A new benchmarking framework is proposed for evaluating the explanatory quality of open-weight vision-language models in face recognition. The framework focuses on explanation quality, including relevance and faithfulness, and provides a structured explanation format for automated querying and auditing. The authors benchmark several families of open-weight VLMs and highlight the need for explanation quality metrics in face recognition systems.