DeepSWE: AI Coding Model Benchmark Solves Data Contamination

A new benchmark called DeepSWE is changing how AI coding models are assessed. It focuses on real-world programming challenges, not artificial ones. Here's the thing: DeepSWE uses contamination-free tasks. This means the AI models haven't seen these problems during their training. These tasks come from 91 open-source repositories, covering languages like TypeScript, Go, and Rust. This ensures a practical evaluation of how coding models handle different programming styles. What's interesting is DeepSWE also has a strict verification system to reduce errors and provide consistent performance. For example, GPT 5.5 balances speed, cost, and accuracy, outperforming models like Opus 4.7 in runtime and expense. Other models, such as Claude Haiku 4.5, face difficulties with multi-part prompts. The bottom line: DeepSWE helps make meaningful comparisons across different AI coding systems, giving a clearer picture of their true problem-solving abilities.