WebApp1K: A Practical Code-Generation Benchmark for Web App Development
Abstract
WebApp1K is a benchmark for evaluating large language models' capabilities in generating web applications, showing that model size correlates with code correctness and that no universal prompting techniques significantly enhance performance.
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.
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