Modern data analytic workloads increasingly require handling multiple data models simultaneously. Two primary approaches meet this need: polyglot persistence and multi-model database systems. Polyglot persistence employs a coordinator program to manage several independent database systems but suffers from high communication costs due to its physically disaggregated architecture. Meanwhile, existing multi-model database systems rely on a single storage engine optimized for a specific data model, resulting in inefficient processing across diverse data models. To address these limitations, we present M2, a multi-model analytic system with integrated storage engines. M2 treats all data models as first-class entities, composing query plans that incorporate operations across models. To effectively combine data from different models, the system introduces a specialized inter-model join algorithm called multi-stage hash join. Our evaluation demonstrates that M2 outperforms existing approaches by up to 188x speedup on multi-model analytics, confirming the effectiveness of our proposed techniques.