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Reasoning models and structured outputs flashcards
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- Front
- When use a reasoning model and when not?
- Back
- Use for hard multi-step tasks where errors are costly and latency is tolerable. Prefer a fast model for extraction, classification, FAQ and tight latency targets. Decide by measuring on your own tasks.
- Front
- Does schema-constrained output guarantee correctness?
- Back
- No. It guarantees form, not truth. Still handle truncation and refusals, validate semantics, retry with the error a bounded number of times, and have a fallback.
- Front
- What do reasoning tokens cost, and are they visible?
- Back
- They are billed as output tokens and count toward the output limit, even when the API does not return them. Watch the reasoning-token count in usage data.
- Front
- Can you trust a reasoning model's visible reasoning as an audit trail?
- Back
- No. In one 2025 study, models mentioned a hint they used only 25% to 39% of the time on average. Treat it as a clue.
- Front
- JSON mode vs schema-constrained structured outputs?
- Back
- JSON mode gives syntactically valid JSON. Schema-constrained outputs restrict decoding to your JSON Schema, so the shape and types conform. Neither makes the values true (on Anthropic, enum capitalisation is also not guaranteed).
- Front
- Tool calling vs structured outputs: how do you choose?
- Back
- They are not a ladder. Use schema-constrained outputs to shape the reply. Use tool calling when the model should act or fetch through your functions. Validate arguments either way.
- Front
- What schema limits and quirks should you plan for?
- Back
- Providers support only a subset of JSON Schema (unsupported features return a 400), Anthropic does not guarantee enum capitalisation, and the first request with a new schema is slower because the schema is compiled.