GPT-5.6 pricing: the cheaper model is not always the cheaper AI workflow

Short summary

Choosing the cheapest AI model per token doesn't guarantee the cheapest end-to-end workflow for your product. Effective cost optimization requires balancing four distinct layers: per-token pricing rates, output volume and response format, prompt caching hit rates, and often-hidden operational costs such as retries, human review queues, model escalations, and support tickets. OpenAI's GPT-5.6 pricing tiers (Sol for complex reasoning, Terra for balanced tasks, Luna for speed) enable intelligent routing based on task complexity.

• •Four-layer cost framework: token pricing, output design, caching effectiveness, and operational overhead
• •Model routing pattern: route simple tasks to Luna, complex reasoning to Sol, use caching for repeated context
• •Workflow architecture and task routing strategy matter as much as per-token model pricing

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