# 25 AI Coding Models Tested with Real Python Bugs Which model actually fixed bugs, and which one just sounded confident? - **Source:** [UX Planet](https://uxplanet.org) - **Published:** 2026-07-23 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/8a02311b-064f-4c40-8e59-e02f31922cb0 ## Purpose of the Test To measure the performance of AI coding models against real-world errors, 100 different Python bugs were selected. These bugs were taken from real production scenarios and presented to the models without any hints. ## Testing Process 25 different AI models were tested against 100 Python bugs, each one separately. The process took nine days and consumed a significant amount of API credits. Each model's ability to detect and fix bugs was recorded. ## Types of Bugs The tested bugs included scenarios like pagination errors, concurrency issues, silent type coercion bugs, memory leaks, and broken regex. These were real-world problems that could have significant consequences. ## Reliability of Results The test results are based on the actual performance of the models, not just marketing claims. Each bug and fix was documented in detail, making the results independently verifiable. ## Leading Model The top-performing model in the tests provided more consistent and accurate fixes compared to others. It was able to solve complex bugs with a high accuracy rate. ## Key Takeaway There are significant performance differences among AI coding models. Developers should consider real-world test results when choosing these models, as it is critical for the reliability of their projects. ## Sources covering this story - https://uxplanet.org/i-tested-25-ai-coding-models-on-100-real-python-bugs-heres-the-leaderboard-725ab72a56b3?source=rss----819cc2aaeee0---4 — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._