LLM Model Selection: How to Pick the Right Model for Every Agentic Task

LLM model selection involves deliberately matching each task to the least expensive model that meets a defined quality threshold rather than relying on a flagship model as the default. The approach applies both to multi-step AI agents, whose individual functions may require different capability levels, and to coding-agent deployments, where costly default models can be used unnecessarily across routine tool calls and file operations. Effective selection requires holding prompts, context, and evaluation conditions constant across models, measuring outputs with metrics or pass/fail assertions, and comparing quality alongside token costs. The text highlights Opik’s evaluation and cost-intelligence tools as ways to conduct these comparisons and identify costly model usage, citing e-commerce company Pattern’s reported estimate of $60,000 in annual savings after finding a smaller model that matched its prior quality baseline. It also notes that providers including Anthropic, OpenAI, and Google offer tiered models with substantial price differences, making periodic review of workspace defaults and task-specific routing important as models, prices, and workloads change.

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