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The paper analyzes six commercial dynamic LLM routers across 14 settings and finds none outperforms a router that randomly selects between two well-chosen models at matched cost, attributing failures to difficulty blindness, length reversal, semantic matching, and roster suboptimality — patterns the standard evaluation objective actually rewards.