25 AI Models Tested on 100 Python Bugs: The Verdict
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An Experiment on AI Coding Models
A developer intentionally broke his own codebase to test 25 AI coding models on 100 real Python bugs. These bugs were extracted from three of his production projects, including complex errors such as an off-by-one error in a pagination helper, a race condition in an asynchronous task queue, a silent type coercion bug that only appeared on Postgres and not on SQLite, a memory leak in a FastAPI worker, and a faulty regular expression that passed all tests except the one that mattered.
A Rigorous Process
The experiment took place over a period of nine days and cost more in API credits than the author is willing to admit. Each bug was submitted individually to the AI models, without hints, to test their ability to identify and fix errors without relying on impressions or marketing promises.
Objective: Assess Real-World Effectiveness
The goal of this approach was to go beyond traditional evaluations based on benchmarks like SWE bench Verified, LiveCodeBench, and Terminal Bench. The developer uncovered the exact bugs, the precise fixes, and the exact figures to allow for independent verification of his work.
This study offers valuable insights for developers looking to integrate AI models into their development process, highlighting those that stand out for their real-world effectiveness.
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