GPT-5.5 low vs. medium vs. high vs. xhigh: the reasoning curve on 26 real tasks (www.stet.sh)

Recent testing of GPT-5.5 Codex across four reasoning effort settings (low, medium, high, and xhigh) on 26 coding tasks from the GraphQL-go-tools repository reveals significant variances in code quality and semantic accuracy. While low and medium settings effectively passed 21 out of 26 tests, medium outperformed low in semantic equivalence and code review metrics—indicating that as reasoning complexity increases, so does the quality of patches produced. The high setting emerged as the optimal balance of cost and quality, demonstrating better integration and coding practices, while xhigh delivered the highest semantic fidelity but at a significantly higher cost and with a greater footprint risk. This analysis underscores the importance of selecting the appropriate reasoning setting for specific coding tasks, challenging the notion that more complexity always yields better results. Instead, the findings suggest a nuanced approach where medium or high settings may often suffice for practical applications, enhancing code clarity and maintainability without incurring the prohibitive costs associated with xhigh. The study advocates for adaptive benchmarking that assesses not only test success but real-world applicability—paving the way for improved decision-making when employing AI-assisted coding agents in software development.