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Reasoning controls are model and protocol capabilities, not one universal provider option. Polyglot represents caller intent with ReasoningSelection and translates it only at the provider request-body boundary.

Named Effort

The portable effort vocabulary is minimal, low, medium, high, xhigh, and max. This vocabulary is a superset, not a promise that every model accepts every value. Polyglot checks the selected model’s curated capability profile and rejects unsupported or lossy mappings before sending the request.

Other Selection Kinds

Providers expose several distinct reasoning controls, so the unified type is a tagged selection rather than a nullable effort string:
providerDefault() is non-invasive: Polyglot emits no reasoning field, so existing raw options and provider defaults remain unchanged. An explicit typed selection conflicts with raw keys such as reasoning_effort, reasoning, or thinking; remove the raw key before using withReasoning().

Curated Capability Matrix

Capabilities are resolved for the model and protocol together. Unknown model IDs fail closed for explicit reasoning selections; Polyglot does not infer support from a provider name or a model-name substring. DeepSeek documents medium and xhigh as aliases that behave like high. Polyglot records those as lossy mappings and does not accept them by default; callers can select high explicitly and retain truthful effective intent.

Capability Inspection

Inspect reasoning metadata on the exact (driver, wire model) profile:
A missing exact pair returns an unknown profile, so known is false. That is not a local rejection: explicit reasoning can proceed when the driver can render it without model-specific data. A wire format that needs an exact effort mapping fails with a missing-translation-fact error unless the caller supplies the facts through an explicitly composed catalog or directly on the request with withReasoningCapabilities(). Polyglot never inherits them from a provider or a similarly named model. Lossy effort mappings are rejected by default. Applications that accept a known semantic change can set allowLossyFallback: true on LLMConfig; framework connection configuration exposes allow_lossy_fallback. The effective request records every accepted adjustment so it can be observed before transport or in the InferenceRequested event.