Notes on GPT 5.x Model Regressions (taoofmac.com)

Recent observations from a developer using OpenAI's GPT-5.x models reveal significant regressions in code analysis capabilities, particularly with the latest iteration, GPT-5.5. The developer found that while GPT-5.5 excels in conversational tasks and complex instructions, it consistently underperformed compared to its predecessor, GPT-5.3-Codex, when tasked with identifying and fixing code logic errors. This trend suggests that newer models, while improved for general tasks, may have sacrificed depth and precision in specialized areas like code review. This raises concerns about the model tuning process, which may prioritize versatility over thoroughness in certain technical applications. The implications for the AI/ML community are profound, especially for developers relying on these models for code analysis. The findings indicate a potential trade-off between broad usability and targeted functionality, which could impact workflows that depend on accurate code assessment. As a result, the developer shifted their approach, using older models for detailed code reviews while employing newer versions for broader planning and conversational tasks. This situation highlights the importance of maintaining flexibility in model selection, advocating for a modular approach that allows developers to adapt to changing model performances over time.