pocoMC performs Bayesian inference, including model comparison, for challenging scientific problems. The code utilizes a normalizing flow to precondition the target distribution by removing any correlations between its parameters. pocoMC then generates posterior samples, used for parameter estimation, with a powerful adaptive Sequential Monte Carlo algorithm manifesting a sampling efficiency that can be orders of magnitude higher than without precondition. Furthermore, pocoMC also provides an unbiased estimate of the model evidence that can be used for the task of Bayesian model comparison. The code is designed to excel in demanding parameter estimation problems that include multimodal and highly non–Gaussian target distributions.