Claude Sonnet 4.6 vs GPT-5.4

For high-volume coding tasks where input tokens dominate costs, GPT-5.4's $2.5/M input pricing delivers nearly identical performance (67/100 vs 66/100) at 17% lower cost per request. However, both models share the same $15/M output pricing and 128K max output tokens, so the cost advantage diminishes for generation-heavy workflows like code refactoring or documentation writing where outputs approach the token limits.

The additional 100K tokens in GPT-5.4 become critical for analyzing large codebases with extensive dependency chains or processing multiple 20-30K line files simultaneously. Since both models cap output at 128K tokens regardless of input size, GPT-5.4's advantage is purely in comprehension capacity rather than generation, making it marginally better for full-repository analysis but irrelevant for typical function-level coding tasks.

Anthropic's models historically show stronger performance on nuanced code review and security analysis tasks that aren't fully captured in standard benchmarks, which typically measure completion and syntax accuracy. The 1.5% score difference (66 vs 67) falls well within benchmark variance, while Claude's training approach emphasizes safer code generation patterns that reduce subtle bugs in production systems.

GPT-5.4's native file modality allows direct processing of binary formats like compiled objects, zip archives, and proprietary document types without preprocessing, while Claude Sonnet 4.6 requires conversion to text or image formats first. This matters most for automated CI/CD pipelines and code analysis tools, though both models' identical $15/M output pricing means the convenience doesn't translate to cost savings.