---
title: Architectural Differences in Language Models Explained
url: https://www.dataloco.com/en/architectural-differences-in-language-models-explained
published: 2026-09-16T13:10:29+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/structured-outputs-vs-function-calling-which-should-your-agent-use/
publisher: Dataloco
---

# Architectural Differences in Language Models Explained

A new article discusses the architectural differences between structured outputs and function calling in modern language model systems. The piece targets machine learning practitioners who are developing autonomous agents and software pipelines, emphasizing the challenges posed by unstructured text in these contexts.

Structured outputs and function calling are identified as two primary mechanisms introduced by modern language model API providers. While both methods utilize JSON schemas to facilitate structured key-value outputs, they serve distinct purposes in agent design, which is crucial for achieving reliable performance.

The article highlights the risk of conflating structured outputs with function calling, warning that selecting the incorrect mechanism can result in fragile architectures and increased costs. It details how structured outputs utilize grammar-constrained decoding to ensure high schema compliance, while function calling is based on instruction tuning to handle interactions with external environments.

The text explains that structured outputs are best for data transformation and standardization when all necessary information is available. In contrast, function calling is suited for scenarios requiring dynamic decision-making and external data fetching.

The implications of choosing between these two methods extend to performance, latency, and overall cost-effectiveness when deploying agents in production. The article also touches on hybrid approaches, where the boundaries between structured outputs and function calling may blur, allowing for more versatile agent designs.

The piece concludes with a decision-making framework for practitioners, advocating for careful consideration of function calling as a powerful but potentially unpredictable tool while positioning structured outputs as the foundational component of modern AI data pipelines.