Use GPT-5.6 Luna in Tabbit
Use in Tabbit GPT-5.6 Luna
Featured prompts
The 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。
Get 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 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。
Reddit 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 。
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。
Reviews and field notes
GPT-5.6 Luna Benchmarks & Pricing (Public Benchmarks & Pricing)
BenchLM rates GPT-5.6 Luna at 67.3/100, ranking it 23 among 218 models. Its strongest area in the public evidence is Coding: 73.0 points, 6/135; Agentic ranks 43/130. The page also lists $0.20 per million input tokens, $1.20 per million output tokens, and cach。
GPT-5.6 Luna Is Really Underrated: Codex User Experience
The poster believes that GPT-5.6 Luna, at medium thinking, is close to GPT-5.4 mini in speed and capability, and can approach GPT-5.5 medium at high effort. In the comments, one user set Luna Max as the default model, while others considered Luna's quality and。
Thoughts after using GPT-5.6 Luna for 48 hours
This 48-hour experience report contrasts with the positive reviews from the Codex community: using GPT-5.6 Luna high in Hermes agent for personal-assistant tasks, the author found it “smart but slow,” prone to repeated iteration, quick to consume quota, occasi。
GPT-5.6 Luna Max vs. Sol Medium: An X User's Real-World Cost Test
Google's index summary for X shows that the author compared GPT-5.6 Luna Max with Sol Medium, saying that Luna used more tokens but averaged about $1.20 per session, while Sol averaged about $29. This result is useful as a community-tested lead suggesting that。
GPT-5.6 Luna Semgrep IDOR Security Benchmark and Cost per True Positive
One-sentence takeaway Semgrep's security evaluation found that Luna costs roughly 6 times less per true positive than heavier models, with only a marginal F1 sacrifice; however, precision and recall are strongly dependent on the harness, so raw-model scores mu。
OpenAI
Use GPT-5.6 Luna in Tabbit
Explore GPT-5.6 Luna model routing, prompt work contracts, cost benchmarks, and coding reports for frequent, verifiable tasks.