An AI model's cost is more than just its per-token price; it encompasses the entire system, including inference, retrieval, compute, and engineering efforts. The true metric for optimization should be the cost per successful business outcome, not just the model's base rate. This framework suggests evaluating models like OpenAI's GPT-5.6 family (Sol, Terra, Luna) based on their role in achieving desired quality, latency, and reliability at the lowest total cost for specific applications. AI
IMPACT Provides a framework for optimizing AI system costs by considering the total workload rather than just model pricing.
RANK_REASON Article provides a framework for analyzing AI model costs, using OpenAI's GPT-5.6 models as an example, rather than announcing a new release or significant industry event.
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