Data-science code efficiency does not track correctness.

DSEffi-Bench covers 1,000 instances across 10+ libraries with stress-testing harnesses and human-validated references, evaluated on 16 models (arXiv 2608.30248). GPT-5.4 leads correctness at 66.9% Pass but its 71.7% efficiency score barely beats GPT-5.4-mini's 71.6% despite solving 47 more tasks, while Kimi-K2.5 is lowest in correctness among frontier models at 40.2% and posts the best efficiency at 73.6%. A human-annotated taxonomy puts 79.1% of efficiency deficits on domain-specific causes rather than algorithmic complexity, and library-conditioned routing approaches Claude-Opus-4.6 Best@3 efficiency at 13x lower cost.