OpenAI Models in Practice

Overview

The right model for the work.

This report develops a practical framework for selecting models and coordinating mixed-model workflows. It reviews official OpenAI documentation and inspects RSB’s codex-agent-tree design, separating model diversity, parallel execution, and independent review. The focus is time and cost per accepted result, including tool use, coordination, verification, and rework.

What the report examines.

• Match model capability and reasoning effort to the task and cost of errors.
• Measure time and cost per accepted result, including tools, verification, and rework.
• Use routing, cascades, bounded workers, and independent review selectively.
• Compare mixed-model workflows with strong single-model baselines using consistent acceptance criteria.

A documented design. An open question.

The inspected RSB design assigns Sol to integration and worker roles, with Astra preferred for consequential independent review. It documents bounded tasks, resource ownership, and one accountable integrator. These are design choices, not evidence that this allocation improves speed, cost, acceptance, or defect rates. A Luna routing branch is a proposed extension.

This is a first-party engineering analysis and prospective evaluation protocol. No comparative benchmark was executed, and the report claims no measured speed or cost advantage. Its numerical examples are hypothetical. Model and service details are dated to 3 October 2026; the report does not establish current availability or peer-review status.

The next research questions.

The report proposes controlled comparisons of single-model, parallel, and mixed-model configurations. A future pilot would measure acceptance, escaped defects, latency, cost, and rework using fixed checks and blinded review. The proposed pilot has not been run.