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The main objective of this study is to identify fibromyalgia syndrome (FMS) clusters using the Revised Fibromyalgia Impact Questionnaire (FIQR), and to examine whether the clusters differ in sociodemographic characteristics, clinical measures, direct and indirect costs, levels of inflammatory markers, and brain morphometry. A hierarchical cluster analysis was performed to classify a large, pooled Spanish sample of patients with FMS (N = 947) using the FIQR as clustering variable. A latent profile analysis was subsequently conducted to confirm the optimal number of FMS clusters. To examine external validity, a battery of clinical measures, economic costs, inflammatory markers, and gray matter volumes of relevant cortical and subcortical areas were analyzed. We also compared the discriminant validity of the clusters with the original FIQR severity categories. To promote the implementation in real-world clinical practice, we built a free online cluster calculator. Our findings indicated that a four-cluster solution more clearly captured the heterogeneity of FIQR data and provided the best fit. This cluster solution allowed for detection of differences for most clinical outcomes and economic costs. Regarding the inflammatory and brain-based biomarkers, differences were found in C-reactive protein, and tendencies were found in the right medial prefrontal cortex, the right parahippocampal gyrus, and the right middle cingulate cortex; brain regions associated with executive functions and pain processing. The original FIQR categories presented similar results, although their precision in discriminating among the nonextreme categories (ie, moderate and severe) was not sound. These findings are discussed in relation to previous research on FMS clustering.