Cost = (input tokens ÷ 1M × input price) + (output tokens ÷ 1M × output price). Prices come from our daily-verified model data. Batch and cached-input discounts are not applied — they only ever make these numbers smaller.
“Needs chunking” means the model's context window can't hold this job in a single pass, so the real cost is higher than the figure shown and quality usually suffers.
Should you just pick the cheapest?
This is an input-heavy job, so the input price per million tokens decides almost the whole bill — the summary you get back is rounding error. Check the context window before the price: a model that can't hold 65,000 tokens has to be fed in chunks, which costs more and summarises worse.
How much does it cost to summarize a 100-page PDF with AI?
$0.0036 on GPT-5 Nano, the cheapest capable option, rising to $0.690 on GPT-6 Astra at the top end. GPT-5 Nano is the value pick at $0.0036 per run. The job is priced at 65,000 input and 800 output tokens — see the working below.
How did you work out the token count for this task?
100 pages of dense prose at ~500 words per page = ~50,000 words ≈ 65,000 input tokens. The summary back is ~600 words ≈ 800 output tokens.
What does this cost at 100 PDFs a month?
$0.357 a month on GPT-5 Nano, $0.357 on GPT-5 Nano, and $69.00 on GPT-6 Astra. Batch APIs typically halve these figures for work that can wait, and prompt caching cuts the input side further when the same context is reused.
Is the cheapest model the right choice for this task?
This is an input-heavy job, so the input price per million tokens decides almost the whole bill — the summary you get back is rounding error. Check the context window before the price: a model that can't hold 65,000 tokens has to be fed in chunks, which costs more and summarises worse.
Are these prices current?
Yes. Every figure on this page is computed from our model pricing data, which is checked daily against each provider's official pricing page. When a provider changes a price, these numbers change with it.