Structured output constrains a model to produce machine-readable formats like JSON matching a schema, enabling reliable integration with software.
Free-form text is fine for humans and hopeless for pipelines. Structured output features range from a JSON mode that guarantees syntactically valid JSON to full schema enforcement, where constrained decoding makes it impossible for the model to emit a token that would violate the supplied JSON Schema.
Schema enforcement eliminated a whole class of parsing failures and retry loops that early LLM applications suffered, and it underpins tool calling, extraction pipelines, and agent frameworks. It shapes content only partially: the JSON will be valid, but values can still be wrong, so validation logic keeps its job.
On cost, structured output is usually a net saving: schemas and format instructions add input tokens, but eliminating malformed-output retries — each a fully billed request — more than pays for them in production.
Last revised 2026-07-05 · All glossary terms → · Live AI model pricing →