arXiv:2602.02823v2 Announce Type: replace Abstract: As LLMs proliferate with diverse capabilities and costs, LLM routing has emerged by learning to predict each LLM's quality and cost for a given query, then selecting the one with high quality and low cost. However, existing routers implicitly assume a single fixed quality and cost per LLM for each query, ignoring that the same LLM's quality varies with its output length. This causes routers to exclude powerful LLMs when their estimated cost exceeds the budget, missing the opportunity that these LLMs could still deliver high quality at reduced
The proliferation of diverse LLMs and the increasing focus on cost-efficiency and quality optimization are driving advancements in LLM routing paradigms.
This development could significantly enhance the efficiency, cost-effectiveness, and real-world applicability of LLMs by enabling more intelligent resource allocation.
LLM routing is evolving beyond fixed quality/cost assumptions, allowing for dynamic selection based on output length and real-time performance.
- · AI developers
- · Cloud providers
- · Businesses leveraging LLMs
- · Users of AI-powered applications
- · LLMs with inflexible cost/quality models
- · Legacy LLM routing solutions
Improved cost-efficiency and performance in applications relying on multiple LLMs.
Increased adoption and integration of LLMs in diverse industries due to better resource management.
New business models emerging around meta-LLM optimization and intelligent AI service orchestration.
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Read at arXiv cs.CL