The Behrens--Fisher problem concerns inference on the difference of two normal means when both variances are unknown and unequal. It is a classical example in which nuisance parameters prevent ordinary exact fixed-sample inference, and it has long served as a benchmark for the foundations of inference. We revisit it through the inferential model (IM) framework of Martin and Liu. After conditioning and regular marginalization, the exact association is two-dimensional, with one coordinate for the standardized mean contrast and one for the variance ratio. Their one-dimensional generalized marginal IM is then best understood as a cylindrical two-dimensional predictive random set: sharp in its mean-contrast projection, by Hsu's stochastic domination, and vacuous in the variance ratio. Our main result is a precise validity-first optimality: among prior-free procedures that retain exact, uniform, finite-sample validity, the IM interval is the shortest. We prove minimaxity and admissibility in the cylindrical class and, by a projection argument, extend this to rectangular and general two-dimensional predictive random sets. A companion tradeoff principle shows that any adaptive procedure can only redistribute interval width across variance-ratio regimes, never shorten it uniformly. A Monte Carlo study bears this out: Welch and the bootstrap under-cover, whereas the conservative fiducial does not dominate the IM interval, being shorter only where the latter over-covers and longer where validity binds.