Claude Code's Edge: Why Sonnet 4.5 Beats GPT-4o for Multi-File Projects
A comparison of Claude Sonnet 4.5 and GPT-4o on autonomous agent workloads reveals advantages of Claude Code for multi-file projects, long-running sessions, and error recovery.
Why it matters
The findings highlight the advantages of using Claude Code for complex, multi-file development tasks where maintaining codebase awareness and long-term context are critical.
Key Points
- 1Claude Sonnet 4.5 outperforms GPT-4o on tasks involving 3+ interdependent files and a test suite
- 2Claude maintains better awareness of existing codebase and avoids conflicts, while GPT-4o generates standalone code
- 3Claude's long-context handling is more reliable, with less instruction forgetting compared to GPT-4o
- 4Claude's prompt caching significantly reduces effective cost compared to GPT-4o
Details
The article presents a 30-day, real-world test comparing the performance of Claude Sonnet 4.5 and GPT-4o on identical autonomous agent workloads. The test involved tasks like writing Python scripts with tests and documentation, refactoring with backward compatibility, and API integration. The results show that Claude Sonnet 4.5 significantly outperformed GPT-4o on these multi-file, long-running tasks. The key differentiator is that Claude tends to read and understand the existing codebase before writing, while GPT-4o more often generates standalone code that can conflict with the existing system. Additionally, Claude's long-context handling is more reliable, maintaining instruction following at over 150K tokens, compared to noticeable degradation for GPT-4o past 100K tokens. The article also discusses the cost advantages of Claude's prompt caching, which can reduce effective input costs by 80-90% compared to GPT-4o's lack of native caching.
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