Top model explainability tools include SHAP, LIME, IBM AIX360, InterpretML, Alibi, Captum, What-If Tool, Eli5, DALEX, and Fiddler AI. In comparison, SHAP and InterpretML offer both local and global interpretability with strong feature importance and visualizations, while LIME focuses on local explanations for tabular, text, and image data. Captum and Alibi are better for deep learning models with advanced techniques like gradients and counterfactuals, whereas enterprise tools like IBM AIX360 and Fiddler AI provide scalability, monitoring, and governance features. Most tools integrate with popular ML frameworks, but open-source ones are more flexible while enterprise platforms are easier to use. Overall, researchers prefer flexible tools, ML engineers use framework-based solutions, and enterprises choose scalable, user-friendly platforms.