When Math Becomes Physics, Can LLMs Keep Up?

Can a language model solve a physics problem as reliably as the mathematics underlying it? We introduce Math2Physics, a benchmark of 216 mathematics–physics pairs from six collections, linked by shared central derivations. An expert-guided conversion procedure expresses mathematical constraints through physical laws, observables, and measurements, producing paired tasks that probe the recovery of mathematical structure from physical descriptions. Expert-reviewed annotations characterize physical domain, authenticity, and the added interpretation steps. Across three models, eight reasoning settings, and 64 attempts per question, mathematics accuracy is 77.03% and physics accuracy is 71.58% on the common 212-pair panel: a 5.45 percentage-point gap (95% paired-bootstrap interval $[3.77,7.14]$). The disadvantage recurs on the same problems across models, and greater reasoning effort does not consistently close it. Yet oracle coverage reaches 93.40% for physics at 64 attempts, approaching mathematics at 94.00%. This contrast reveals a gap between finding a correct physics solution among many attempts and producing one reliably in a single attempt. We further use the paired response banks to compare mathematics and physics preference training on held-out physics problems. Math2Physics provides a common foundation for studying scientific reasoning across formulations, reasoning settings, and training subjects.