Papers › Insights from Benchmarking Frontier Language Models on Web App Code Generation

Insights from Benchmarking Frontier Language Models on Web App Code Generation

8 Sep 2024arXiv:2409.05177archive 2025-07-28

Yi Cui

This paper presents insights from evaluating 16 frontier large language models (LLMs) on the WebApp1K benchmark, a test suite designed to assess the ability of LLMs to generate web application code. The results reveal that while all models possess similar underlying knowledge, their performance is differentiated by the frequency of mistakes they make. By analyzing lines of code (LOC) and failure distributions, we find that writing correct code is more complex than generating incorrect code. Furthermore, prompt engineering shows limited efficacy in reducing errors beyond specific cases. These findings suggest that further advancements in coding LLM should emphasize on model reliability and mistake minimization.

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onekq/webapp1k officialmentioned in paper report

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BenchmarkingCode GenerationPrompt Engineering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Code Generation WebApp1K-React gpt-4o-2024-08-06 pass@1 0.885 #3 of 8 Archive leaderboard report
Code Generation WebApp1K-React claude-3.5-sonnet pass@1 0.8808 #4 of 8 Archive leaderboard report
Code Generation WebApp1K-React mistral-large-2 pass@1 0.7804 #6 of 8 Archive leaderboard report
Code Generation WebApp1K-React deepseek-coder-v2-instruct pass@1 0.7002 #7 of 8 Archive leaderboard report
Code Generation WebApp1K-React llama-v3p1-405b-instruct pass@1 0.302 #8 of 8 Archive leaderboard report

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