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We compute the Bayesian evidence for models considered in the main analysis of Planck cosmic microwave background data. By utilizing carefully defined nearest-neighbor distances in parameter space, we reuse the Monte Carlo Markov chains already produced for parameter inference to compute Bayes factors for many different model-data set combinations. The standard 6-parameter flat cold dark matter model with a cosmological constant () is favored over all other models considered, with curvature being mildly favored only when cosmic microwave background lensing is not included. Many alternative models are strongly disfavored by the data, including primordial correlated isocurvature models (), nonzero scalar-to-tensor ratio (), running of the spectral index (), curvature (), nonstandard numbers of neutrinos (), nonstandard neutrino masses (), nonstandard lensing …