Key Points: • CMIP6 models can capture the timing of annual cycle (particularly in February) and spatial patterns of SIT resembling the observations. • Compared to sea-ice area, CMIP6 models exhibit larger negative biases in thickness/volume, with a higher degree of variation among models. • Seasonal variations in sea-ice show positive (negative) relationships between sea ice area and thickness during September (February). Abstract This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes. Plain Language Summary In this study, we investigated sea-ice thickness and volume in the Southern Ocean using data from 26 different climate models and observation datasets. Our findings show that the models generally capture the seasonal cycle and spatial patterns of sea-ice thickness well, with the highest average thickness occurring in February. We also found that the models tend to perform better in simulating sea-ice area compared to thickness. Furthermore, simulated sea-ice area and thickness tend to behave differently during different seasons-positively (negatively) covarying in September (February). The models that performed well in simulating sea-ice area faced challenges in accurately representing thickness and volume. This raises the question regarding the overall performance of such models or, more definitively, whether it's reliable to evaluate model accuracy or performance based solely on sea-ice area. Nevertheless, sea-ice thickness simulations in CMIP6 can offer a basis for initial analyses of absolute sea-ice changes in the Southern Ocean, despite the need for more reliable observational thickness.