A recent experiment, TRINITY-Router, systematically evaluated 8 large language models across 316 diverse tasks to test common routing hypotheses. The findings reveal that many widely held assumptions about model specialization—such as certain models being universally better for coding or reasoning—do not hold up under rigorous testing. Instead, the data suggests that optimal routing is highly task-dependent and often counterintuitive. For developers building LLM-based applications, this study provides a valuable empirical foundation for designing more effective model selection strategies, moving beyond anecdotal or heuristic-based approaches. The methodology and results are particularly relevant for teams working on multi-model architectures or LLM orchestration systems.
A large-scale experiment challenges common LLM routing assumptions, offering empirical evidence for better model selection.