Structured Outputs vs. Function Calling: Which Should You...

What’s Happening

Breaking it down: Language models (LMs), at their core, are text-in and text-out systems.

Function Calling: Which Should Your Agent Use? By Matthew Mayo on in Language Models 0 Post In this article, you will learn the architectural differences between structured outputs and function calling in modern language model systems. (wild, right?)

Topics we will cover include: How structured outputs and function calling work under the hood.

The Details

When to use each approach in real-world ML systems. The performance, cost, and reliability trade-offs between the two.

Image by Editor Introduction Language models (LMs), at their core, are text-in and text-out systems. For a human conversing with one via a chat interface, this is perfectly fine.

Why This Matters

But for ML practitioners building autonomous agents and reliable software pipelines, raw unstructured text is a nightmare to parse, route, and integrate into deterministic systems. To build reliable agents, we need predictable, machine-readable outputs and the ability to interact seamlessly with external environments. To bridge this gap, modern LM API providers (like OpenAI, Anthropic, and Google Gemini) have introduced two primary mechanisms: Structured Outputs: Forcing the model to reply to a predefined schema (most commonly a JSON schema or a Python Pydantic model) Function Calling (Tool Use): Equipping the model with a library of functional definitions that it can choose to invoke dynamically based on the context of the prompt At first glance, these two capabilities look similar.

This adds to the ongoing AI race that’s captivating the tech world.

Key Takeaways

• But, they serve fundamentally different architectural purposes in agent design.
• Conflating the two is a common pitfall.
• Choosing the wrong mechanism for a feature can lead to brittle architectures, excessive latency, and unnecessarily inflated API costs.

The Bottom Line

Conflating the two is a common pitfall. Choosing the wrong mechanism for a feature can lead to brittle architectures, excessive latency, and unnecessarily inflated API costs.

What’s your take on this whole situation?