Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

• URL: http://arxiv.org/abs/2603.04601v1
• Date: Wed, 04 Mar 2026 21:00:33 GMT
• Title: Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development
• Authors: Hung Tran, Langston Nashold, Rayan Krishnan, Antoine Bigeard, Alex Gu,
• Abstract summary: Existing benchmarks measure isolated tasks rather than the complete "zero-to-one" process of building a working application from scratch.<n>We introduce Vibe Code Bench, a benchmark of 100 web application specifications with 964 browser-based substeps.<n>Our contributions include (1) a novel benchmark dataset and browser-based evaluation pipeline for end-to-end web application development, (2) a comprehensive evaluation of 16 frontier models with cost, latency, and error analysis, and (3) an evaluator alignment protocol with both cross-model and human annotation results.
• Score: 6.072381417546439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code generation has emerged as one of AI's highest-impact use cases, yet existing benchmarks measure isolated tasks rather than the complete "zero-to-one" process of building a working application from scratch. We introduce Vibe Code Bench, a benchmark of 100 web application specifications (50 public validation, 50 held-out test) with 964 browser-based workflows comprising 10,131 substeps, evaluated against deployed applications by an autonomous browser agent. Across 16 frontier models, the best achieves only 58.0% accuracy on the test split, revealing that reliable end-to-end application development remains a frontier challenge. We identify self-testing during generation as a strong performance predictor (Pearson r=0.72), and show through a completed human alignment study that evaluator selection materially affects outcomes (31.8-93.6% pairwise step-level agreement). Our contributions include (1) a novel benchmark dataset and browser-based evaluation pipeline for end-to-end web application development, (2) a comprehensive evaluation of 16 frontier models with cost, latency, and error analysis, and (3) an evaluator alignment protocol with both cross-model and human annotation results.

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