Model Selection
definition
Choosing the right model for your agent involves balancing task complexity, latency requirements, cost constraints, and tool-use capabilities. Frontier models like Claude Sonnet and GPT-4o excel at complex reasoning and multi-step planning, while smaller models like Claude Haiku and GPT-4o-mini are better suited for high-volume, lower-complexity tasks like classification or extraction.
Choosing the right model for your agent involves balancing task complexity, latency requirements, cost constraints, and tool-use capabilities. Frontier models like Claude Sonnet and GPT-4o excel at complex reasoning and multi-step planning, while smaller models like Claude Haiku and GPT-4o-mini are better suited for high-volume, lower-complexity tasks like classification or extraction. Many production agent systems use model routing — sending simple tasks to fast, cheap models and escalating complex ones to more capable models — which can cut costs by an order of magnitude while maintaining quality where it matters. This concept connects to token economics for understanding cost-performance tradeoffs, choosing your stack for full tool selection, and the autonomy spectrum for matching model capability to the level of autonomy required.