Over the last decade, performance-based financing (PBF) has gained momentum as a health financing innovation, which combines linking healthcare payments to performance with increased provider autonomy and supervision. 1 2 The combination of these elements is expected to boost supply-side efforts towards increasing quantity and quality of service provision, triggering a demand-side response towards improved service utilisation.1 3 4 A recent paper by Paul et al has critically questioned the widespread introduction of PBF in light of the limited available evidence on its effectiveness.5 The response to this paper has been varied, with authors advancing arguments for and against PBF. Some African PBF implementers have claimed that PBF is an evolving strategy with potential benefits on health systems despite its existing challenges. 6 Others have drawn attention to the unintended consequences of PBF7 or to the need to assess the economic value of such an approach.8 Beyond their diverse arguments, however, most authors have concurred with Paul et al5 on the limited scope of currently available evidence and have postulated the need to better assess how PBF works under different contextual constraints within and across settings.9 10 Our commentary positions itself against this background, acknowledges the limited scope of current evidence on PBF, and explicitly argues in favour of devoting more effort to unravel heterogeneity across and within settings. Our argument is based on the recognition that by virtue of how impact evaluations are designed, the focus has been on the average effect, which masks important heterogeneity across settings, providers and users.11-13 To date, only a handful of studies have assessed heterogeneity of PBF effects across population subgroups4 14 15 or across health providers.16-18 Similarly, little attention has been devoted to understanding which factors can explain heterogeneity in the response to PBF or why PBF stimulates changes in some instances, but not in others.3 4 10 In light of the above, we call for more systematic analyses of heterogeneity, defined in relation to both the need to report differential effects and the need to understand what drives or explains such differential effects within and across settings. We first define and outline potential sources of heterogeneity and then offer initial guidance on how to measure and understand heterogeneity.