Compared to Claude, GPT5 prefers encapsulating code, but this behavior often increases code complexity.
It doesn’t strictly follow plans every time, especially for complex projects, failing to focus adequately on task-relevant code.
When asked to modify code, it frequently alters correct (already completed) code (achieving the same outcome but with different implementations, e.g., splitting a single expression into multiple steps) instead of focusing substantively on core issues.
Its understanding of projects across different application scenarios is weaker than Claude’s. Claude’s approach to solving challenging problems aligns more closely with that of an “experienced engineer.”
It often reiterates results from previous tasks or restates planned objectives, typically requiring more conversational rounds than other models to complete a development task (even very simple ones).
It tends to overcomplicate simple issues (including planning), which is unfriendly to most internet products pursuing the MVP principle.
When writing algorithms, it fails to select suitable solutions, even when provided with examples.
It rarely invokes tools, and even when explicitly instructed to execute, it does not always do so.
Strengths:
Its articulation of understanding complex projects is excellent (even when not acted upon).
Its planning for development work is strong (though sometimes overly complex).
For projects using multiple programming languages, it has a lower probability of syntax confusion compared to Claude.