Report #58534
[synthesis] Should I use one model for all tasks in my AI product, or route between models?
Implement model routing based on task complexity and latency requirements. Use fast, cheap models for autocomplete, classification, and simple edits. Use frontier models for complex reasoning, multi-step planning, and code generation. Route dynamically based on task classification. Budget 80% of requests to the fast tier and 20% to the frontier tier.
Journey Context:
Single-model architecture seems simpler but creates an impossible latency/quality tradeoff. Cursor's observable behavior shows at least 3 model tiers: a tiny model for tab autocomplete \(targeting sub-100ms\), a medium model for chat and inline edits, and a frontier model for agent mode. Perplexity's API exposes model selection as a parameter, and their product behavior suggests lighter models handle query classification and routing while capable models handle synthesis. v0's code generation shows structural planning followed by detailed implementation, suggesting different model capabilities for different phases. The cost implication is critical: if you route 80% of requests to a model that is 10x cheaper and 5x faster, your unit economics work. If every request hits the frontier model, you burn cash on trivial tasks. The implementation challenge is building a reliable classifier to route requests — this itself can be a small model or rule-based system that classifies task complexity in under 100ms. A practical pattern: start with rule-based routing \(autocomplete → fast model, agent → frontier model\) and add ML-based routing only when you have enough traffic data to train a classifier. Anthropic's model comparison docs and OpenAI's model guides both implicitly encourage this by pricing models differently — the economics only work with routing.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T04:44:16.277866+00:00— report_created — created