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A Score question rates a situation on an ordered scale you define. It returns a discrete score (the winning level index) plus expected_level, a probability-weighted continuous estimate across all levels, per-level probabilities, and a legend echoing your level descriptions back by index. Use a Score when the answer is a position on a scale with defined levels - urgency, severity, sentiment intensity - rather than an unordered category.
Score vs. Noul: if you just need a binary gate, use Noul instead - it’s simpler and returns a single probability rather than a distribution over levels.

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Request

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string
required
Always "score".
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string
required
The question to evaluate, in natural language.
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string[] | object
required
An ordered array of level descriptions, index 0 is the lowest level: ["low urgency, can wait", "medium urgency, handle soon", "high urgency, handle immediately"].Also accepts an object with numeric-string keys (e.g. {"1": "...", "3": "...", "5": "..."}) - keys are sorted and then discarded, and the response is always re-indexed 0..N-1 regardless of the keys you used.Supply between 1 and 255 levels.

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Answer object

The matching entry inside answers is:
  • score - a discrete integer index, the argmax level.
  • expected_level - Σ(level × probability), the continuous, probability-weighted position across all levels. Use it when you want finer-grained thresholds than the discrete index provides.
  • legend isn’t generated by the model - the API builds it from your own criteria, indexed.
  • confidence - top1 − top2, same formula as Choice, from 0 (two adjacent levels tied) to 1 (one level has ~all the probability mass). See confidence and uncertainty for how to act on it.

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Using the result