Bias Toward Action Operator Preamble
Generate an action-oriented assistant prompt focused on task completion and minimal clarification requests, outputting a structured report of actions and assumptions.
GPT-6 Luna
LLMGPT-6 Luna is the fast, high-volume tier of OpenAI's GPT-6 family, released 22 September 2026 alongside Sol and priced 50% below its GPT-5.6 predecessor. It is built for repeatable work at scale: classification, extraction, labelling and short replies, with reasoning that can be switched off entirely for the lowest latency. Free and Go users get it in the desktop app.
How to use GPT-6 Luna
Set model to gpt-6-luna and reasoning_effort to none for the fastest, cheapest turns, or low when a field must be inferred. Use strict tools or json_schema output so results are machine-safe. Drop temperature and top_p when reasoning is on. Batch independent records and run them in parallel.
Best for
High-volume classification, extraction, tagging, routing and short-form generation; first tier in a two-tier setup where hard cases hand off to Sol or Astra; free-tier users who want GPT-6 behaviour in the desktop app.
How it behaves
Optimised for speed and volume. Supports reasoning_effort none, where all judgment must live in the instructions and the output should be a strict function call or JSON schema. Function calling on Chat Completions works only at none; use the Responses API for reasoning with tools. Follows instructions tightly and inherits the GPT-6 bias toward action.
Tips
Write the label boundaries into the system prompt and force a strict function call so the code can trust the output. Ask for a short evidence quote alongside every label as an audit trail. Keep effort at none or low for copy-and-extract work and escalate the uncertain cases to Sol.
Pricing
Included for Free and Go users in the ChatGPT desktop app, and in paid plans. API prices are 50% below GPT-5.6 promotional pricing; see the OpenAI pricing page for current per-token rates.
Prompts for GPT-6 Luna
See all 7Concrete Draft Before Clarifying Questions
Generate a complete working draft before asking clarifying questions, ensuring assumptions are clearly marked.
Batch Extraction Schema for Repeatable Work
Generate structured JSON output for data extraction from specified document types, ensuring accurate field replication.
High-Volume Classification Run With No Reasoning
Generate consistent, high-volume labels for records without reasoning, using structured function calls.
User Instruction Precedence Over Loaded Skills
Defines instruction precedence for a workspace assistant in interactions within a specified workspace.
Plain Paragraph Output Policy
Generate clear and concise response templates through structured writing rules, ensuring clarity in discussions.