There is an acknowledged need for the design and implementation of physical phantoms appropriate for the experimental validation of model-based dose calculation algorithms (MBDCA) introduced recently in 192Ir brachytherapy treatment planning systems (TPS), and this work investigates whether it can be met. A PMMA phantom was prepared to accommodate material inhomogeneities (air and Teflon), four plastic brachytherapy catheters, as well as 84 LiF TLD dosimeters (MTS-100M 1 × 1 × 1 mm3 microcubes), two radiochromic films (Gafchromic EBT3) and a plastic 3D dosimeter (PRESAGE). An irradiation plan consisting of 53 source dwell positions was prepared on phantom CT images using a commercially available TPS and taking into account the calibration dose range of each detector. Irradiation was performed using an 192Ir high dose rate (HDR) source. Dose to medium in medium, [Formula: see text], was calculated using the MBDCA option of the same TPS as well as Monte Carlo (MC) simulation with the MCNP code and a benchmarked methodology. Measured and calculated dose distributions were spatially registered and compared. The total standard (k = 1) spatial uncertainties for TLD, film and PRESAGE were: 0.71, 1.58 and 2.55 mm. Corresponding percentage total dosimetric uncertainties were: 5.4-6.4, 2.5-6.4 and 4.85, owing mainly to the absorbed dose sensitivity correction and the relative energy dependence correction (position dependent) for TLD, the film sensitivity calibration (dose dependent) and the dependencies of PRESAGE sensitivity. Results imply a LiF over-response due to a relative intrinsic energy dependence between 192Ir and megavoltage calibration energies, and a dose rate dependence of PRESAGE sensitivity at low dose rates (<1 Gy min-1). Calculations were experimentally validated within uncertainties except for MBDCA results for points in the phantom periphery and dose levels <20%. Experimental MBDCA validation is laborious, yet feasible. Further work is required for the full characterization of dosimeter response for 192Ir and the reduction of experimental uncertainties.