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LLM Token Chunker — Split Text for RAG & Context Windows

Paste long text — a document, transcript or codebase file — and split it into chunks with configurable overlap, sized for a specific model's context window or a RAG pipeline's embedding step. Each chunk shows its real token count (not a word-count estimate) and the approximate API cost across common model pricing.

How to use LLM Token Chunker

1. Paste your text.
2. Set target chunk size and overlap.
3. Review the chunks with per-chunk token counts.
4. Copy chunks individually or export the full set.

Frequently asked questions

Why does chunk overlap matter?

Without overlap, a fact split across a chunk boundary loses context on both sides — a small overlap (10-20% of chunk size) keeps nearby context intact for both chunks.

Are the token counts exact?

They use the same tokenization approach the target model family uses, so they are accurate for planning — not a rough word-count-divided-by-4 estimate.

What chunk size should I use for RAG?

It depends on your embedding model and retrieval granularity — smaller chunks (200-500 tokens) give more precise retrieval, larger chunks (800-1500) preserve more context per hit.

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Also searched for: token chunker for rag · split text for llm context window · count tokens for gpt pricing

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