OpenAI published an official guide on October 2, 2026 for choosing and operating models in the GPT-6 family. It is practical guidance for the already available Astra, 6.1 Sol and Luna models, rather than a new model launch. The useful decision is therefore not which model is universally best, but which combination of model, reasoning level and speed meets a representative workload at an acceptable success rate, latency and cost per successful task. Source
Start with the shape of the workload
OpenAI positions GPT-6 Astra for the hardest reasoning work, GPT-6.1 Sol for complex coding, research and computer use, and GPT-6 Luna for focused repeated work at scale, such as classification or structured summaries. A sensible starting point is the complexity and cost of an error. Test Luna first for high-volume tasks with a clear target, Sol when the job spans code, research or external tools, and Astra when the task genuinely needs the highest level of reasoning. These are OpenAI’s recommended roles, not an independent benchmark or a guarantee of results. Source
After choosing a model, tune reasoning effort separately. The guide gives Low for routine extraction or small edits, Medium for work that needs judgment such as planning or comparison, and High for difficult debugging or careful review. Extra high or Max should be tested only where supported and retained only if the improvement justifies the added time and cost. The API can change reasoning effort during a conversation without breaking cache, but supported settings and current prices still need to be checked in the official model and pricing pages. Source
Treat speed as a separate price decision
Fast mode targets faster and more consistent response times than Standard processing, but carries a higher per-token price. Ultrafast increases generation speed independently of reasoning effort and, according to the guide, is available for GPT-6 Astra in Codex and the API. Either can make sense for latency-sensitive chat or rapid coding loops, but teams should compare end-to-end time and cost per successful task on the same inputs before making a faster mode the default. A speed mode does not automatically lower reasoning effort or guarantee accuracy. Source
For repeated calls, stable instructions and reference material should come before changing task details, while tool definitions should remain consistent so prompt caching can be reused. OpenAI says cached input tokens can cost up to 95% less than uncached input tokens, depending on the model. That is a maximum discount on eligible cached input, not a promise that the total bill for every request falls by 95%. Compaction can reduce context size in longer conversations, while production preparation should also measure task success, latency and cost per successful task and review monitoring and data controls. Source
Design an intervention path for long-running work
In the API, mid-turn steering queues corrections through the Responses WebSocket API; it does not cancel tools already running or undo completed actions. Asynchronous tool calling allows independent work to continue while a slower tool runs, but dependent steps must wait for its result. Multi-agent delegation with GPT-6.1 Sol is described as beta. In Codex, GPT-6 Astra can ask clarifying questions and accept steering while it works, so a workflow should define which decisions may proceed automatically and which ones require user input. Source
Computer use is best reserved for steps that cannot be completed more directly. The guide recommends using an API or connected tool when it can perform the action, and computer use when the model must read a screen, click controls or fill in a form. A practical pre-production check is to replay representative tasks while varying model, reasoning level and speed; record failure conditions and approval boundaries; and verify monitoring and data-control choices before real work is handed over. Source