Struggle Bench Benchmark Targets AI Hardest Open Problems

A new benchmark, "Struggle Bench," has been publicly released, containing a curated set of problem instances designed to exceed the difficulty of current frontier model evaluation suites.

Standard benchmarks are nearing saturation, producing compressed performance curves that obscure genuine capability gains. Struggle Bench provides a higher-resolution signal for model differentiation, specifically targeting reasoning bottlenecks, long-horizon planning, and novel task adaptation. For operators, this changes regression testing: passing Struggle Bench subsets should become a gating criterion for model upgrades in production workflows that involve complex, multi-step autonomy. It also renders obsolete the practice of relying solely on prior leaderboards for vendor selection. Second-order, expect model providers to overfit training data to publicly leaked struggle instances; operational value lies in private, held-out variants of this benchmark for internal eval pipelines. Builders should integrate Struggle Bench as a pre-deployment stress test, but budget for increased inference compute costs, as these tasks require extended chain-of-thought and higher sampling temperatures to achieve meaningful pass rates.