This research proposes a framework for predicting the performance of vision-language models (VLMs) based on the capabilities of their large language model (LLM) backbones. The framework, called the Capability-Driven Multimodal Scaling Law, uses a low-dimensional capability score extracted from LLM textual benchmarks to predict VLM performance. The authors demonstrate the effectiveness of the framework by training over 150 VLMs on 34 LLMs and evaluating their performance on various benchmarks.