LLM Routing
A technique for dispatching large language model requests to models of differing capability and cost in order to optimize performance and expense; routing decisions are often made using proxy indicators such as prompt length.
Timeline (last 90 days)
A 2026 LLM routing study (arXiv:2609.17542) found that omitted function words (such as the connectives however, although, if...then, etc.) are what make text look shorter; after removing connectives the token count drops, but the reasoning load does not decrease.
If you route LLM traffic by token count (long prompts to strong models, short prompts to cheap models), you are running a systematically biased proxy metric that misreads "short" as "simple".
Practical advice: do not route LLM traffic by length, but by features that actually cost the model—such as nesting depth, unresolved anaphora, and whether reasoning is explicitly stated or implicit.
The paper conducted experiments on two tasks, a synthetic label-efficient prediction game and LLM routing, and E2D.PMSide outperforms previous methods on both tasks.
LLM routing refers to the system choosing among multiple models of different sizes or capabilities when faced with a user query, in order to balance cost and quality.
In the LLM routing scenario, E2D.PMSide estimates the success probability of weak models via a regression oracle, achieving comparable answer quality at lower inference cost.
All Facts (6)
A 2026 LLM routing study (arXiv:2609.17542) found that omitted function words (such as the connectives however, although, if...then, etc.) are what make text look shorter; after removing connectives the token count drops, but the reasoning load does not decrease.
50%UnverifiedIf you route LLM traffic by token count (long prompts to strong models, short prompts to cheap models), you are running a systematically biased proxy metric that misreads "short" as "simple".
50%UnverifiedPractical advice: do not route LLM traffic by length, but by features that actually cost the model—such as nesting depth, unresolved anaphora, and whether reasoning is explicitly stated or implicit.
50%UnverifiedThe paper conducted experiments on two tasks, a synthetic label-efficient prediction game and LLM routing, and E2D.PMSide outperforms previous methods on both tasks.
50%UnverifiedLLM routing refers to the system choosing among multiple models of different sizes or capabilities when faced with a user query, in order to balance cost and quality.
50%UnverifiedIn the LLM routing scenario, E2D.PMSide estimates the success probability of weak models via a regression oracle, achieving comparable answer quality at lower inference cost.
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