GPT-5.6 Luna prompt guide
Coding and agents
5 source-checked resourcesReddit Codex: Multi-Model Routing Configuration for Luna Subagents and Sol Review
This is not an official configuration, but a personal routing setup shared by a Codex user: use Luna for routine implementation, brainstorming, and subagents; use Sol high for plan reviews and final decisions; and use a fast model for test execution. It turns 。
Apply Occam’s Razor: Reducing Overengineering in Luna/Codex Prompts
The poster recommends explicitly asking Codex to use Occam’s Razor to reduce Sol’s overengineering; the rule can likewise serve as a boundary-setting prompt when Luna handles coding tasks. Comments add important limitations: simplicity does not mean removing m。
Reddit Codex: Diagnostic and Verification Prompt for Luna Subagent Compatibility
The post discusses a compatibility issue between Sol/Terra's Multi-Agent V2 and Luna V1 in Codex. The original post provides a complete prompt to "inspect, back up, modify, validate, and report," requiring the user to check the actual versions and model catalo。
Use Cheap Luna to Orchestrate Threads: Use Threads Rather Than Same-Model Subagents
The author recommends using Luna as a thread orchestrator, letting different threads choose Sol, Terra, or Luna for each task instead of having Sol Ultra automatically generate a batch of equally expensive subagents. The original prompt is very short. Its core。
X: Create a Custom luna_worker Agent for Codex
The X indexed snippet presents a small but executable Codex agent configuration idea: have Sol create a custom agent named lunaworker, set its model to gpt-5.6-luna, and then have that agent take on subagent tasks. The second snippet asks you to preserve the e。
Reasoning and thinking
3 source-checked resourcesThe Builder's Guide to GPT-5.6: Luna's Model Selection, Agent Orchestration, and Caching
OpenAI's official builder's guide does not offer a single “universal prompt.” Instead, it recommends designing workflows around model routing, reasoning-effort levels, preserving reasoning, native compaction, multi-agent systems, programmatic tool calling, and。
GPT-5.6 Prompting Guide: Luna's Work Contract and Model Routing
This long-form article for marketing, writing, and development workflows reframes “prompting techniques” as a work contract: define Goal, Context, Output, Boundaries, and Completion check, then choose Luna, Terra, or Sol according to task risk. It recommends L。
X: Route Lightweight Tasks to GPT-5.6 Luna with fast_scan
The Google index snippet for the X article “How To Use GPT-5.6 All Day Without Hitting Limits” shows a lightweight workflow named fastscan, intended for quick searches, codebase exploration, file reads, and lightweight analysis. It uses the gpt-5.6-luna model 。
API and integration
3 source-checked resourcesGet Started with OpenAI GPT-5.6 on Amazon Bedrock: Reasoning, Tool Calling, and Caching
AWS's article positions Luna as a high-throughput, low-latency model for classification, summarization, and routing, and uses Responses API examples to show how to set reasoning effort, call tools, carry the model's output in full into the next turn, and cache。
GPT-5.6 Luna API Model Parameters and Cost Configuration
One-sentence takeaway The official model page confirms Luna's current API ID, pricing, reasoning tiers, tool surface, and rate limits. It can serve as a configuration baseline for high-throughput routing, but a single request above 272K tokens incurs a surchar。
GPT-5.6 Luna Low-Risk First-Pass and Escalation Routing Workflow
One-sentence takeaway Put Luna on low-risk first-pass work such as summaries, labels, lightweight review prechecks, and scaffolding, then escalate failed or complex tasks to Terra/Sol. This is CodeRabbit's reusable routing recommendation for three tiers of cod。
General methods
1 source-checked resourcesGPT-5.6 Luna
Use in Tabbit
Curated prompt patterns, model settings, and practical examples for GPT-5.6 Luna, with the original source attached to every entry.