The variability in translational models profoundly impacts the outcomes and predictive value of preclinical studies for gastrointestinal (GI) cancer treatments. Preclinical models, including 2D cell cultures, 3D organoids, patient-derived xenografts (PDXs), and animal models, provide distinct advantages and limitations in replicating the complex tumor microenvironment (TME) of human cancers. Each model's unique biological and structural differences contribute to discrepancies in treatment responses, challenging the direct translation of experimental results to clinical settings. While 2D cell cultures are cost-effective and suitable for high-throughput screening, they lack the 3D architecture and cellular interactions of the in vivo TME. Organoids offer a more comprehensive 3D structure that better mirrors tumor heterogeneity, yet they still face limitations in fully mimicking in vivo conditions, such as vascularization and immune cell interactions. PDXs, although more representative of human cancers due to their genetic fidelity and TME preservation, are costly and resource-intensive, with human stromal and immune components gradually replaced by murine counterparts over time. This review assesses the strengths and limitations of each model, highlighting recent advancements in translational platforms that incorporate complex TME features. Understanding the influence of model selection on treatment efficacy predictions is essential for enhancing the reliability of preclinical findings and advancing personalized therapeutic strategies for GI cancers.