Switching LLMs mid-conversation is a ski rental problem
What happened
Edgee describes that typical LLM routers only decide which model to use for each request, but for coding agents that maintain a conversation history, switching models discards cached prompts, incurring extra cost. They frame the timing of model switches as a ski‑rental online problem and propose a self‑learning routing system that evaluates past sessions to choose the cheapest model map and switching policy.
Why it matters
Choosing the right moment to switch models can reduce costs while keeping performance, highlighting the importance of accounting for prompt caching in multi‑turn AI applications.
Is this a big deal?
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