Technology Radar
We've sometimes caught ourselves defaulting every task that requires an LLM to the most capable model available, paying no attention to cost, latency or required capability. We call this blanket frontier model usage. Frontier models can cost significantly more and run more slowly than their more nimble counterparts; routine work such as exploration, simple edits or summarizing is often better served by a smaller model or even open-weight models. You can switch tiers manually, per subagent or through harness settings that switch automatically based on task complexity or stage. Each model has its own prompt cache, so you can also switch at natural boundaries, a new session, a subagent or the handoff from planning to implementation, rather than per request. A warning on our warning, though: more capable models sometimes finish tasks using fewer tokens, so use context profiling to check whether your optimization is working.
