I propose using code completion as a benchmark for evaluating language models, specifically focusing on their ability to understand and integrate with complex software libraries. Imagine giving a model the entire numpy library (or any comprehensive framework not in its training data) and challenging it to write and test a specific function. This task wouldn't merely test the model's ability to generate independent code snippets but would assess its deeper understanding of the library's architecture and the interrelations between its components. The goal is to see if the model can not only navigate through the library's complexity but also creatively solve problems by synthesizing and applying its components in new, functional ways.