ModelCensusopen-source ai reliability harness

No vendor funds this, which rules out a great deal we could otherwise have said, and is the only reason the rest is worth reading.

on independence

Blog28 Jul 2026conceptpositioninggovernancereproducibility

Nobody pays us to say this

A five-minute check for whether any AI benchmark is safe to quote in a board paper — and what passing it cost us.

Every benchmark says it is independent. By the end of this you will have a five-minute check for whether one actually is — before its number goes into your board paper.

What you need

  • The benchmark's methods page or repository
  • The single number you want to quote
  • Five minutes

Stage 1 — was the panel fixed before the run?

Check

Look for a model roster committed, with a date, before any results existed.

Why: A roster that can change after results can quietly lose the model that embarrassed someone.

Stage 2 — can you read the scoring?

Check

The detector or grading code is public, or at minimum the judge prompt is.

Why: An unpublished scorer is an opinion with a leaderboard attached.

Stage 3 — does the number open?

Check

Click from the number you want to quote to the raw output that produced it.

Why: If you cannot, nobody you show it to can either.

Panel fixedbefore the runCases frozenversionedDetectors publicreadable codeEvery cell linksto its transcript
How ModelCensus answers the three checks. A finding that flatters a vendor and one that embarrasses it go through the identical path.

Stage 4 — who saw it before you did?

  • No vendor sees results before publication
  • No embargo, no sign-off
  • Model costs paid personally — no advertising, nothing for sale
  • Corrections open to anyone, published as dated errata
MARKETINGCHECK THE CHOICESTRUST BY REPUTATIONQUOTABLELaunch-day benchmarkOpen, vendor-sponsoredClosed leaderboardOpen and unfundedtrust usread the codeCan you inspect the methodPaid for by who is measuredindependentvendor-funded
Only the bottom-right survives a sceptical board member. Placement of the categories is judgement, not measurement.

What passing it cost us

A fixed public panel means I cannot quietly drop a model that produces an inconvenient result. And there is no private-instance mode: running this against your deployment would produce numbers nobody outside your company could check.

Both cost adoption. Both are the point.

The accelerator

If you only have thirty seconds, ask one question: can I click from this number to the output that produced it? Most benchmark numbers in most decks fail right there.

The last benchmark number that made it into one of your decks — could anyone have clicked through to the transcript?

Every figure here describes something measured and committed. See the measurements · read the method