Practical tips for getting the best results from GPT‑6 models while managing time and cost 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 and compaction 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 , 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 , async tools , and delegation to handle updates and independent work.

…