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Why Token-Level LLM Routers Spend 95% of Their Time on Cache Bookkeeping

This story is from 2026-10-09. It is preserved in the archive; the latest stories are on the live feed.

Most production model routers work at the prompt boundary. You send a request to Cursor or RouteLLM, a classifier guesses whether the task is hard, and one model generates the entire response from the first token to the stop sequence. Algorithmic papers over the last year have pushed a finer granul…

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  1. 2026-10-09 16:25 · DEV Community — AI
    Why Token-Level LLM Routers Spend 95% of Their Time on Cache Bookkeeping

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