Classification is where most automations quietly break, and GPT-6 Luna Decisions is built to fix exactly that. Instead of parsing free-text LLM output, you send shared text, JSON, or images to this OpenAI decision model and receive a Choice answer: one named option plus a probability distribution across every option you defined, from 2 to 255 per question.
That shape changes how you engineer routing. A support request arrives — text body, account JSON, maybe a screenshot attachment — and GPT-6 Luna Decisions returns billing with 96 percent probability and a ranked list of alternatives. Your workflow routes on the winner and escalates on ambiguity, using the numbers rather than guesswork. The same loop powers content classification, intent detection, lead qualification, and agent handoff.
Requests go through the Jev playground and the /v1/systemone endpoint hosted by Decisions API, an independent aggregator of decision models. Up to 8 questions can ride in a single call, each with its own options, and a 1,050,000-token context window means long tickets and large payloads fit without chunking. Multimodal input covers up to 4 images per request, so classification over screenshots or scanned documents needs no separate OCR step.
The contract is explicit and was verified against OpenRouter in October 2026: 255 options accepted, 256 rejected, 2-to-10 Score levels for rubric questions, and Noul for yes/no probability checks. Platform pricing is 1500 credits ($0.15) per million input tokens on top of the $0.10 upstream rate, with a minimum of one credit and a 100-credit welcome grant for new accounts. Failed calls release their reservation.
If your pipeline needs decisions it can log, audit, and act on, GPT-6 Luna Decisions gives you the probability distribution that regex-wrapped chat models never had.
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