OpenAI’s new GPT-6 guide compares model roles and recommends production practices for prompts, costs, long-running tasks and tool use.
OpenAI published a practical guide on October 2, 2026, for selecting and deploying models in its GPT-6 family. It distinguishes three models by intended workload: GPT-6 Astra for demanding reasoning, GPT-6.1 Sol for complex coding, research and computer use, and GPT-6 Luna for focused, repeated work at scale, such as extracting invoice fields or producing structured summaries.
The guidance frames model selection as a balance among capability, cost and response time. Developers can also adjust reasoning effort in the API, from low for routine extraction or small edits to higher levels for analysis, debugging and careful review. OpenAI advises testing whether higher effort improves results enough to justify added time and cost. Its guide also points to faster processing modes for cases where response speed is important, with a price tradeoff.
For production deployments, OpenAI recommends measuring task success, latency and cost per successful task on representative workloads before release. It highlights prompt caching for recurring context and compaction for longer conversations, along with monitoring and data-control planning. The company says cached input tokens can cost up to 95% less than uncached input, depending on the model; teams are advised to account for cache writes and long-context rates when estimating workflow costs.
The guide also addresses how to structure instructions. It recommends specifying the assignment, relevant context, constraints and what counts as completion, while defining which decisions an agent may make independently and when it should ask for approval. For extended tasks, it describes steering a run with updated instructions, letting work continue while asynchronous tools operate, and delegating independent subtasks where supported. OpenAI says GPT-6 models can take on tasks lasting hours or days, but stresses that teams should set boundaries for clarification and review.
The document presents these recommendations as practical operating guidance, not as benchmark results. Its focus is helping developers match models and workflow settings to particular jobs while monitoring quality, speed and expense.