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?
Sending raw rows is the expensive way to do this and it is what most people try first. If the same sheet gets queried repeatedly, having the model write code that runs over the file costs a few hundred tokens instead of ninety thousand — and it does arithmetic correctly.
How much does it cost to analyze a spreadsheet with AI?
$0.0050 on GPT-5 Nano, the cheapest capable option, rising to $0.960 on Claude Fable 5 at the top end. DeepSeek V4-Flash is the value pick at $0.013 per run. The job is priced at 90,000 input and 1,200 output tokens — see the working below.
How did you work out the token count for this task?
10,000 rows across ten short columns ≈ 9 tokens per row ≈ 90,000 input tokens. A written analysis with findings ≈ 900 words ≈ 1,200 output tokens.
What does this cost at 20 analyses a month?
$0.100 a month on GPT-5 Nano, $0.259 on DeepSeek V4-Flash, and $19.20 on Claude Fable 5. 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?
Sending raw rows is the expensive way to do this and it is what most people try first. If the same sheet gets queried repeatedly, having the model write code that runs over the file costs a few hundred tokens instead of ninety thousand — and it does arithmetic correctly.
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.