Draft payment reminder letters that sound like you
Drafts overdue-invoice reminders in your tone and your customer's language, escalating from friendly nudge to final notice.
- How it works
- For each overdue invoice the model drafts a reminder matched to its stage — friendly nudge, firm reminder, final notice before collections — following your house examples, so a first reminder to a ten-year customer doesn't read like a debt collector wrote it. The concrete facts (invoice number, amount, days overdue, payment link) are merged in from your export by the surrounding template, never written by the model. It can draft in your customer's language.
- Data you need
- An export of overdue invoices with customer name, amount, due date and reminder stage, plus two or three past reminder letters you were happy with as tone examples. A note per customer (key account, new, repeat late payer) sharpens the tone choice.
- What to expect
- Solid in the major EU languages; drafting in the smaller ones is weaker and worth a native-speaker check before it becomes routine. Numbers and dates must come from the template, not the model — asked to copy figures, a language model will occasionally alter one, and a reminder with a wrong amount is worse than no reminder. In some EU countries dunning stages carry legal meaning, so keep your existing legal wording fixed and let the model write only around it. If you send a handful of reminders a month, a plain template is simpler.
- Where people stay involved
- Someone reads and sends each letter, or at least reviews the batch — especially final notices and key accounts, where a wrong tone costs real relationships. Legal escalation wording comes from your lawyer, not the model.
Which model, and what it costs to run
Qwen3-32B
This job runs in bulk rather than to a waiting person, so size is not the constraint — we take the strongest independent score on structured output and tool calls that we may serve freely and that fits on a single card.
- Licence
- Apache-2.0
- Weights at 4-bit
- 18 GB
- Context
- 32K tokens
- Publisher
- Alibaba (Qwen Team)
What the hardware costs
One 48 GB card holds it
- Rent in the EU
- $1.60/hrScaleway, Paris (PAR2)
- Buy the card
- $7,569new, one-off
- Or rent it by the token
- $0.12 / $0.12per M in / out · Nebius AI Studio · EU
Hardware only, third-party prices from 2026-07. The figure excludes the KV cache, which grows with context length and how many people use it at once — sized properly in a conversation, not guessed here. Renting by the token is cheaper up front; why our customers still self-host is below.
Structured output and tool calls
Measured in a sandbox, on somebody else's functions. It tells you which models are capable of the shape of the job, not which one will survive contact with your API.
Independent measurement · UC Berkeley (Gorilla project) · board updated 2026-04-12
Reading a long document whole
No benchmark measures this
No current benchmark ranks today's open models here. HELMET showed that the popular test — finding a planted sentence — predicts nothing, and its own table has not been rerun on 2026 models.
So we do not show a chart here. We measure it on your own content, in the first week, and you see the result before anything ships.
Sticking to the document
Measured on public documents, by a model acting as judge. Read it beside the answer rate: the lowest hallucination rates on this board belong to models that simply decline more often.
Independent measurement · Vectara · board updated May 11, 2026
The API is cheaper per token. Here is why our customers don't use it.
We will not pretend otherwise: renting a model by the token from a serverless API costs less per million tokens than a card we run for you. We show that price on every use-case page. What it does not include is the part a shop with a customer database actually pays for.
- 01
Your data never leaves hardware you can point at
A serverless "we don't retain your data" is a clause in a contract. Running the model on a card in Amsterdam is a fact of architecture: your catalogue, tickets and customer records are never sent to a third party at all. For a GDPR audit, that is the difference between a promise and a floor plan.
- 02
The price cannot move without your say-so
A serverless rate card is somebody else's lever. The provider can raise the price, retire the model, or change the terms, and your cost moves with it. The same model on the same card costs the same next year — you own the number.
- 03
The model cannot be taken away
Hosted APIs deprecate models on their own schedule; the one you built on can be gone in a quarter. An open-weight model on your own hardware runs for as long as you keep the lights on. No vendor can end-of-life it out from under you.
And the price gap closes with volume: past a card you keep busy — very roughly four billion tokens a month — owning is cheaper outright, even before the three reasons above.
Getting a case like this one from a conversation to production takes about two months, and you can stop at the end of any phase.
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