Mass spectrometry (MS) is increasingly used for isotopic studies of metabolism with a broad range of applications going from system biology to biotechnology. The analytical information is derived from the exploitation of the isotopic cluster, from which the contribution of each isotopologue (i.e. molecular entities differing only in the number of isotopic substitutions) of metabolites can be quantified. The correctness of biological interpretations depends on the quality of the isotopic measurements (isotopologue distributions). Despite an increasing number of stable isotope labeling studies, there has been so far very limited investigations of the accuracy and precision of the measurement of isotopologue distributions by MS. The main reason is the lack of relevant standards to evaluate the MS equipments and the entire experimental workflow. Here, we present a strategy to probe the accuracy and the precision of 13C-isotopologue measurements by MS, which is based upon the production of a biological sample containing a mixture of metabolites with controlled and predictable proportions of the isotopic species. This can be achieved by feeding a micro-organism with a mixture of labelled substrate in which 13C atoms are statistically distributed. As proof of concept, two model microorganisms, the eukaryotic cell Pichia augusta and the prokaryotic cell Escherichia coli, have been cultivated respectively on methanol or acetate as sole carbon source and at appropriate proportions of all existing isotopic species. These proportions have been chosen to produce metabolites in which the isotopologue distribution should remarkably give the binomial coefficients of the Pascal’s triangle. After preparation of the cellular extracts using state-of-art procedures, the entire isotopologue distributions of intracellular metabolites with carbon skeletons ranging from 3 to 10 carbon elements were quantified by LC-MS/MS and compared to predicted values. For both P. augusta or E. coli metabolites, the experimental MIDs were in close agreement with predictions, i.e. within 0.7% accuracy and of ±1% precision. This strategy can be used for i) evaluating instruments, ii) determining the occurrence of isotopic effects, iii) evaluating the quality of isotopic data for metabolic flux analysis.