The Cost of Running AI: Inference Economics Explained

01 The 1000x Price Range

The cost of AI inference spans three orders of magnitude. GPT-4.5 and Claude 4 Opus cost $15 per million tokens. GPT-4o-mini costs $0.30. DeepSeek V3 costs $0.27. A locally-hosted Llama 3.2 3B costs $0.01 (just electricity). This means you're paying 1,500x more for GPT-4.5 than for a local 3B model. The question is: is the quality difference worth 1,500x? For most tasks, no. For tasks that require frontier intelligence, yes. The key is knowing which tasks need which model.

02 The Routing Strategy

Smart companies don't use one model — they route. Easy tasks (classification, extraction, simple Q&A) go to cheap models (GPT-4o-mini, local 3B). Medium tasks (summarization, basic code generation) go to mid-tier models (Llama 70B, DeepSeek V3). Hard tasks (complex reasoning, creative writing) go to frontier models (GPT-4.5, Claude 4). This routing can reduce costs by 80-90% with minimal quality loss. The technical challenge: building a router that accurately predicts task difficulty before sending it to a model.

03 The Inference Price War

Inference prices are dropping 50-70% per year as models become more efficient and competition intensifies. OpenAI's API prices have dropped 90% since GPT-4 launched. Google and Anthropic have followed. This is a Moore's Law for AI: same quality, lower cost, every year. The implication: AI becomes a commodity. If inference is nearly free, the value shifts to data, application integration, and proprietary workflows. The companies that win won't be the ones with the best model — they'll be the ones with the best data and the best products.