Choosing an OpenAI API Model Without Guessing

OpenAI's model catalog has become a product surface in its own right. The current catalog separates frontier models from image, realtime, audio, embedding, open-weight, and older models. Within the frontier family, it frames GPT-5.6 Sol for the hardest professional work, Terra for a balance of capability and cost, and Luna for high-volume work where efficiency matters. That sounds like a menu, but production teams need a routing rule. Start with the job, not the model name. Use the strongest lane when an error is expensive, the work crosses tools, or the request has long dependencies. Use the balanced lane when quality still matters but the task repeats. Use the efficient lane for extraction, ranking, short summaries, and other bounded work that can be checked. The second rule is to keep the model decision behind a small interface. Store the task class, chosen lane, model version, reasoning setting, latency, cost, and outcome. That makes a model change a measured policy update instead of a code search. The third rule is to test with representative work. A benchmark score cannot tell you whether an article brief keeps the right facts, whether a capture agent preserves provenance, or whether an editor accepts the output without a rewrite. Keep a compact set of real tasks and compare quality, time, and cost before changing the default. The catalog will keep moving. A stable routing policy lets the product benefit from that movement without making the product itself unstable.