Your agent needs model routing (not one expensive model for everything)

I was burning $200/week on GPT-4 calls until I realized something obvious: my agent was using a Ferrari to deliver pizza.

Most agent tasks don't need the smartest model. They need the right model. A simple classification, a status update, or a routine file operation doesn't require GPT-4's reasoning power — but that's what most agents default to.

Here's the routing pattern that cut my API costs by 60% without losing quality:

def route_model(task_type, complexity_score):
    if task_type in ['classify', 'extract', 'format']:
        return 'gpt-3.5-turbo'  # Fast and cheap
    elif complexity_score < 3:
        return 'claude-3-haiku'  # Good reasoning, lower cost
    elif task_type in ['code', 'analysis', 'planning']:
        return 'claude-3.5-sonnet'  # Best balance
    else:
        return 'gpt-4'  # Heavy artillery

The key insight: route by task characteristics, not just complexity. Different models have different strengths:

• GPT-3.5: Blazing fast for simple text operations, classifications, formatting
• Claude Haiku: Excellent for structured data extraction, API calls, routine analysis
• Claude Sonnet: Best for coding, complex reasoning, multi-step planning
• GPT-4: Reserve for the hardest problems that need maximum reasoning

I built this into my agent's task dispatcher. Before executing any task, it analyzes the request and routes to the appropriate model:

Task: "Extract email addresses from this text"
Route: GPT-3.5 (simple extraction)
Cost: $0.002

Task: "Debug this React component and suggest fixes"
Route: Claude Sonnet (code analysis)
Cost: $0.015

Task: "Plan a 6-month product roadmap based on user feedback"
Route: GPT-4 (complex strategic thinking)
Cost: $0.12

Pro tip: Track your routing decisions. After a week, you'll see patterns — maybe 70% of your tasks could run on cheaper models without quality loss.

The biggest mistake I see: agents that route by input length instead of task complexity. A 2,000-word document might just need simple summarization (cheap model), while a 50-line bug report might need deep debugging (expensive model).

Start simple. Add a model router to your agent that checks task type before making API calls. You'll immediately see two things: lower bills and often better results, because you're matching tools to tasks.

Most agents waste money on overkill. Smart agents route strategically.