text: In contrast to routers motivated by controlling costs, several LLM router designs focus solely on improving quality of 
  responses [31, 45, 57, 58]. 
  
  The LLM routers described thus far do not modify the queries or individual LLM responses. Other types of control planes 
  do. Ensemble approaches such as mixture-of-expert (MoE) [29, 30, 52, 56] architectures select a subset of underlying 
  models to apply to each token of a query and merge their responses. LLM synthesis [40] architectures operate similarly, 
  but route the entire query to a subset of underlying LLMs and merge their responses. These approaches reduce inference 
  costs by using fewer and/or less complex underlying models. 
  
  Applications of LLM routers. A key use case for LLM routers is to help LLM-based application reduce cost. Several 
  commercial routers, including Unify [12], Martian [5], NotDiamond [7], and others, offer this as a service. By replacing a 
  few lines of code, the application can send user queries to a router service, rather than directly to some LLM provider. The 
  service selects the optimal LLM and forwards the queries. Commercial router services claim that this results in significant 
  cost savings: up to 98% in the case of Martian [5], and 10× in the case of NotDiamond [7]. 