Summary: Decomposition of plant litter is a key process in the global carbon cycle, and its realistic representation is essential for accurate prediction of ecosystem carbon and nutrient dynamics. Traditionally, the exponential decay model, which assumes a constant relative decomposition rate through time, has been widely used for both the analysis of empirical data and large-scale modelling. While this model may be adequate in some contexts, there are also many empirical observations for which it does not fit the data well. Using a number of previously published leaf and wood decomposition data sets, comprising a diverse selection of litters, we compared the performance of five simple decay models varying in complexity and functional form. To accomplish this, we used a combination of model selection criteria and out-of-sample extrapolation analyses. Model selection criteria favoured an optimal balance between parsimony and flexibility, and a rarely used model built on the Weibull distribution proved to have sufficient flexibility to fit the full range of decomposition trajectories including litters that show an initial lag phase. Using the Weibull fits, we further showed that the litter N content affects the shape of the decomposition trajectory, a novel biological result. Out-of-sample extrapolation showed biased predictions for all models and estimates of steady-state stocks were often highly sensitive to model choice. As the field moves beyond the single-pool exponential framework, it is useful to confront alternate models with data from a wide array of litter types. This analysis demonstrates that model flexibility is important for representing the wide array of possible decomposition dynamics encountered in nature, with lag phase dynamics more widespread than previously thought. Out-of-sample extrapolation and derived parameters that rely on it remain problematic with all tested models. Progress in understanding the drivers and consequences of litter decomposition will come from combining empirical approaches with more focused process-based modelling that allow for the assessment of alternative model assumptions. © 2013 British Ecological Society.