The goal of variable clustering is to partition a random vector ${\bf X} \in R^p$ in sub-groups of similar probabilistic behavior. Popular methods such as hierarchical clustering or $K$-means are algorithmic procedures applied to observations on ${\bf X}$, while no population level target is defined prior to estimation. We take a different view in this paper, where we propose and investigate model based variable clustering. We consider three models, of increasing level of complexity, termed generically $G$-models, with $G$ standing for the partition to be estimated. Motivated by the potential lack of identifiability of the $G$-latent models, which are currently used in problems involving variable clustering, we introduce two new classes of models, the $G$-exchangeable and the $G$-block covariance models. We show that both classes are identifiable, for any distribution of ${\bf X}$. Our focus is on clusters that are invariant with respect to unknown monotone transformations of the data, and that can be estimated in a computationally feasible manner. Both desiderata can be met if the clusters correspond to blocks in the copula correlation matrix of ${\bf X}$, assumed to have a Gaussian copula distribution. This motivates the introduction of a new similarity metric for cluster membership, CORD, and a homonymous method for cluster estimation. Central to our work is the derivation of the minimax value of the CORD cluster separation for exact partition recovery. We obtained the surprising result that this value is of order $\sqrt{{\log (p)}/{n}}$, irrespective of the number of clusters, or of the size of the smallest cluster. Our new procedure, CORD, available on CRAN, achieves this bound, is easy to implement and has computational complexity that is polynomial in $p$.