If your n8n workflow has more than one AI step, you probably do not want the same model in all of them

Took me way too long to work this one out honestly. Had a client flow with a couple of AI Agent steps in it and i just put the same model in both without really thinking about it. That is the default thing to do right, pick a good model, use it everywhere.

Fine on a few test items. Then it goes on real volume and the token usage is just silly, and it is dragging. And i spent a good bit of time convinced the model was the issue, kept messing with prompts, thought about upgrading. None of that was it.

The two steps just want different things. One is doing the grunt work, big input, runs on everything, no thinking really, just pull the data and hand it back. The other one gets almost nothing as input but it has to make an actual call on the messy cases. I was giving both the exact same treatment which in hindsight is dumb.

So the grunt one i moved over to GLM-5.3. In n8n you just take the OpenAI Chat Model node and change the base URL to point at Zai, pick the model, done. Small, runs fast and the context window is the thing there, it was eating a lot per run and it did not fall over on the big job. Left the judgment step on Opus. Opus is still better when it actually has to think hard, i am not disputing that. Just a waste having it grind through the repetitive stuff.

And then, kind of embarrassing, turns out one of the steps did not need a model at all. It was just checking the data was in the right shape. Regex in a Code node. Instant, and it cannot invent a field that was not there, which the LLM did do once or twice. Would not have caught that if i had not gone digging through where the errors were.

That is basically it. Switch node handles the routing off a value from the first step. The whole reliability jump was just using the right thing for each step. Not the model. Kind of anticlimactic really.