Comparing GPT-5.4 and Claude Opus 4.6 for Real-World Tasks

The author compares the performance of GPT-5.4 and Claude Opus 4.6 across various tasks like code quality, debugging, and cost. They recommend using both models strategically based on the specific needs of the task.

Details

The article discusses the author's experience using GPT-5.4 and Claude Opus 4.6 for real-world projects over the past month. While GPT-5.4 has a larger 1M token context window, the author found that they rarely needed more than 200K. However, when they did need the larger context, such as for an entire codebase migration, GPT-5.4 handled it without quality degradation, unlike Claude which starts losing coherence around 400K tokens. For complex code refactors and multi-file changes, the author found that Claude consistently produced better quality code that maintained architectural patterns and caught edge cases that GPT-5.4 missed. However, GPT-5.4 was faster for simple boilerplate and utility functions. The author also noted that Claude was significantly better at debugging, finding the root cause of issues about 70% of the time when provided with a stack trace and context, compared to GPT-5.4 which often suggested multiple possible causes. Claude seemed to have a better understanding of code flow. In terms of cost, the author found that GPT-5.4 was about 40% cheaper per token for equivalent quality tasks, making it more cost-effective for high-volume, lower-complexity work like generating tests, writing docs, and simple CRUD operations.