Get renewal dates and payment terms out of all your contracts into one table
A batch job that reads each contract end-to-end and fills one table of renewal dates, notice periods and payment terms, every cell linked to its source.
- How it works
- A one-off, then occasional, batch job: the long-context large model reads each supplier, lease, SaaS and insurance contract end-to-end and fills a table — counterparty, start date, term, auto-renewal, notice period and deadline, payment terms, indexation clauses. The output is a renewal calendar, so auto-renewals stop catching you by surprise. Each cell links back to the passage it came from so it can be spot-checked.
- Data you need
- The contracts as digital text. Contracts that exist only as signed scans must go through a separate OCR step first — no vision model is served — and OCR errors will carry through into the table. Amendments and side letters should be collected alongside the contracts they modify.
- What to expect
- Notice-period arithmetic ('3 months before the end of the then-current term') is where models slip — extract the clause text and compute the actual calendar date deterministically or by hand. Amendments in separate files are easy to miss; feed them together with the main contract. Extraction from dense legal phrasing is where the model is most likely to be merely adequate, in any language. A wrong date in a renewal calendar is worse than no calendar, which is why the spot-check exists.
- Where people stay involved
- Someone spot-checks the extracted dates against the source passages, and any deadline you'd act on — terminating a contract, disputing an indexation — gets verified by reading the clause in full first.
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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Sort expenses into your bookkeeping categories
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