In this article we introduce a new framework for counterparty risk model backtesting based on Bayesian methods. This provides a conceptually sound approach for analyzing model performance which is also straightforward to implement. We show that our methodology provides important advantages over a typical, classical, backtesting set-up. In particular, we find that the Bayesian approach outperforms the classical one in identifying whether a model is correctly specified which is the principal aim of any backtesting framework. The power of the methodology is due to its ability to test individual parameters and hence identify which aspects of a model are misspecified as well as the degree of misspecification. This greatly facilitates the impact assessment of model issues as well as their remediation.