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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUse function calling when a model needs to invoke an application capability, retrieve external data, or take an action through your system. Use Structured Outputs with a JSON Schema response format when the model’s answer should follow a predictable structure for your app to process or display. They are not mutually exclusive: Structured Outputs can also constrain a function’s arguments.
What is the difference?
The key difference is the purpose of the structured payload—not whether it is JSON. Function calling connects the model to functions your application provides. A structured response format shapes the answer the assistant returns to the user.
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| Decision | Function calling | Structured response format |
|---|---|---|
| Use it when | The model should select or invoke an application capability, access outside data, or trigger an action. | The assistant should answer the user in a predictable format for downstream parsing or UI rendering. |
| What the model produces | A tool call containing a function name and arguments; your application handles the call and may return its result to the model. | A response that follows a supplied, supported JSON Schema when Structured Outputs is enabled. |
| What you define | A function and its parameter schema. | The schema for the response object your application needs. |
| What to ask | “Should the model invoke one of the capabilities I provide?” | “Should the answer itself have a predictable structure?” |
OpenAI describes function calling as a way for models to interface with external systems and access data outside their training data. A JSON Schema response format, by contrast, is for structuring the assistant’s response. See the function-calling guide and Structured Outputs guide.
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Define a function tool when the model needs a capability your application controls—for example, looking up a record in your system or initiating an action. The model can select a tool and provide arguments, but your application is responsible for handling the call and deciding what happens next.
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Choose how tools may be selected
Tool choice determines whether the model can skip calling a tool, must call one, or is directed to a particular tool. For example, auto lets the model decide whether and which available tool to call, while required or forced choices narrow that behavior. Exact option names and shapes depend on the API surface, so check the reference for the endpoint you use.
Validate before execution
Do not treat generated arguments as trusted application input. The API reference cautions that function arguments may be invalid JSON or include parameters not declared in the schema. Parse and validate them against your application’s expectations before executing the function. See the Chat API reference.
When should you use Structured Outputs?
Use a JSON Schema response format when the model is answering the user and your application needs the answer to fit a known object shape—for instance, to render fields consistently or pass them to downstream code. This is the right distinction when your question is not “which function should the model invoke?” but “what structure must its answer have?”
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Both Structured Outputs and JSON mode can return valid JSON, but only Structured Outputs is designed to adhere to a supported schema. JSON mode alone does not guarantee that required keys, types, or enum values match your intended shape. If downstream code relies on those constraints, prefer Structured Outputs on a compatible model and test the schema you actually plan to use. OpenAI recommends Structured Outputs where supported; see its guide.
Can you use both?
Yes. Function calling and Structured Outputs are complementary: a function tool has parameters, and Structured Outputs can constrain those arguments. The choice is about what the structured data is for. Use a response schema for the assistant’s user-facing answer; use a function tool when the model needs to connect to an application capability. When the model must both call a function and produce a structured answer, design and handle each part of that flow explicitly.
What strict mode requires
Strict mode can enforce adherence to a function’s parameter schema, subject to the supported JSON Schema subset and its constraints. OpenAI documents requirements for strict function schemas that include setting additionalProperties to false and making all properties required. To represent a value that may be absent, use a nullable type rather than omitting that property from the schema. Check the current supported-schema guidance before depending on more complex constructs.
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These constraints are relevant to function-tool arguments; they do not remove the need to handle tool calls safely in your application. See the function-calling guide and Structured Outputs guide for current schema details.
Handle refusals and incomplete responses
Schema adherence does not mean every request produces a usable object. A refusal may not follow the response schema, and an output may be incomplete. Check the refusal indication and response completion status before passing parsed data to code that assumes a complete, schema-conforming result. See the Structured Outputs guide.
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A practical decision process
- Identify the job. If the model needs to retrieve data or trigger a capability, define a function tool. If it only needs to return a structured answer, define a response schema.
- Choose the right constraint. For function tools, consider strict mode when your schema fits the documented requirements. For user-facing structured answers, use Structured Outputs rather than relying on JSON mode for schema adherence.
- Plan runtime handling. Validate tool arguments before execution. For structured responses, handle refusals and incomplete output before consuming the object.
- Check the endpoint documentation. Tool-choice options, supported models, and schema support can vary or change; confirm the current reference for the API surface you are using.
OpenAI’s documentation was checked on October 7, 2026. Because supported models, schema features, and endpoint behavior can change, verify the current Structured Outputs, function-calling, and Chat API reference pages before relying on implementation details.
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