Your best model probably isn't your best tool caller

Reddit r/AI_Agents 06/17/26, 01:19 AM News

Summary

The article argues that tool-calling reliability often does not scale with model capability; smaller models can outperform larger ones in schema adherence and format discipline, suggesting that raw capability is not the sole factor in choosing a model for tool use.

Saw a 7B model hold a tool schema cleaner than a frontline model last week and it reminded me how little tool reliability tracks raw capability. People reach for the biggest, smartest model for the agent, figuring more capable means more reliable at tool calls. It often goes the other way. Tool calling is mostly format discipline, holding a schema and not improvising fields, and that is a different skill from reasoning. The bigger model is often more willing to get creative in the exact spot you needed it to stay boring, so it's the one that invents a field or wraps the JSON in prose. So the model topping the leaderboard is answering a different question than the one you're asking when you wire it to tools. Those of you running agents, are you choosing your tool model on capability, or have you measured valid call rate per model on your own tools?

Original Article

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