A proprietary benchmarking platform that sits every frontier model through a versioned battery of exams across eight dimensions — and returns one statistically rigorous 2uring Score, on a bank the models have never seen.
Illustrative · live leaderboard at launch
Every frontier model is fluent, confident, and impossible to tell from a person. That question is closed. The one that decides your stack is still wide open: for this task, at this budget, which model is actually best — and by how much?
The public answers are broken. Leaderboards leak into training data. Single benchmarks flatten eight questions into one. Vendors grade their own homework. So teams pick models on a headline number or a Twitter thread and find out the cost in production.
The 2uring Test closes that gap.
A model that wins on reasoning can lose on cost. The 2uring Score rolls up every axis — and the breakdown shows exactly where each model earns it and where it does not.
Multi-step reasoning, math, and domain knowledge under conditions the model has never seen. The exams reward working through a problem, not pattern-matching a memorized answer.
How often the model is simply right — and, just as important, how often it declines to answer instead of confidently inventing one. Hallucination is scored as the failure it is.
Does the model do exactly what it was told — format, constraints, tone, and all — or drift toward what it would rather say? Precision on the contract, not vibes.
Real tasks with executable verifiers: write it, fix it, refactor it, make the tests pass. Graded on code that runs, not code that looks plausible.
Planning, tool use, and recovery across multi-step tasks. Can the model hold a goal, call the right tools in the right order, and get back on track when a step fails?
Retrieval, synthesis, and reasoning across very long inputs — the needle, and everything the needle depends on. Degradation as context grows is measured, not assumed away.
Behavior under adversarial prompts, jailbreak attempts, and edge cases. A model you deploy has to hold its footing when the input is hostile, not just when it is friendly.
Score per dollar and score per second. The best model on a leaderboard is often the wrong model for production — 2uring makes the trade explicit instead of hiding it.
Once a test set is on the internet, it is in the next model's training data. The score stops measuring capability and starts measuring contamination.
One number flattens eight different questions into a ranking that hides where a model actually wins and where it quietly loses.
The party selling the model chose the tests and the framing. Independent, held-out evaluation is the whole point.
Anecdotes do not have confidence intervals. A model-selection decision with real money on it needs evidence that reproduces.
The frontier moves monthly. A score from two releases ago is a historical artifact, not a basis for the decision in front of you.
Every model sits the same versioned exams across all eight dimensions, through a fixture-, Anthropic-, or OpenAI-compatible adapter. Adding a new model never touches the scoring engine.
Executable verifiers grade what can be graded automatically; blinded judge panels grade the rest without knowing which model produced which answer. No home-field advantage.
The statistical layer runs each exam repeatedly and reports bootstrap confidence intervals, so you know whether a gap between two models is signal or noise.
The 2uring Score rolls up into a plain-language verdict — better, cheaper, or both — mapped directly to the model-selection decision you actually have to make.
New models sit the battery on release and join the leaderboard on a short cycle. Your private evaluations re-run so your decision stays current as the frontier moves.
Cost, latency, and quality all matter and they pull in different directions. You need the trade quantified, not a headline benchmark from the vendor.
A newer model promises more. Before you re-plumb your stack, you want proof it is actually better on your workload — with a confidence interval attached.
You need an answer a CFO can read: which model, on which dimensions, at what cost per outcome — not a slide of accuracy percentages on synthetic tests.
You want independent, third-party validation on a contamination-controlled bank — a number buyers trust because you did not choose the test.
A decision, not a data dump. Every result ties back to the versioned battery that produced it.
Every 2uring Score runs on a proprietary held-out item bank with contamination controls, is graded by executable verifiers and blinded judge panels, and ships with bootstrap confidence intervals. The exams are versioned, so a score is always tied to the exact battery that produced it.
Most benchmarks give you a decimal and hope you do not ask how sure they are. We print the interval, because a model-selection decision deserves to know.
A proprietary held-out item bank in a separate private repo, with contamination controls that keep exams out of training data. The number measures the model, not its memory.
The eight dimensions come from people who ship agents in production and know which capabilities actually decide whether a deployment works. Not an academic leaderboard.
Bootstrap confidence intervals on every score, blinded judge panels, versioned batteries. We publish the uncertainty instead of hiding behind a single decimal.
Let's discuss how AI can create measurable advantage for your organization. No pitch decks — just a conversation.