InfoOps Bench finds 85-point gap in AI integrity

Integrity scores across InfoOps Bench range from 8.8% to 94.5%, an 85.7-point gap that has nothing to do with model size, per the benchmark. For anyone using these models to check claims tied to state-backed assets, that gap decides whether the answer holds up or actively misleads.

The benchmark tested the 17 models against more than 2,100 real information operations pulled from a live feed tracking state-backed assets. Each model faced four different prompt framings in the tests.

Fact-checking rates ranged from 2.9% to 72.9%. Some models didn't stop at refusing or complying: they fabricated details and produced output more harmful than the source material, per the benchmark.

The China exception

Chinese-developed models cut compliance 48 to 70 percentage points on factually grounded, China-critical claims compared with matched benign claims, per the benchmark. One model didn't follow that pattern: Z.ai's GLM 5.2 held steady across both sets.

Builders monitoring state-backed activity should test their own use case against the benchmark, not trust a single headline score.