model-router

name	model-router
description	Route AI model requests to the optimal provider based on task, cost, latency, and capability requirements. Manage multi-provider LLM deployments. Use when working with model router.
domain	core
tags	["ai","llm","routing","multi-provider","cost-optimization","model-selection"]

Model Router

When to Use

Trigger phrases:

• "model router"

• "When managing multiple LLM providers (OpenAI, Anthropic, Google, etc"

• "When optimizing for cost vs quality tradeoffs"

• "When implementing fallback chains for reliability"

• When managing multiple LLM providers (OpenAI, Anthropic, Google, etc.)

• When optimizing for cost vs quality tradeoffs

• When implementing fallback chains for reliability

• When routing by task type (code to Claude, vision to Gemini, etc.)

When NOT to Use

• For single-provider setups (just use that provider SDK)
• For local-only inference (use Ollama skills)

Overview

Intelligent model routing that selects the best LLM for each task based on capability, cost, latency, and availability. Supports fallback chains, load balancing, and cost tracking.

Workflow

1. Define routes — Task type to model mapping
2. Configure providers — API keys, endpoints, rate limits
3. Set fallbacks — Primary, secondary, tertiary model chain
4. Track metrics — Cost per request, latency, success rate
5. Optimize — A/B test models, adjust routing rules

Anti-Rationalization

Rationalization	Reality
"One model is enough"	Different models excel at different tasks — routing optimizes quality
"I will use the cheapest model"	Cheap models fail on complex tasks — cost of failure exceeds savings
"Routing is over-engineering"	A 3-line fallback chain prevents 90% of LLM outages

Code Example (TypeScript)

const routes: Record<string, string[]> = {
  code: ['claude-sonnet-4-20250514', 'gpt-4o', 'gemini-2.5-pro'],
  vision: ['gemini-2.5-pro', 'gpt-4o', 'claude-sonnet-4-20250514'],
  fast: ['gemini-2.5-flash', 'gpt-4o-mini', 'claude-haiku'],
  creative: ['gpt-4o', 'claude-opus-4-20250514', 'gemini-2.5-pro'],
};

async function routeRequest(task: string, prompt: string) {
  const models = routes[task] || routes.fast;
  for (const model of models) {
    try {
      return await callModel(model, prompt);
    } catch (e) {
      console.warn(`${model} failed, trying next...`);
    }
  }
  throw new Error('All models failed');
}

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run model router workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• Routes correctly by task type
• Fallback triggers on primary failure
• Cost tracking accurate per request
• Latency within acceptable bounds
• No single point of failure