Cost to Summarize 100 Documents with LLM APIs
Calculate the real cost of using LLM APIs to summarize documents. Compare all models for processing 100 documents with verified per-million-token pricing.
⚡ Your Workload
📊 Cost Summary
Cost per documents across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | Cost for 100 documents |
|---|
How this calculator works
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 and verified by our automated scraper pipeline that runs 3× daily. No fabricated numbers — every price links to its source.
Frequently asked questions
How much does it cost to summarize 100 documents with an LLM?
Using a budget model, summarizing 100 documents (~300 pages total) costs under $1. Mid-tier models like GPT-4.1 Mini cost around $5-10. Frontier models like Claude Opus 4.8 or GPT-4.1 can cost $30-60 for the same workload.
Which LLM is best for document summarization?
For pure cost efficiency, DeepSeek V3 and Gemini Flash offer the lowest per-document cost. For quality, Claude and GPT-4.1 produce more accurate summaries. The best value is often a mid-tier model like Gemini 3.1 Flash or GPT-4.1 Mini, which balance quality and cost.
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.