This study evaluates whether frozen tabular foundation models can function as probabilistic photometric-redshift estimators for quasars in the S-PLUS DR6 survey’s 12-band system. We benchmark TabPFN 2.5, RealTabPFN 2.5, and TabICL against eight task-specific baselines using metrics spanning density estimation and point prediction, with training sets from 500 to 121,626 quasars. TabPFN 2.5 performs best on most metrics except unweighted CDE loss, is particularly advantageous for small training sets and challenging regimes, and maintains calibration under covariate shift. SHAP analysis identifies WISE infrared bands as dominant predictors. TabPFN 2.5 is a strong default for probabilistic quasar photo-z estimation when data are limited or shift-robust calibration is essential.