Ask questions across a tender pack instead of reading 200 pages
Load a tender's document pack and ask plain questions — deadlines, certifications, penalty clauses — with answers that cite the page they came from.
- How it works
- You load the tender pack — invitation, specs, contract terms, annexes — and ask plain questions: submission deadline, required certifications, insurance minimums, evaluation weighting, penalty clauses. Retrieval finds the relevant passages; the large model answers and points at the page each answer came from. You read the handful of passages that decide bid or no-bid instead of the whole pack.
- Data you need
- The tender documents with selectable text. Nothing of yours is needed — this works on day one for any tender, which makes it one of the easiest cases to start with. Public-sector packs often include scanned or image-only PDFs, which must be OCR'd before loading, as no vision model is served.
- What to expect
- Treat it as a fast index into the pack, not the authority on it. The model can state a wrong figure or invent a requirement with full confidence, and 'no such clause found' does not prove the clause is absent — mandatory-requirement checklists still need a human pass over the relevant sections. Contradictions between documents may come back as a single confident answer rather than a flagged conflict, and legal interpretation of clauses remains a lawyer's job.
- Where people stay involved
- Every answer that affects the bid decision or the submission is verified against the cited passage — one click, since the citation is attached. Deadlines and mandatory requirements especially: in tendering, a wrong 'yes we qualify' is a disqualification, not a typo.
Which model, and what it costs to run
Qwen3-8B
This job answers while someone waits, so latency comes first: we hold it to a single small card and, within that, take the best independent score on sticking to the document.
- Licence
- Apache-2.0
- Weights at 4-bit
- 5 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
- $0.04 / $0.04per M in / out · DeepInfra
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.
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
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.
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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Next
Tell us what your team does by hand
Describe the process that takes the most time. We will say plainly whether a model is the right tool for it.