Efficiently decarbonizing fossil-based energy systems requires extensive electrification of end-users’ heating and mobility sectors, alongside the provision of demand-side flexibility. Aggregators can harness this flexibility to enable cost-efficient supply in response to market incentives. While numerous studies address flexibility in energy systems, the underlying models conceptualize it in markedly different ways. This study, therefore, develops a meta-analytical modeling framework, derived from a systematic literature review, that couples market optimization with physics-based dynamic simulation to examine how varying the modeling depth and aggregation level of flexibility components, specifically heat pumps and battery electric vehicles, affects optimization results. Findings show that greater modeling depth yields more realistic optimization outcomes but at a higher computational cost. Endogenous heat pump coefficient-of-performance and storage modeling dampens price-arbitrage incentives and apparent flexibility, underscoring the need for detailed physical representation. Taking into account partial load behavior of electric cars avoids energy underprovisioning due to reduced efficiency. Aggregation level has only a minor influence when appropriate dispatch models are used. Transparent reporting of model depth, aggregation choices, and validation practices is thus crucial to ensure robust insights for utilities and policymakers.