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?
Extraction is graded pass/fail on every field, so a model that is right 95% of the time still hands you 50 bad records per thousand. Price the human review time alongside the token cost — it usually dwarfs it, and a better model is the cheaper way to reduce it.
How much does it cost to extract data from invoices with AI?
$0.0000 on Mistral: Mistral Nemo, the cheapest capable option, rising to $0.013 on Claude Fable 5 at the top end. Llama 4 Scout is the value pick at $0.0002 per run. The job is priced at 1,200 input and 300 output tokens — see the working below.
How did you work out the token count for this task?
A single-page OCR'd invoice with line items ≈ 1,200 input tokens. A structured JSON record of the fields ≈ 300 output tokens.
What does this cost at 1,000 invoices a month?
$0.033 a month on Mistral: Mistral Nemo, $0.210 on Llama 4 Scout, and $13.50 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?
Extraction is graded pass/fail on every field, so a model that is right 95% of the time still hands you 50 bad records per thousand. Price the human review time alongside the token cost — it usually dwarfs it, and a better model is the cheaper way to reduce it.
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.