Which AI model should you bet your company on?
Most enterprise workloads don’t live at the frontier anyway. Extraction, summarization, classification, document comparison, and customer-service assistance often work perfectly well with smaller, cheaper models. OpenAI’s own pitch for the trio of GPT-5.6 models isn’t simply that Sol is better. It’s that Terra and Luna deliver different combinations of intelligence, latency, and cost. Luna, the cheapest tier, nearly matches the previous generation’s peak performance at less than half the estimated cost, according to OpenAI.
The practical question, of course, is where to start. An enterprise can’t test every model, every reasoning setting, and every price tier before doing any work. So here’s my advice (which I don’t follow in my own work, but I’m not defining enterprise strategy and can be a little price-insensitive). Start with the cheapest credible model that appears capable of the task. Give it a representative set of real examples and, before you start testing, define what counts as good enough. If it passes, stop. If it fails, move up a tier or try a model with strengths better suited to the work.
That sounds almost offensively simple, but it reverses the way many people, including me, use these products. We start with the biggest model because we’re afraid of what we might lose. Enterprises should start lower and require evidence before paying for more intelligence.
