Embryogenesis is one of the most fundamental yet least understood processes in biology. Recent advances in single-cell and spatial genomics enable molecular profiling of developing embryos at unprecedented spatial and temporal resolution, creating new opportunities for predictive AI systems that model development across space and time. However, there is currently no standardized benchmark for evaluating whether machine learning systems can accurately predict developmental dynamics, spatial organization, or perturbation outcomes during embryogenesis. We present The Virtual Embryo Challenge, the first large-scale benchmark competition for predictive embryogenesis modeling. The competition is built on a multimodal mouse embryo dataset containing approximately one million cells across 11 developmental stages, integrating single-cell multi-omics, spatial transcriptomics, and genetic perturbations. Participants will address three tasks: temporal gene-expression prediction, spatial-temporal prediction, and mutant perturbation prediction. Participants will have access to curated datasets, preprocessing pipelines, evaluation scripts, starter notebooks, and reproducible baselines. Submissions will be evaluated using biologically grounded metrics for gene-expression accuracy, cell-type composition, and spatial organization. The competition also includes a novel Agent Team track, where autonomous coding agents or recursive LLM-based systems iteratively improve predictive models. The Virtual Embryo Challenge provides a reproducible framework for predictive developmental biology and aims to accelerate progress toward virtual embryo, virtual organ, and eventually digital human modeling.