Stop paying frontier prices for classification.

Key takeaways

• 1Using one frontier model for every agent step is the most common cost mistake on production stacks. The spread between a classification tier and a frontier tier is often 10x on the same output quality for that step.
• 2Tier 1 (small, fast, cheap) is for classification and routing: intent detection, query classification, entity extraction, language detection, topic categorization. Haiku 4.5 and Gemini 3.5 Flash are the usual picks in June 2026.
• 3Tier 2 (mid-tier workhorse) is for tool use and structured output: function calling, JSON schemas, most agent loop steps, short reasoning, summarisation, API orchestration. Sonnet 4.6 is the default seat here.
• 4Tier 3 (frontier reasoning) is for multi-step planning, deep reasoning, hard code generation, and agentic orchestration where fewer steps justify higher per-token price. Opus 4.8 fast mode belongs here, not on every sub-task.
• 5Tier 4 (embeddings) is a different product job: semantic search and document retrieval for RAG. Do not run generation models where an embedding model plus vector index is the right tool.
• 6Build the router agent before you tune prompts on the frontier model. A Haiku or Flash classifier picks the execution tier; route classification to Tier 1, tool calls to Tier 2, hard reasoning to Tier 3, retrieval to an embedding index.
• 7Run the router in shadow mode first, then promote per route (not stack-wide). The router needs typed output, exit conditions, and an eval suite built from production mis-routes.
• 8The cost metric that matters is cost per completed task on your eval set, not list price per million tokens. Audit last month's bill by model ID: if roughly 80 percent of spend is frontier, you have a routing problem, not a capability problem.