Cost to Summarize 100 Documents with LLM APIs

Each document summarization requires ~3,000 input tokens (the document content, assuming ~2,250 words or ~6 pages) and ~200 output tokens (the summary). Longer documents will proportionally increase input costs. Models with larger context windows can handle longer documents in a single request.

Formula: cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × quantity / 1,000,000

All prices are per million tokens, sourced directly from official provider pricing pages. No fabricated numbers — every model in the table links to its detail page, which cites the official pricing source.

How much does it cost to summarize 100 documents with an LLM?

It depends on the model you choose. Summarization is input-heavy (~3,000 input / ~200 output tokens per document), so cost tracks input price closely. The calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing — compare the exact cost for all models in the table above instead of a band that rots.

Which LLM is best for document summarization?

For pure cost, the models at the top of the cost table above give the lowest per-document price. For summary quality, higher-tier models generally produce more faithful summaries. The calculator shows exact cost per model so you can trade cost against quality with real numbers.

How are document summarization token costs calculated?

Each document uses ~3,000 input tokens (document text) and ~200 output tokens (summary). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_documents. Prices are per million tokens, verified from official provider pages.