Author(s): Udaykiran Estari

Originally published on Towards AI.

Why defaulting to the smartest model might be your most expensive routing mistake.

OpenAI wants you to believe Sol is the best model in the GPT-5.6 family, but defaulting to it might be your most expensive engineering mistake. The moment GPT-5.6 stopped being a single endpoint and became three distinct tiers — Sol, Terra, and Luna — model selection turned into a rigorous unit-economics problem. Most teams are about to get the spreadsheet wrong.

The article explains why routing should be based on cost per accepted outcome (including retries, reviewer time, token length, cache effects, and the cost of mistakes) rather than benchmark “winners.” It breaks down the practical differences across Sol (flagship/high-consequence work), Terra (balanced default for interactive and routine tasks), and Luna (cost-sensitive bulk with strict schemas), and shows how reasoning effort and orchestration add another cost lever beyond tier choice. It then provides a deployable decision framework: start with a simple one-minute policy, run a validator-and-escalate cascade, measure acceptance/retry/review and cache hit rates, and log enough telemetry to improve routing over time. Finally, it warns where the approach can fail (safety constraints, agentic behavior risks, orchestration pitfalls, and rollout/entitlement mismatches), concluding with actionable steps to ship a router based on measured unit economics instead of “vibe-based” defaults.

Read the full blog for free on Medium.

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Published via Towards AI