The application of biomarkers for "recent" infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the "recent" state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the "recent" state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while "random effects" describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing "recent"/'non-recent" classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects" (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.