Know which AI actually works on your workload.
A public benchmark tells you which model is good at a public benchmark. Give us two hundred to five hundred cases out of your production, and we will tell you which model and configuration completes your tasks, and what each one costs per task that actually finished.
The only benchmark that matters is yours.
Models are increasingly interchangeable on paper and are not interchangeable on your work. A model that leads a public leaderboard can fail the one document shape your pipeline depends on, and a model nobody ranks can hold quality at a third of the cost. Neither fact is discoverable from a rate card.
We measure it for you. We do not yet tell you what usually wins — that answer needs many comparable workloads behind it, and saying it early is how you get caught by the customer whose result disagrees.
Three numbers a scorer outside the payment path cannot produce.
We are the gateway the calls run through, so we pay for them. That is the whole difference: the cost in our report is what was charged, not a rate card multiplied by a token count.
What the run actually cost, not list price times tokens.
A scorer on the outside multiplies a rate card. We paid the invoice, including the negotiated and wholesale rates that make the same model cheaper in one place than another.
How many tries it took to get one result you could use.
A model that answers in one attempt and a model that answers in three look identical on a leaderboard and cost you three times as much.
Money spent on runs that produced nothing you kept.
Refusals, malformed output, timeouts, budget stops. This is where the headline finding of most engagements lives, and nobody outside settlement can compute it.
How it runs
- You give us real tasks
Two hundred to five hundred cases out of production — the work that actually runs, not a benchmark suite. No source access, no data access, no infrastructure changes.
- We define what success means
Written down before anything runs, in your terms: the six fields extracted, the test suite passing, the ticket resolved. A score without a stated criterion is an opinion.
- We run every candidate on it
Three to ten models and configurations against the same cases, through the gateway, at hard budget caps. Success is program-verifiable only: tests pass, exact match, correct end state. Never a model grading a model.
- You get numbers and a policy
Which configuration wins on your workload, what it costs per completed task, where the failures cluster — and a routing policy you can deploy or ignore.
What a finding looks like
Never a score out of a hundred. The output is a sentence you can act on: For your workload, Model D holds 99% of current quality at 43% of the cost.
| Configuration | Task success | 95% interval | Cost / completed task | Attempts |
|---|---|---|---|---|
| Configuration A | 96.4% | 94.1 – 98.1 | $0.038 | 1.08 |
| Configuration B | 95.9% | 93.4 – 97.7 | $0.017 | 1.11 |
| Configuration C | 89.4% | 86.0 – 92.1 | $0.009 | 1.62 |
| Configuration D | 96.1% | 93.8 – 97.9 | $0.012 | 1.14 |
A, B and D are indistinguishable on quality — their intervals overlap, so no winner is declared on that axis. C is separated and lower. What separates A from D is cost per completed task, and that gap is more than three to one.
What a model provider can and cannot buy
Analysis of their own results: where they win, where they lose, and what it costs against the alternatives on the same workload.
Presence. Inclusion in an evaluation is free and decided by us, and no payment moves a result, changes a task selection, or buys a place in a comparison.
Send us one workflow that already runs.
Not a demo call. Two hundred to five hundred real cases and a way to tell whether each one succeeded. You keep your data, your source and your infrastructure — we need the tasks and the criterion, nothing else.