Guardrails only work when they return a decision, not a paragraph. GPT-6 Luna Decisions is an OpenAI decision model served by Decisions API that reads shared text, JSON, or images and returns typed answers with probabilities — the exact shape a safety layer needs to approve, block, or escalate without parsing a word of prose.
Three answer types map cleanly onto guardrail patterns. Noul answers yes/no policy questions with a probability: is this claim supported by the citation, is this request allowed, is this output safe to ship. Choice returns a named verdict plus a distribution across up to 255 options, for moderation categories and risk classes. Score places an input on an ordered 2-to-10-level scale for severity and confidence thresholds. Your middleware reads the number and acts: high-confidence pass, low-confidence human review, clear fail blocked.
Verification workloads are the signature use case. Citation verification becomes a Noul question per claim, with the source as text or an image and the claim as JSON — and the platform example of a duplicate-charge complaint routed to billing at 96 percent probability shows the same machinery applied to LLM guardrails and fraud-adjacent triage. Input supports up to 4 images per call, so screenshot-based safety checks ride the same endpoint.
The contract is documented and was verified against OpenRouter in October 2026 (255 options accepted, 256 rejected), with answers read from data.result.answers via /v1/systemone and up to 8 questions batched per request. Pricing is published: $0.10 per million input tokens upstream, 1500 credits ($0.15) per million platform-side, one-credit minimum, 100 welcome credits, and failed calls release their reservation.
If you are wiring guardrails into an LLM stack, GPT-6 Luna Decisions gives you probabilities you can set thresholds against — not vibes you have to parse.
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