JSON mode helps a model return valid JSON. Structured Outputs go further by constraining the response to a developer-supplied schema. In production, that difference matters because downstream systems usually need typed fields, required properties, and predictable shapes.
This is still an LLM output-shape guide. If the product is a typed decision (route this ticket, yes/no this guardrail) rather than generated JSON, start with Route Tickets with TypeSafe Jev instead.
Use JSON mode for loose formatting
JSON mode is enough when the application only needs parseable JSON and can tolerate flexible fields. It is useful for quick prototypes, logging, or simple transformations where strict validation is not required.
Use Structured Outputs for contracts
Use Structured Outputs when a workflow depends on a specific object shape: extraction fields, function-call arguments, UI state, routing decisions, eval grader output, or API payloads. Pair the schema with application-side validation before taking action.
Gemini 3.8 Flash lists structured outputs as supported on the generate-content card. Gemini 3.8 Live does not. Do not expect a Live voice SKU to return schema-constrained JSON.
Jev is not JSON mode
Jev (jev-latest) is TypeSafe's System One decision model. You send state plus Choice / Score / Noul questions. You get typed answers and probabilities. TypeSafe's own FAQ treats this as a different job from LLM JSON mode or Structured Outputs.
Use Jev when code must branch on a closed option set (up to 255 Choice options) with calibrated confidence. Use Structured Outputs when you still need an LLM to write, extract long nested objects, or call tools as part of generating text.
Schema matching on Jev means the model cannot emit an undeclared Choice option. It does not mean the option is factually correct. Same rule as LLM Structured Outputs: validate semantics in application code.
Validate semantics separately
Schema validity does not prove the model is correct. A field can have the right type and still contain the wrong date, unsafe recommendation, unsupported citation, or invalid business decision.
Handle refusals and truncation
Production code should handle refusals, max-token stops, schema limitations, and provider-specific constraints. For high-risk actions, validate values and require approval rather than assuming a schema-valid response is safe.
Official sources
- OpenAI Structured Outputs announcement: https://openai.com/index/introducing-structured-outputs-in-the-api/
- OpenAI function calling documentation: https://platform.openai.com/docs/guides/function-calling
- TypeSafe Jev introduction: https://docs.typesafe.ai/introduction.md