MMVet

MM-Vet is an evaluation benchmark that examines large multimodal models on complicated multimodal tasks by testing integration of six core vision-language capabilities: recognition, knowledge, spatial awareness, language generation, OCR, and math. It uses an LLM-based evaluator for open-ended outputs and evaluates 16 capability integrations, providing insights into models' ability to solve complex tasks requiring multiple integrated skills.