Calibrate any model.
Watch it happen.
Paste a key, pick a model, and watch Aperture’s honesty layer fit to it — live, in this tab. A real-vs-fake battery separates, a null test tries to kill the signal, and you walk away with a certificate.
Ten minutes. Your key, your tab, your certificate.
Aperture asks your model 168 questions — half about real entities, half about fabricated ones it can’t possibly know — and reads which it can tell apart. Nothing but the finished certificate ever reaches us, and only if you choose to register it. On a top model? It may already be in the registry below — certified first-party, ready to use.
?demo=1 to run your own model.openrouter.ai, none to us. Use a scoped or low-limit key if you like.Models that know what they don’t know.
Every certificate below is public. ✓ aperture-verified means we ran the calibration ourselves, first-party, on this exact battery; self-attested means a team ran it in their own browser. Click through for the full reading — the AUROC, the null test, what it caught — and the calibrated probe, ready to deploy.
Reading the wall: self-attested numbers are the registrant’s own claim, computed in their browser and not re-run by us. words N% certs come from hosted surfaces that expose no token logprobs — the same model self-hosted usually earns the full fingerprint. Thinking/reasoning models need the reasoning calibration mode; absent models were skipped rather than mis-measured. verified ‹date› chips are the Model Notary — a daily spot-check re-runs each model against its own certificate; DRIFT means the served alias no longer matches it. Certificates are battery-regime-bound — entity, citations, or medical. Full semantics in the docs.
Your model isn’t here? Calibrate it above in ten minutes — or, for a self-hosted model the browser can’t reach, download the CLI: the same battery, the same math, a certificate you can register from your own machine.
The same method, run in the open.
No black box. This is the exact calibration behind the deployed off-map reading — the difference is you get to watch every step, and the numbers land honestly, pass or fail.
It reads the words
For each fabricated entity, the refusal reader checks whether your model says, in plain language, that it has no record. Those are caught before any probe runs.
It reads the confidence
For the fakes your model answers anyway, a probe fits to the shape of its token-by-token confidence — the fingerprint that separates a grounded answer from an invented one.
It tries to disprove itself
Then it shuffles the real/fake labels and refits, over and over. If the signal survives where the labels are random, it’s real. If it doesn’t, the certificate says so.
Put the honesty layer in front of your model.
A calibrated probe drops straight into the Aperture client — every answer your model gives gets read on the map or off it, before it reaches a user. The certificate is the key to a /v1 endpoint.