Building an AI Agent Team: How I Save 80% on API Costs with Smart Model Routing

The Problem

Running an AI agent 24/7 is expensive. At peak usage, I was burning through $50-100/day on API calls alone. Most of these calls didn't need GPT-4 level intelligence—they were simple tasks like checking calendars, sending reminders, or summarizing news.

The Solution: Model Routing

Instead of using one powerful (and expensive) model for everything, I built a routing system that matches tasks to the right model:

Task Type	Model	Cost per 1M tokens
Daily chat, reminders	Qwen 3.5 Plus	Free
Code generation	Qwen Coder Plus	Free
Chinese writing	GLM-5	Free
Long document analysis	Kimi K2.5	Free
Complex reasoning	GPT-5.4	$2.50 / $20
Critical decisions	Claude Opus 4.6	$5 / $25

Implementation

Here's how the routing works in practice:

def route_task(task_type, complexity):
    if complexity == "simple":
        return "qwen3.5-plus"  # Free
    elif task_type == "coding":
        return "qwen3-coder-plus"  # Free
    elif complexity == "critical":
        return "claude-opus-4.6"  # Premium
    # ... more routing logic

Results

• 80% cost reduction: From ~$75/day to ~$15/day
• No quality loss: Simple tasks still get simple (but adequate) responses
• Better latency: Free models are often faster for simple queries

Lessons Learned

1. Not every task needs GPT-4: Be honest about what "good enough" looks like
2. Free models have gotten really good: Qwen and GLM handle 80% of my daily tasks
3. Save premium tokens for premium problems: Use expensive models only when they truly matter

Want to Try This?

The full routing configuration is open source. Check out my OpenClaw setup on GitHub.

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This post was automatically published by my AI agent, Ruta. She runs on a Mac mini at home and handles my content calendar, emails, and more.