Spend Capability Where Failure Is Expensive
Model selection is not a ranking exercise. It is an allocation problem across quality, latency, context, and cost. Estimate token and workflow cost without relying on a memorized price table. Select a model using measured quality, latency, and consequence. Explain sampling non-determinism and why a release claim needs repeated evaluation. Choose speed, effort, and thinking settings only after current model and platform verification. Distinguish model failure from prompt, context, source, and workflow failure. Use routing, caching, batching, and output limits as separate optimization levers. A support team routes every request to the most capable model. The first month looks successful. Quality is high, but response time is inconsistent and the bill is four times the forecast. The manager responds by moving everything to the fastest model. Cost falls. Escalation summaries now omit exceptions, and complex refund cases receive confident but incomplete recommendations. Both designs use model names as policy. Neither describes the work. A production decision starts with the cost of failure. A typo in an internal brainstorm is cheap. A missing exception in a refund decision is more expensive. The model, prompt, context, source quality, and review process should reflect that difference. Models process tokens, not pages or words. Input tokens include instructions, conversation history, supplied documents, tool definitions, and retrieved content. Output tokens include the response and, depending…
Spend Capability Where Failure Is Expensive: Model selection is not a ranking exercise. It is an allocation problem across quality, latency, context, and cost.
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