The cost of summarizing documents or transcripts at scale.
Because summaries are short and the task is easy, a small model does the job at a fraction of the cost. Trim the input too. Compare models on the AI cost calculator.
The AI Summarization Cost Calculator estimates your monthly bill for running documents through a large language model to produce summaries. It multiplies your documents per month by the per-document token counts, then applies each model's rates: the input tokens per document are priced at your input $/1M rate to cover reading each source, while the summary tokens per document are priced at your output $/1M rate to cover the generated text. Because input and output are billed separately, and long source documents usually dwarf short summaries, the reading side typically drives most of the total.
The key trade-off to watch is document length versus summary length. Output tokens often cost several times more per million than input tokens, so a longer, more detailed summary can raise your bill faster than the raw word count suggests. Before committing to a model, plug in realistic token counts and compare rates side by side: a cheaper input price means little if you feed in large documents at high volume, and a low output price matters most when you need lengthy summaries. Adjusting either token field shows exactly where your spending concentrates.
Cost per document = input tokens Γ input price + summary tokens Γ output price. Summaries are short, so the input (the document you feed in) dominates β a smaller model handles most summarization well and far cheaper.
Usually not. Summarization is one of the tasks where small models perform close to frontier ones. Route it to a cheap model and save most of the cost.