Chunk Size Calculator

A chunk size calculator helps you determine the optimal number of tokens to include in each document segment (chunk) when building a Retrieval-Augmented Generation (RAG) pipeline. In RAG, documents are split into chunks that are embedded into vectors and stored in a vector database. When a user query arrives, the most relevant chunks are retrieved and injected into the LLM's context window as grounding information. Chunk size is one of the most impactful parameters in RAG system design — too small and chunks lose context; too large and retrieval precision drops, embedding costs rise, and LLM context windows fill up faster. Our chunk size calculator online models all of these variables together so you can find the right balance before writing any code.

1. Configure Your Corpus: Enter the average token count per document and the total number of documents. The calculator uses these to project total chunk counts and one-time embedding indexing costs across your chosen embedding model.
2. Set Chunk Size and Overlap: Choose your target chunk size in tokens and an overlap percentage. Overlap prevents important context from being severed at chunk boundaries — 10–20% is the standard range for prose, while code benefits from lower overlap near natural function boundaries.
3. Configure Models and Context Budget: Select your embedding model and LLM. Then set your system prompt size, answer reserve, and number of chunks to retrieve per query. The calculator shows whether your configuration fits within the LLM context window and how much of it is consumed by each component.
4. Review Quality Scores and Comparisons: The configuration quality score grades your chunk size and overlap settings. The comparison table shows how 7 standard chunk sizes perform across chunk count, embedding cost, context fit, and retrieval quality for your specific corpus and models.

• Small chunks (64–256 tokens): Higher retrieval precision — each chunk covers a narrower topic so semantic search matches more specifically. However, individual chunks may lack enough context to be self-explanatory, which can reduce generation quality. Embedding costs are also higher because more chunks are generated.
• Medium chunks (256–512 tokens): The sweet spot for most prose RAG pipelines. Chunks are large enough to carry full ideas and small enough for precise retrieval. Most embedding models are trained on sequences in this range, so semantic similarity works most reliably here.
• Large chunks (512–2048 tokens): Richer context per chunk but lower retrieval precision. Useful for documents where broad thematic coverage matters more than pinpoint accuracy — such as legal contracts, technical manuals, or scientific papers. Fewer chunks reduce embedding cost but fill LLM context faster.
• Overlap percentage: Overlap ensures that sentences crossing a chunk boundary are captured in both adjacent chunks. Too little overlap (0–5%) risks losing context at boundaries. Too much overlap (30–50%) bloats your vector store and increases redundant storage and retrieval costs. The 10–20% range is optimal for most use cases.

The chunk size calculator runs entirely client-side in your web browser. No document content, corpus configuration, model selections, or cost estimates are ever transmitted to a server. All calculations happen locally on your device using JavaScript, which means your pipeline design stays completely private. The tool is 100% free to use with no account, subscription, or installation required.