AI agent costs need model choice per prompt, not per task
Running AI agents on daily work makes it hard to choose which model should handle each request. The common advice is to use cheaper models for easy work and stronger models for hard work, but in practice that often turns into guesswork. One task can contain both cheap and expensive turns.
A may spend many turns reading files, running commands, and summarizing errors. Those simpler turns may be fine for a small like Qwen. A later turn may need deeper reasoning about a difficult bug, and that is where a more expensive model can be worth it.
The useful unit for model choice is therefore each prompt or turn, not the whole task. Choosing one model for an entire agent run can waste money on easy steps or hurt quality on hard ones. A possible answer is a triage layer where a small each prompt, estimates its difficulty, and sends it to the cheapest model likely to handle it well.
Key points
- One agent task can include both simple turns and turns.
- Simple turns include reading files, running commands, and summarizing errors.
- Hard debugging turns may still need a stronger, more expensive model.
- Choosing one model for a full run can mean overpaying or underperforming.
- A small model could act as a triage layer and route each prompt to a suitable cheaper model.