# WebApp1K: A Practical Code-Generation Benchmark for Web App Development

arXiv: https://arxiv.org/abs/2408.00019 · HTML: https://mlsanity.com/paper/2408.00019/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://mlsanity.com/paper/2308.01861/) (2023)

## Built on by (most cited first, 11 of 11)

- [WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models](https://mlsanity.com/paper/2604.18224/) (2026)
- [LLM Code Customization with Visual Results: A Benchmark on TikZ](https://mlsanity.com/paper/2505.04670/) (2025)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [PairCoder++: Pair Programming as a Universal Paradigm for Verified Code-Driven Multimodal and Structured-Artifact Generation](https://mlsanity.com/paper/2607.01883/) (2026)
- [Insights from Benchmarking Frontier Language Models on Web App Code Generation](https://mlsanity.com/paper/2409.05177/) (2024)
- [LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework](https://mlsanity.com/paper/2511.20403/) (2025)
- [Signal or Noise? A Benchmark Study of Agent Skills in Web Development](https://mlsanity.com/paper/2608.23067/) (2026)
- [A Case Study of Web App Coding with OpenAI Reasoning Models](https://mlsanity.com/paper/2409.13773/) (2024)
- [CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation](https://mlsanity.com/paper/2507.19904/) (2025)
- [OpenHarmony Bench: Evaluating LLMs and Coding Agents on OpenHarmony App Development](https://mlsanity.com/paper/2608.16022/) (2026)
- [Large-scale factor analysis shows machine intelligence is only partially interpretable](https://mlsanity.com/paper/2609.36515/) (2026)

## Abstract (arXiv)

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.