Large Language Models (LLMs) have allowed recent LLM-based approaches toachieve excellent performance on long-video understanding benchmarks. Weinvestigate how extensive world knowledge and strong reasoning skills ofunderlying LLMs influence this strong performance. Surprisingly, we discoverthat LLM-based approaches can yield surprisingly good accuracy on long-videotasks with limited video information, sometimes even with no video specificinformation. Building on this, we explore injecting video-specific informationinto an LLM-based framework. We utilize off-the-shelf vision tools to extractthree object-centric information modalities from videos, and then leveragenatural language as a medium for fusing this information. Our resultingMultimodal Video Understanding (MVU) framework demonstrates state-of-the-artperformance across multiple video understanding benchmarks. Strong performancealso on robotics domain tasks establish its strong generality. Code:https://github.com/kahnchana/mvu