Curious about your experience with Sonnet 4.5 vs GPT-5 for large refactors

Exploring the Effectiveness of Sonnet 4.5 versus GPT-5 in Large-Scale Code Refactoring

As software developers undertake increasingly complex legacy migration projects, the choice of AI tools for code refactoring can significantly impact efficiency and accuracy. Recently, I have been evaluating two prominent AI models—Sonnet 4.5 and GPT-5—in the context of migrating a heavily intertwined and “spaghetti” codebase.

Initial Observations and Performance Characteristics

In my testing, GPT-5 demonstrated a notable advantage in generating new logic structures. Its responses appeared more inventive and adaptable when creating fresh code segments, seemingly capable of handling complex reasoning tasks with a higher degree of sophistication. This suggests that GPT-5 could be a valuable asset for tasks requiring the formulation of innovative solutions or the refactoring of particularly intricate logic.

Conversely, Sonnet 4.5 exhibited strengths in preserving the original code context over extended interactions. During conversations extending up to 50 messages, Sonnet maintained a more accurate understanding of the existing codebase, with fewer instances of losing track of prior details. This consistency is especially crucial when working with legacy systems where maintaining structural integrity and existing conventions is paramount.

Notable Differences in Variable Name Accuracy

One of the key trade-offs I observed pertains to variable name hallucinations—a common challenge in AI-assisted code generation. Sonnet 4.5 consistently produced variable names that aligned closely with the original code, reducing the need for manual correction. This attribute minimizes the risk of introducing errors through misnamed variables, thereby streamlining the refactoring process.

In contrast, GPT-5 occasionally introduced hallucinated or inconsistent variable names, necessitating additional review and tidying. While this flexibility can be beneficial for exploring alternative solutions, it may pose challenges when accuracy and fidelity are critical.

Implications for Large-Scale Refactoring Projects

These observations highlight an important consideration: the optimal AI tool may vary depending on the specific phase or focus of a refactoring project. For maintaining code fidelity and context over lengthy interactions, Sonnet 4.5 appears advantageous. Meanwhile, GPT-5 can serve effectively when exploring new logic pathways or implementing significant structural changes.

Conclusion

Choosing between Sonnet 4.5 and GPT-5 involves understanding their respective strengths and limitations. If your project demands consistent context retention with minimal hallucination of variable names, Sonnet 4.5 could be the preferred choice. However, for tasks requiring creative logic generation and innovative restructuring, GPT-5 might offer superior capabilities.

I welcome insights from colleagues who have experience with these tools—are you observing similar trade-offs, or have you found strategies to leverage the strengths of both? As AI continues to evolve, understanding these nuances will be essential for optimizing large-scale code migrations.