Structured Outputs vs Function Calling
Structured outputs make a model's response conform to a schema, typically JSON Schema, so downstream code can parse it. Function calling also produces structured data, but as a request to run a specific tool with arguments, after which the result goes back to the model and the conversation continues.
Definitions
- Structured Outputs PTL-0062
- Structured outputs constrain a language model to produce responses that conform to a specified format, typically a JSON Schema, either through instructions or through constrained decoding that guarantees validity.
- Function Calling PTL-0060
- Function calling, or tool use, is a model capability in which the model outputs a structured request to invoke a developer-defined function with arguments, which the application executes and returns as a result to the model.
Differences
| Structured Outputs | Function Calling | |
|---|---|---|
| Purpose | Shape the final answer | Ask the application to take an action or fetch data |
| What happens next | Your code consumes the output | The application runs the tool and returns the result to the model |
| Guarantee | With constrained decoding, the output is always valid | Arguments follow the tool's schema; validity depends on the provider |
Which to use
- Use structured outputs when you need the answer itself in a fixed format.
- Use function calling when the model needs to act or gather information before answering.
Sources
- OpenAI (2024). Structured model outputs.
- Willard & Louf (2023). Efficient Guided Generation for Large Language Models.
- Anthropic (2024). Tool use with Claude.
- Patil et al. (2023). Gorilla: Large Language Model Connected with Massive APIs.
Cite this page
Protologue. (2026). Structured Outputs vs Function Calling. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0). https://protologue.com/compare/structured-outputs-vs-function-calling/