The research introduces LLaVA-Critic, a new open-source large multimodal model specifically designed to evaluate the performance of other multimodal models. Trained on a specialized dataset, it functions effectively in two primary ways: first, as an LMM-as-a-Judge, providing reliable scores comparable to or better than commercial models like GPT, and second, for Preference Learning, generating reward signals that improve model alignment. This work highlights the potential of open-source models for self-critique and scalable evaluation in the multimodal domain. The text details the dataset creation process, model architecture, and experimental results supporting LLaVA-Critic's capabilities.