  We introduce WebApp1K, a practical code-generation benchmark to measure LLM
ability to develop web apps. This benchmark aims to calibrate LLM output and
aid the models to progressively improve code correctness and functionality. The
benchmark is lightweight and easy to run. We present the initial version of
WebApp1K, and share our findings of running the benchmark against the latest
frontier LLMs. First, open source LLMs deliver impressive performance, closely
trailing behind GPT-4o and Claude 3.5. Second, model size has strong
correlation with code correctness. Third, no prompting techniques have been
found to lift performance either universally to all models, or significantly to
a single model.
