GPT-6 Luna

What is GPT-6 Luna?

GPT-6 Luna is OpenAI’s model for focused, high-volume tasks. Through OpenRouter, it is a candidate for turning supplied source documents into a draft, classifying messages or producing structured text. Give it clear evidence and acceptance rules; a low token price does not remove the need to check factual claims.

Use a narrow brief for repeated work

OpenAI describes Luna as an efficient model for focused tasks. Our suggested editorial use is a first draft built from a small set of approved sources: define the reader’s question, supply the facts and require uncertainty to remain visible. This is a starting point for review, not evidence of independent reporting.

For classification, provide a fixed set of labels and examples of borderline cases. Ask for the chosen label and a short reason, then evaluate against records someone has already classified. This reveals whether the model follows your categories rather than merely producing a plausible answer.

Suggested workflow: prepare a sourced profile

Collect the official overview, relevant documentation and current pricing conditions. Decide what the reader needs to accomplish, then request a short introduction, practical uses, a starting workflow and concrete limitations. Ask the model to connect material claims to the supplied evidence.

Check source support and missing qualifications before editing the style. Send uncertain claims to a separate reviewer or remove them. Record total usage for the attempt and its correction, so your cost measure reflects a finished draft rather than a single response.

API cost and integration details

The OpenRouter identifier is openai/gpt-6-luna. At the October 17 check, both the model page and OpenAI’s standard text pricing listed $0.10 per million input tokens and $0.50 per million output tokens. Long-context tiers, caching, tools and service options can alter the bill; check the current route before budgeting.

OpenAI documents a specific restriction for its Chat Completions API: function calling requires reasoning effort none. Its Responses API has different tool support. Check OpenRouter’s endpoint compatibility rather than transferring every direct-API feature to your integration.

When to escalate a task

Keep a harder case for comparison with a higher-priced reasoning model, such as Claude Opus 5.5: a source conflict, an ambiguous requirement or a patch that fails its tests. Escalate based on the failure you observe rather than sending every task to the more expensive model.