I've been using GPT-5.4 and Claude Opus 4.6 daily for the past month across real projects. Not benchmarks — actual production code, debugging sessions, and architecture decisions. Here's what I found.
Context Window: GPT-5.4 Wins on Paper
GPT-5.4's 1M token context sounds incredible. In practice, I rarely needed more than 200K. But when I did — feeding it an entire codebase for a migration — GPT handled it without quality degradation. Claude starts losing coherence around 400K in my experience.
Code Quality: Claude Wins
For complex refactors and multi-file changes, Claude consistently produced better code. It understood architectural patterns, maintained consistency across files, and caught edge cases GPT missed.
GPT was faster for boilerplate and simple implementations. If I need a quick utility function, GPT-5.4 is 2x faster.
Debugging: Claude Wins (Significantly)
When I paste a stack trace and 500 lines of context, Claude finds the bug in one shot about 70% of the time. GPT-5.4 gets there eventually but often suggests 2-3 things to try first.
Claude seems to "understand" code flow better. It'll say "the issue is in line 247 where you're awaiting a non-async function" rather than "here are 5 possible causes."
Cost: GPT-5.4 Wins
GPT-5.4 is roughly 40% cheaper per token for equivalent quality tasks. For high-volume, lower-complexity work (generating tests, writing docs, simple CRUD), the cost difference adds up.
My Recommendation
Use both. Seriously. I use GPT-5.4 for:
- Quick completions and boilerplate
- Data transformation scripts
- First-draft documentation
I use Claude Opus 4.6 for:
- Complex debugging
- Architecture decisions
- Code review
- Anything touching multiple files
The "which AI is better" debate is wrong. The right question is "which AI for which task."
Which model are you using for what? Drop your setup below.
Top comments (1)
Nice work! The code examples are clean and easy to follow.