A recent experiment evaluated 11 large language models on their ability to refactor a complex "god node" within a LangGraph agent, testing both architectural design and analytical accuracy.

Key Points

• The experiment compared 11 models, including GPT-5.4/5.5, Fable-5, DeepSeek-4-pro, and Qwen-3.7-max, on a code reorganization task.
• Models were tasked with splitting a 350-line "plan node" into a more modular, observable graph structure.
• Fable-5 and GPT-5.5 emerged as the most reliable evaluators, consistently identifying critical bugs like double-calculation errors and skipped iteration increments.
• The study highlighted that while models are effective at generating architectural options, they frequently struggle with factual consistency and risk assessment in complex codebases.
• Findings suggest that using multiple models to generate proposals, followed by a rigorous review by top-performing evaluators, yields the most robust architectural results.

Why it Matters

This experiment demonstrates that while LLMs are powerful tools for architectural refactoring, they remain prone to subtle logic errors and hallucinations that require human oversight. Relying on a single model for critical infrastructure changes is risky, but using a multi-model approach significantly improves the quality and safety of the final system design.