SmellBench Refactoring (OpenHands) - Localization Accuracy: leaderboard
Metric: Localization accuracy (%): the edited class or method matches the ground-truth refactoring target; 294 injected-smell refactoring cases (7 smell types, 3 difficulty levels, guided and targeted instructions) in 7 real Python repositories, run once in the Harbor Docker framework with a 20-minute budget, OpenHands agent harness; higher is better. Source: arxiv.org. Saturation forecast: Around December 2026. 6 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Claude Sonnet 4.5 | 84.01 |
| 2 | GPT-5 Mini | 77.21 |
| 3 | Qwen 3 Coder 480B A35B Instruct | 74.56 |
| 4 | DeepSeek V3.2 | 72.24 |
| 5 | Qwen 3 Coder 30B A3B Instruct | 70.67 |
| 6 | Gemini 2.5 Flash | 66.67 |
Interactive version: theaggregate.ai/benchmark?slug=smellbench-refactoring-openhands-localization-accuracy · How It Works · Data refreshed daily, snapshot 2026-09-29.