Under the objective Align model selection with task requirements (e.g., cost, speed, quality) , deploying models effectively requires matching model cognitive capacity to task complexity and throughput requirements.

Option C correctly routes complex, multi-layered tasks—such as synthesizing intricate legal precedents, statutory compliance, or multi-step policy analysis—to high-capability models (e.g., Claude 3 Opus or Sonnet) where deep reasoning is essential. Option E correctly routes simple, high-frequency, low-complexity tasks—such as standard FAQ lookups—to light, fast models (e.g., Claude 3 Haiku) to achieve ultra-low latency and minimal cost per request. Conversely, choosing models purely based on cost (Option A) is inefficient, deploying large frontier models for simple keyword extraction (Option B) wastes financial and rate-limit budgets, and assigning complex strategic synthesis across many documents to lightweight models (Option D) leads to shallow analysis and missed nuances.