CodeRabbit's evaluation of GPT-5, the latest generational leap in AI reasoning models, reveals its superior ability to understand, reason through, and identify errors in complex codebases compared to previous models like Sonnet-4, Opus-4, and OpenAI’s O3. In a comprehensive battery of 300 error-diverse pull requests, GPT-5 identified 85% of bugs, significantly outperforming other models, which detected between 66% and 69%. Particularly impressive was its performance with the most challenging pull requests, achieving a 77.3% pass rate, which is a marked improvement over previous models. GPT-5's ability to catch a wider array of issues, particularly concurrency, performance, and security bugs, demonstrates its enhanced reasoning skills. The model's advanced contextual reasoning and ability to provide granular, task-oriented recommendations make it a potent tool in AI-powered code reviews. Consequently, CodeRabbit plans to integrate GPT-5 as the core reasoning model in its pipeline, aiming to enhance the quality and depth of code reviews, offering a significant leap in engineering insight and reliability.