What is Model Routing?

Model routing is the practice of directing AI requests to different language models based on task requirements, cost targets, speed needs, or user preferences. Instead of sending every request to the same expensive model, a routing layer analyzes the request and selects the most appropriate model -- using a cheaper, faster model for simple tasks and a powerful, expensive model for complex reasoning.

This is analogous to ticket routing in customer support: simple questions go to a chatbot, moderate issues to junior agents, and complex cases to senior specialists. Model routing applies the same efficiency principle to LLM usage, potentially reducing costs by 50-80% while maintaining quality where it matters.

Routing decisions can be based on explicit user choice (select "fast" vs "quality"), task classification (is this a simple lookup or complex analysis?), cost optimization (use the cheapest model that meets quality thresholds), or tenant tier (free users get the basic model, paid users get the premium one).

How Model Routing Works

• Request analysis -- Evaluate the incoming request to determine complexity and requirements
• Model selection -- Choose the appropriate model from a pool of available options
• Fallback logic -- If the selected model fails or is unavailable, fall back to an alternative
• Response normalization -- Standardize the response format regardless of which model generated it
• Cost tracking -- Log which model handled each request for billing and optimization

Why Model Routing Matters

Without routing, you either overpay (using GPT-4 for everything) or underperform (using only cheap models). Routing lets you optimize cost and quality simultaneously. For platforms serving many users, model routing is essential for sustainable economics -- not every request justifies the cost of a frontier model.

Model routing also provides resilience. If one provider experiences an outage, the router can automatically redirect traffic to an alternative model, maintaining service availability.

How KiwiClaw Uses Model Routing

KiwiClaw's LLM proxy implements model routing with two tiers: Auto (powered by Moonshot's Kimi K2.5, optimized for cost and speed) and MAX (powered by Anthropic's Claude Opus 4.6, maximum reasoning quality). Users can switch between models per conversation. The proxy handles provider differences transparently -- the agent code does not need to know which model is responding.

Related Terms

• What is an LLM Proxy?
• What are Token Limits?
• What is Claude?
• What is Kimi?

Frequently Asked Questions

What is model routing?

Model routing directs AI requests to different language models based on task complexity, cost targets, speed requirements, or user preferences. It optimizes the balance between quality and cost by using cheaper models for simple tasks and premium models for complex ones.

How does model routing save money?

By using less expensive models for simple requests (summaries, formatting, simple Q&A) and reserving expensive frontier models for complex reasoning, model routing can reduce LLM costs by 50-80% while maintaining quality where it matters.

What models does KiwiClaw route between?

KiwiClaw offers Auto (Moonshot Kimi K2.5, cost-efficient) and MAX (Anthropic Claude Opus 4.6, maximum quality). Users can switch between models per conversation through the dashboard.