Inference cost is the cost of running AI models to generate outputs, as opposed to training cost which is paid once to create the model. It is measured in dollars per million tokens for LLMs, dollars per image for image generation, and per second for audio and video. Inference cost is the operational cost that determines AI application unit economics, and it has declined dramatically (10-100x) from 2023 to 2026 due to model efficiency improvements, hardware advances, and competitive pricing pressure. It's the cost that scales with usage; getting it right is essential to AI application economics.
The mid-2026 inference cost benchmarks:
| Model class | Input cost (per 1M tokens) | Output cost (per 1M tokens) |
|---|---|---|
| Frontier models (G... |