We compute the Bayesian Evidence for models considered in the main analysis of Planck cosmic microwave background data .
By utilising carefully-defined nearest-neighbour distances in parameter space , we reuse the Monte Carlo Markov Chains already produced for parameter inference to compute Bayes factors B for many different model-dataset combinations .
Standard 6-parameter flat \Lambda CDM model is favoured over all other models considered , with curvature being mildly favoured only when CMB lensing is not included .
Many alternative models are strongly disfavoured by the data , including primordial correlated isocurvature models ( \mathrm { ln } B = -7.8 ) , non-zero scalar-to-tensor ratio ( \mathrm { ln } B = -4.3 ) , running of the spectral index ( \mathrm { ln } B = -4.7 ) , curvature ( \mathrm { ln } B = -3.6 ) , non-standard numbers of neutrinos ( \mathrm { ln } B = -3.1 ) , non-standard neutrino masses ( \mathrm { ln } B = -3.2 ) , non-standard lensing potential ( \mathrm { ln } B = -4.6 ) , evolving dark energy ( \mathrm { ln } B = -3.2 ) , sterile neutrinos ( \mathrm { ln } B = -6.9 ) , and extra sterile neutrinos with a non-zero scalar-to-tensor ratio ( \mathrm { ln } B = -10.8 ) .
Other models are less strongly disfavoured with respect to flat \Lambda CDM .
As with all analyses based on Bayesian Evidence , the final numbers depend on the widths of the parameter priors .
We adopt the priors used in the Planck analysis , while performing a prior sensitivity analysis .
Our quantitative conclusion is that extensions beyond the standard cosmological model are disfavoured by Planck data .
Only when newer Hubble constant measurements are included does \Lambda CDM become disfavoured , and only mildly , compared with a dynamical dark energy model ( \mathrm { ln } B \sim + 2 ) .