GPT-5.6 Luna

Luna is the fast, inexpensive tier of the GPT-5.6 line — built for volume, latency-sensitive interfaces, and the parts of a system where a model call should feel instant.

Its role is structural rather than headline: the model you call a hundred times in a pipeline, not the one you call once for something important.

Latency is the feature. In anything a user is waiting on — autocomplete, live suggestions, inline classification — a fast adequate answer beats a slow excellent one, because the slow one arrives after the user has moved on.

It is also the right tool for pre-processing: deciding whether a request needs a bigger model, extracting fields, normalising messy input. Used this way it reduces total system cost rather than adding to it.

Reasoning depth is limited and it degrades on long context. Give it a complex multi-constraint problem and you get fluent, confident, wrong output — which is more dangerous than an obvious failure, because it passes a casual read.

It needs tighter prompts than larger tiers. Ambiguity that Sol would resolve sensibly, Luna resolves arbitrarily.

Use Luna where speed is the product and the task is well-specified. Do not use it for anything where a plausible wrong answer causes harm, and do not use it as a general fallback — pair it with a larger model behind a routing decision instead.