How to Choose a GPT-6 Model for Business and Prepare for Launch

Match the model to the task

OpenAI recommends these starting points for GPT-6 model selection. They are vendor recommendations, not guarantees of quality on your data:

• GPT-6 Luna — repeated tasks with clear goals: extracting invoice fields, classifying requests, and producing structured summaries.
• GPT-6.1 Sol — complex coding, research, and working with applications.
• GPT-6 Astra — the hardest reasoning tasks, including difficult debugging and careful review.

Compare cost per successful task, not just model pricing. Before testing, define a correct result, acceptable response time, and unacceptable errors.

Start with a separate test

To integrate through an application programming interface (API), you need an OpenAI account, an API key, and an application that sends requests. Ask your developer to:

1. Create a separate test project in the OpenAI dashboard. Store the key in an environment variable or secret management service, not in code or a public repository.
2. Send a request through the Responses API, setting model to gpt-6-astra, gpt-6.1-sol, or gpt-6-luna. Describe the desired result, input data, constraints, and what counts as done.
3. Run the same representative tasks on suitable models. Record the success rate, response time, and cost per successful result.

Also configure reasoning.effort, which controls how much effort the model spends reasoning. OpenAI recommends low for routine work, medium for comparing options, and high for complex analysis. Increase it only when testing shows a useful improvement.

Prepare the live service

Specify which decisions the model can make independently and which require approval. For example, let it organize a summary, but require approval before changing the project’s scope. Review instruction files the model can access: conflicting guidance can affect its actions.

For repeated requests, use caching to reuse shared input. Put stable instructions and reference materials before changing task details. Include cache writes and long-context rates in cost estimates; fixed savings cannot be promised.

A key migration limitation: Astra and Sol do not support reasoning.effort: none. Use the Responses API for tool calling. Check budget controls, too: a hard spend limit stops affected API traffic when tracked spending reaches the threshold.

Next, agree on launch criteria based on test results. Before onboarding customers, keep the production project separate from the test project, set up quality and cost monitoring, and review data-handling rules for your application.