A model guide for the GPT-6 family

GPT‑6 is our most advanced suite of models yet , and offers you a choice of models for different kinds of work.

Whether you’re turning an idea into a working prototype, building and testing a feature, or orchestrating multi-step workflows across code repositories, databases, and external APIs, this guide explains how to choose a GPT‑6 model, give it effective instructions, manage long-running work, and prepare for production.

TL;DR

- Run effectively in production. Use caching ⁠ (opens in a new window) and compaction ⁠ (opens in a new window) to manage context and cost. Measure task success and latency, and plan for monitoring and data controls.

- Match the model to your workload. Balance capability, cost, and latency by choosing the model, reasoning effort ⁠ (opens in a new window) , and speed that fit the task.

- Adjust your prompts and skills. Keep prompts, skills, and repository instructions consistent about what the model should deliver, what it can do independently, and what counts as done.

- Keep long-running work on track. Use steering ⁠ (opens in a new window) , async tools ⁠ (opens in a new window) , and delegation ⁠ (opens in a new window) to handle updates and independent work. Set clear boundaries for when the model should ask for input.

1. Run effectively in production

Prepare your workflow for production

Before deploying, there are several checks and best practices you’ll want to put into place.

- Keep efficiency in mind. Cut context the task doesn’t need ⁠ (opens in a new window) while keeping the evidence it does. Where your application supports it, run independent tasks together ⁠ (opens in a new window) so one slow step doesn’t hold up unrelated work.

- Reuse shared context through prompt caching ⁠ (opens in a new window) for recurring work. Cached input tokens cost up to 95% less ⁠ (opens in a new window) than uncached input tokens, depending on the model. Put stable instructions and reference material before changing task details, and keep tool definitions consistent. The caching dashboard ⁠ (opens in a new window) and diagnostics guide ⁠ (opens in a new window) help you see where that reuse breaks down. Include cache writes and any long-context rates when estimating the cost of a complete workflow.

- For longer conversations, compaction ⁠ (opens in a new window) reduces context size while preserving the state needed to continue.

- Decide how you’ll monitor behavior ⁠ (opens in a new window) and review the data controls ⁠ (opens in a new window) for your application.

- Test before deploying: Run representative tasks and measure task success, latency, and cost per successful task. Check out our API deployment checklist ⁠ (opens in a new window) .

Match the model to the workload

Think of the model choice and reasoning level as an intelligence/price tradeoff.

- Model:

- GPT‑6 Astra ⁠ (opens in a new window) for the hardest reasoning work where maximum intelligence is needed.

- GPT‑6.1 Sol ⁠ (opens in a new window) for complex coding, research, and computer use.

- GPT‑6 Luna ⁠ (opens in a new window) for focused tasks at scale and everyday, repeated work with a clear goal, such as extracting invoice fields, classifying requests, or producing structured summaries.

When evaluating the best model for the task, compare pricing ⁠ (opens in a new window) for each model.

- Reasoning level: In the API, choose how much effort the model spends on the task.

- Low: Routine tasks, such as extracting facts or making small edits.