For labs
Measure your model where it actually breaks
You get the score and everything behind it: the bundle, the image digests, every trial that ran, and the verdict at each gate. Not a CSV of prompts and a number you have to take on trust.
What arrives with a dataset
- Scored in containers
- Reward is written by a verifier inside the sandbox. There is no model judging another model anywhere in the scoring path.
- Reproducible across versions
- Agent and verifier images are built rootless and pinned by sha256, so re-running a score next quarter runs the same bytes.
- Hard by measurement
- Every task carries the pass rate that admitted it, measured across 120 trials on the model matrix rather than asserted by its author.
- Seventeen capabilities
- Each category has its own artifact contract, base image and resource ceiling, so a result is attributable to a capability.
Benchmark your model
Tell us the capability you are trying to measure. If it is already saturated we will say so rather than sell you a thousand more tasks that measure it.