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Allowing users to select their own LLM models often degrades quality and consistency. Most users lack the evaluation data and benchmarking infrastructure to make informed model choices, leading to suboptimal results based on hype rather than performance. Different models interpret prompts differently, causing workflow instability and inconsistent outputs. Dynamic, data-driven routing systems that automatically select the optimal model for each task based on continuous evaluations deliver better results than user-driven selection. Model orchestration should be an engineering decision grounded in metrics, not a user preference feature.