---
title: "Comment by Mihai_LeanZero on \"What Is LLM Routing? A Simple Guide to Saving Money on AI\" | Hashnode"
description: "The context-sharing gap you describe is real, but there's a second failure mode in cascade setups that's quieter: tool-call schema drift between models. If the"
url: "https://hashnode.com/posts/what-is-llm-routing-a-simple-guide-to-saving-money-on-ai/6aaf570cc919986c0e4e7e19/comment/6aaf7e68e7a982d63c9495d6"
---

[Discussion](https://hashnode.com/posts/what-is-llm-routing-a-simple-guide-to-saving-money-on-ai/6aaf570cc919986c0e4e7e19)

Comment [What Is LLM Routing? A Simple Guide to Saving Money on AI](https://hashnode.com/posts/what-is-llm-routing-a-simple-guide-to-saving-money-on-ai/6aaf570cc919986c0e4e7e19)

[Mihai\_LeanZero](https://hashnode.com/@mihaileanzero)

[Sep 20](https://hashnode.com/posts/what-is-llm-routing-a-simple-guide-to-saving-money-on-ai/6aaf570cc919986c0e4e7e19/comment/6aaf7e68e7a982d63c9495d6)

The context-sharing gap you describe is real, but there's a second failure mode in cascade setups that's quieter: tool-call schema drift between models. If the cheap model calls a function and the cascade kicks in mid-task because the answer looked weak, the fallback model doesn't just lose conversation context, it can also structure tool calls differently (argument names, nesting, even whether it emits a call at all for the same intent). We hit this running a local model first and falling back to a cloud one on tool-heavy agent runs: the fallback silently produced a plausible-looking response with no tool call at all, because it interpreted the same instruction as answerable from text. No error, just a wrong answer that passed review because it read fine. Worth treating as a distinct risk from context loss, since it doesn't show up until the tool call was actually needed.