A reasoning model can appear to return a bad or empty answer when your application treats every response as a complete JSON object. Before blaming the model, inspect the raw response, check whether generation finished, and handle refusals separately. Then parse and validate the content that remains.
Why a response can look like a bad answer
“JSON” can mean two different things in an API integration: syntactically valid JSON, or output that also conforms to a schema your application supplied. Those are different guarantees. OpenAI describes JSON mode as a way to obtain JSON-formatted output; when the application needs the response to match a schema, OpenAI recommends Structured Outputs when supported for the use case. OpenAI’s structured-output guide explains the distinction.
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Neither guarantee means that every response is a complete business object. A refusal is a distinct outcome, and generation can stop before the object is complete. The Structured Outputs documentation notes that refusals and output cut off at the token limit may not match the supplied schema. JSON mode can also have edge cases that produce an incomplete object. A parser that expects a complete object in every case may therefore report a parsing error, an empty value, or a downstream validation failure without telling you which response state caused it.
Diagnose the response before parsing it
- Inspect the raw response envelope. Log or otherwise examine the API response before relying on an SDK’s parsed object. Check the completion state and any refusal indicator exposed by the API. SDK parsing helpers are convenient, but they do not replace handling response states. See OpenAI’s SDK and structured-output guidance.
- Check whether generation completed. Do not send content marked incomplete to a parser as if it were a finished object. OpenAI’s JSON-mode examples call out length truncation and content filtering as conditions to detect. The relevant status and field names depend on the API mode; follow the documentation for the endpoint you use. OpenAI’s JSON-mode guidance covers these cases.
- Handle refusal as its own branch. If the response indicates a refusal, return or record that outcome through an explicit application path. Do not try to coerce refusal content into the expected business object. OpenAI documents a refusal signal for Structured Outputs so applications can identify and handle this case. See the refusal guidance.
- Parse only completed, non-refusal content. Use the SDK parsing helper or JSON parser appropriate to the API, then validate requirements that your schema cannot express, such as business rules or cross-field consistency. Schema conformance is not the same as application-level correctness.
- Record the outcome by category. Keep incomplete responses, refusals, parse errors, schema mismatches, and application-validation failures distinct in logs and metrics. This makes the failure mode visible instead of collapsing every case into “bad model answer.”
Choose the output mode for the guarantee you need
| Approach | What it is for | What your application still needs to handle |
|---|---|---|
| JSON mode | JSON-formatted output; it does not by itself guarantee conformity to a supplied schema. | Detect incomplete output and other documented edge cases, then parse and validate the result. |
| Structured Outputs | Output constrained to match a supplied schema when supported for the use case. | Handle refusals and interrupted or incomplete output; validate application-level rules beyond the schema. |
OpenAI recommends Structured Outputs rather than JSON mode when schema matching is needed and the feature fits the use case. Its guarantee has conditions: the response must not be a refusal or prematurely interrupted. In its announcement, OpenAI wrote: “When the response does not include a refusal and the model’s response has not been prematurely interrupted (as indicated by finish_reason), then the model’s response will reliably produce valid JSON matching the supplied schema.” That statement describes the documented feature under those conditions; it is not a guarantee about refusals, interrupted responses, every provider, or whether the result satisfies your business logic. OpenAI’s Structured Outputs announcement.
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Make the schema and token budget work together
Write schemas for the model and the application
Use clear, intuitive key names and descriptions for important fields. Keep the schema focused on the task, and evaluate whether its structure produces usable results for your application. These are OpenAI’s recommendations in its structured-output documentation. A schema can enforce shape, but it cannot automatically establish that a value is sensible or that the object meets every downstream requirement.
Allow for more than visible answer text
For reasoning models, a generation’s token budget may cover reasoning tokens, visible output, and non-visible formatting tokens—not just the JSON your application eventually reads. An output limit that leaves too little room for the full generation can lead to an interrupted response. Account for the complete generation when setting limits, and treat an incomplete status as a reason not to parse the content as a finished object. See OpenAI’s reasoning-model guidance.
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When the integration uses another provider
Anthropic also documents structured JSON output, but provider behavior should not be assumed to match OpenAI’s schema support, refusal signals, or interruption semantics. Verify the current endpoint and SDK documentation for the provider you use before implementing provider-specific status checks or relying on a particular schema feature. Anthropic’s structured-outputs documentation.
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