Glioblastomas are the most malignant subtypes of glioma and many efforts are currently made to improve their characterization though molecular, microvascular, immunogenic and metabolomic approaches. The variability within pre-clinical tumor models may mimic glioma heterogeneity and force the development of innovative analytical methodologies. In this study, we investigate the metabolic variability within three rat models of glioma: C6, RG2 and F98, using in vivo magnetic resonance spectroscopy (1H MRS) and ex vivo high resolution magic angle spinning (1H HRMAS MRS). We used a multivariate statistic approach with orthogonal projection to latent structure-discriminant analysis (OPLS-DA) that was compared with univariate statistic. OPLS-DA reveals a clear separation between C6, RG2 and F98 tumors and, with the help of shared and unique structure plot (SUS-Plot), promotes a comprehensive view of their metabolic differences. Both in vivo and ex vivo analyses are similar but ex vivo 1H HRMAS MRS provides more robust results. In conclusion, MRS-based OPLS-DA appears sensitive enough to correctly predict the classification of tumors and to investigate the relationship between the host brain metabolism and the grafted tumor.