored tokens by your embedding price prices the indexing run.

## Assumptions & limitations
- Sliding fixed-size windows; semantic/sentence-aware splitters differ.
- Overlap must be strictly smaller than chunk size.
- Embeddings billed per token at your entered rate.

## How to use
1. **Measure the corpus in tokens**; Use our token counter on representative documents rather than word counts.
2. **Choose chunk and overlap**; 512/64 is a common starting point; retrieval quality should tune it, not habit.
3. **Price the embedding run**; Stored tokens include duplication; the honest figure for API budgeting.

## Example
A 10,000-token document split into 512-token chunks with 64-token overlap yields 23 chunks (ceil((10000 − 64) / (512 − 64))); about $0.0002 of embeddings at $0.02/Mtok.

Result for these inputs:

```
Chunks produced: 23 chunks
```

## About this calculator
### Why overlap exists

Fixed cuts slice sentences and ideas in half. Overlap gives each chunk a running start into its neighbor’s territory, so a fact straddling a boundary lives fully inside at least one embedded chunk. The cost is duplicate storage; visible here as an overhead percentage.

### Bigger chunks vs better retrieval

Larger chunks preserve more context per hit but blur relevance signals and burn query-time context. Smaller chunks retrieve precisely but fragment reasoning. Practitioners commonly sweep 256–1024 tokens against real queries instead of trusting defaults.

## FAQs
### Why was my equal overlap rejected?

Overlap ≥ chunk size means zero stride; the window never advances and chunking never terminates. Progress requires strict overlap < chunk.

### Does chunking affect query cost too?

Yes; retrieved chunks enter the prompt. More, smaller chunks raise per-query input tokens even though they sharpen retrieval.

### Is fixed-size splitting optimal?

Rarely perfect. Sentence- and section-aware splitters respect structure; this calculator still approximates their output well for planning.

## Related calculators
- [Embedding Storage Calculator](https://codeasystem.com/calculators/developer/embedding-storage-calculator/)
- [Context Window Calculator](https://codeasystem.com/calculators/developer/context-window-calculator/)
- [LLM API Cost Calculator](https://codeasystem.com/calculators/developer/llm-api-cost-calculator/)

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Last updated: 2026-08-23 · Version: 1.0.0 · [HTML version](https://codeasystem.com/calculators/developer/rag-chunking-calculator/)