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Cost-aware large language model selection focuses on routing tasks to different models based on price–performance tradeoffs, aiming to meet quality targets while controlling spend. Approaches weigh latency, accuracy, and token costs to decide when to use small, inexpensive models versus larger, more capable ones for specific prompts and workloads.