This paper compares and analyzes the face recognition performance of general base models (e.g., CLIP, BLIP, GPT-4o, Grok-4) and specialized models (e.g., AdaFace, ArcFace). Experiments using multiple base models and benchmark datasets demonstrate that the specialized models outperform the zero-shot base model, and that the zero-shot base model improves on over-segmented face images. Furthermore, score-level fusion of the base and specialized models improves accuracy at low error rates. Furthermore, base models such as GPT-4o and Grok-4 provide explainability for the face recognition pipeline and help address the low confidence decision-making of AdaFace. In conclusion, we emphasize the importance of appropriately combining specialized and base models.