Advances in bioinformatics pipelines for variant calling have accelerated with the decreasing cost of next-generation sequencing (NGS). Accurate detection of somatic mutations is critical for precision oncology, particularly for guiding therapy decisions. However, somatic variant calling remains challenging due to cancer heterogeneity, diverse mutational landscapes, and sequencing noise. A comprehensive dataset of fully characterised tumoral genomes, representing the variability across cancer types, is still lacking, even among synthetic data, limiting systematic evaluation and optimization of variant calling tools. In this work, we evaluated nine existing somatic simulators (Synggen, BAMSurgeon, SVEngine, VarSim, Xome-Blender, tHapMix, Pysim-sv, SCNVSim, HeteroGenesis) for their ability to control biological (variant type, number, position, length, content, zygosity; sample clonality and contamination) and technical parameters (sequencing errors, coverage, base quality). None provided full control over both domains, nor guidance for cancer-specific parameter tuning. To address this, we developed MOV&RSim, a novel simulator that leverages data-driven information to set variants and reads characteristics, generating realistic tumoral samples, and providing complete control on both biological and technical parameters. Additionally, we leveraged well-annotated variant databases (COSMIC and TGCA) to create cancer-specific presets that inform the simulator’s parameters for 21 cancer types. MOV&RSim, packaged in Docker and freely available for academic use, enables users to simulate biologically realistic and technically nuanced tumoral samples. It represents the most flexible and comprehensive simulation framework currently available for benchmarking and optimizing somatic variant calling pipelines across diverse cancer types.