AI APIs are not free. Here is how I think about model selection to keep costs sensible without sacrificing quality.
When you start using AI APIs seriously, the first bill can be a surprise. Tokens add up fast — especially if you are running the same capable model for every single task, including the trivial ones.
The key insight is that different tasks need different models. A quick nudge message asking “have you worked out today?” does not need the same horsepower as a complex reasoning task or match day analysis. Using Claude Haiku for background nudges and summaries, and reserving Claude Sonnet or Gemini Pro for the heavy lifting, cuts costs significantly without any loss in quality where it matters.
OpenClaw makes this easy with per-cron model overrides. You define the model at the job level, so routine tasks run cheaply and important ones get the full treatment.
Another big lever is context size. Woodhouse loads memory files at the start of each session. The more you dump into memory, the more tokens every conversation costs. Keeping MEMORY.md lean and purposeful is not just good housekeeping — it is cost management.
My current split: Haiku for heartbeat nudges and routine checks, Sonnet for conversations and project work, Gemini 2.5 Pro for match day analysis and deep research. The difference in monthly spend is meaningful.