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How to Make the Most of GPT-6 Series AI Models? OpenAI Releases a Guide to Choosing Models, Optimizing Prompts, and More
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- DeepHorus
OpenAI released a guide to the GPT-6 family of models on October 2, sharing recommendations on model selection, prompt optimization, long-running task management, and production readiness, among other topics. IT Home reported on October 3 that the guide is intended for users who want to turn ideas into runnable prototypes, develop and test features, or orchestrate multi-step workflows spanning code repositories, databases, and external APIs. OpenAI stated that its goal is to help users control time and costs while fully leveraging the capabilities of what is, to date, the most advanced family of models.
Regarding model selection, OpenAI recommends choosing a model according to the workload rather than automatically defaulting to the model with the greatest capabilities. For simple tasks, lightweight models are generally faster and less expensive. GPT-6 Astra is designed for work that requires the highest level of intelligence and handles highly difficult reasoning, while GPT-6.1 Sol is suitable for complex programming, research, and computer-use tasks. GPT-6 Luna is suited to processing specific tasks at scale, as well as to clearly defined, repetitive everyday work, such as extracting fields from invoices, classifying requests, and generating structured summaries.
Prompt optimization is another major focus of the guide. OpenAI believes that, instead of writing out lengthy sequences of steps, users should first explain the desired result, who it is for, and what must not be changed. A more complete input should also specify the relevant context, constraints, and what counts as completion, so that the model can clearly understand the task objective, execution boundaries, and completion criteria.
Regarding authorization, OpenAI recommends that users explain what the model may decide on its own and when it should ask for input or consultation. This approach incorporates both the task requirements and decision-making authority into the prompt, reducing the need to merely list operational steps. The company emphasizes that, rather than requiring the model to execute every step in sequence, users should provide effective instructions sufficient to support it in completing the work.
The choice of reasoning effort likewise needs to be adjusted to the task. OpenAI says that everyday question-and-answer tasks do not need to pursue the highest reasoning effort; more demanding analysis, debugging, or in-depth research is more suitable for an upgrade. The resulting practical principle is to prioritize speed and cost for simple work, use stronger reasoning capabilities for difficult work, and avoid using the highest configuration for every request.
For users, this guide places model capabilities, task difficulty, and cost control within a single selection framework. In practice, users can first clarify the desired result, audience, context, constraints, and completion criteria, and then select the model and reasoning effort according to the workload. For multi-step tasks involving multiple code repositories, databases, and external APIs, OpenAI also recommends paying attention to the management and production readiness of long-running tasks, using a more suitable model, clear instructions, and the necessary investment in reasoning to complete the workflow.
