The question of which contextual topic modelling algorithm performs best has become increasingly important as the field rapidly develops new approaches. However, existing evaluations typically focus on limited datasets and metrics, often claiming superiority for novel algorithms. This study presents a comprehensive empirical evaluation of eleven contextual topic modelling algorithms across ten diverse datasets, five numbers of topics, and four performance metrics, resulting in 22,000 metric evaluations. Rather than identifying a single superior algorithm, our results reveal clear evidence of performance complementarity: different algorithms excel on different problem instances and under different evaluation criteria. Through aggregate performance analysis, pairwise dominance comparisons, and multi-objective Pareto frontier analysis, we demonstrate that algorithmic dominance varies significantly across problem instances. Most remarkably, in 84% of cases, all algorithms are Pareto optimal when considering all metrics simultaneously, indicating that each offers unique strengths that cannot be dominated by others. These findings challenge the common practice of claiming algorithmic superiority and suggest that algorithm selection should be guided by specific problem characteristics and performance priorities rather than blanket recommendations. Our work contributes to the growing recognition that performance complementarity is fundamental to computational problems, extending this concept to contextual topic modelling and providing a foundation for future algorithm selection research. Code used to conduct this study is provided ( https://github.com/AlgorithmicAmoeba/tm_framework ).