There is growing interest in creating large-scale computational models for biological process. One of the challenges in such a project is to fit and validate larger and larger models, a process that requires more high-quality experimental data and more computational effort as the size of the model grows. Quantitative model refinement is a recently proposed model construction technique addressing this challenge. It proposes to create a model in an iterative fashion by adding details to its species, and to fix the numerical setup in a way that guarantees to preserve the fit and validation of the model. In this survey we make an excursion through quantitative model refinement – this includes introducing the concept of quantitative model refinement for reaction-based models, for rule-based models, for Petri nets and for guarded command language models, and to illustrate it on three case studies (the heat shock response, the ErbB signaling pathway, and the self-assembly of intermediate filaments).