Imagine teaching a computer to see and reason, to understand a picture and answer questions about it as you would. That's the challenge of vision-language AI models (VLMs). But how can we analyze and improve the "seeing" and "thinking" aspects of these models separately when they are usually intertwined? Researchers have introduced Prism, a groundbreaking framework for decoupling and assessing the distinct capabilities of VLMs. Prism separates the process into two stages: a perception stage, where the VLM extracts visual information from an image and translates it into text, and a reasoning stage, where a large language model (LLM) uses this textual information to answer questions. Think of it like giving the LLM an eyewitness report of the image. This modular design allows researchers to test different VLMs and LLMs independently. They can keep the "reasoning" constant while testing different "seeing" models, or vice-versa. Using this framework on benchmarks like MMStar, researchers gained fascinating insights. For instance, commercial VLMs like GPT-4o showed superior perception skills, while open-source models often struggled with reasoning. Surprisingly, the size of the language model within these open-source VLMs didn't greatly affect their perception abilities. But the real potential of Prism extends beyond analysis. It offers an efficient new model for tackling vision-language tasks. By combining a smaller VLM (focused on perception) with a powerful LLM (dedicated to reasoning), Prism achieves results comparable to much larger models but with lower training costs and faster processing. This modularity allows for optimizing the system's individual components. You could choose the best "eyes" and the best "brain" independently to address a specific task. Quantitative evaluations showed that Prism, when configured with a small visual captioner and a freely available LLM like ChatGPT-3.5, often outperformed much larger open-source VLMs on various benchmarks, particularly on questions requiring complex reasoning. This framework offers a promising path towards more efficient and interpretable vision-language AI. While challenges remain, Prism opens doors to building more tailored, powerful, and cost-effective AI systems that can interpret images in the years to come. It also allows developers to create highly specialized models without huge datasets or massive computing power, democratizing access to advanced AI capabilities.