GPT-4 vs Claude vs Local Models: Which Should You Use?

I was paying $300/month for AI models. Then I learned about model routing. Now I pay $80. Same quality, 73% less cost.

The secret? Using the right model for each task.

Here's the complete breakdown of when to use GPT-4, Claude, or local models for your OpenClaw agents.

The Model Comparison

Model	Best For	Cost/1K tokens	Context
GPT-4	Complex reasoning, coding, analysis	$0.03 input / $0.06 output	128K tokens
Claude 3.5	Long documents, writing, summarization	$0.003 input / $0.015 output	200K tokens
GPT-3.5	Simple tasks, formatting, quick responses	$0.0005 input / $0.0015 output	16K tokens
Local (Llama 3)	Simple queries, high volume, privacy	~$0 (hardware cost only)	8K tokens

When to Use Each Model

GPT-4: The Heavy Lifter

Use GPT-4 when you need:

• Complex reasoning: Multi-step analysis, strategic planning
• Code generation: Writing scripts, debugging, refactoring
• Creative tasks: Novel approaches, unique angles
• High-stakes decisions: When accuracy matters most

My use case: Research Agent uses GPT-4 for complex analysis. Worth the cost for quality insights.

Claude 3.5: The Writer

Use Claude when you need:

• Long context: Processing documents, reports, books
• Natural writing: Blog posts, emails, content creation
• Summarization: Condensing long texts accurately
• Cost efficiency: 10x cheaper than GPT-4 for many tasks

My use case: Content Agent uses Claude for drafting. Better writing quality at lower cost.

GPT-3.5: The Workhorse

Use GPT-3.5 when you need:

• Simple formatting: Converting data, restructuring content
• Quick responses: FAQs, simple queries
• High volume: Tasks where you process thousands of requests
• Cost control: 60x cheaper than GPT-4

My use case: Scheduler Agent uses GPT-3.5. Simple task, doesn't need premium model.

Local Models: The Private Option

Use local models when you need:

• Data privacy: Sensitive information stays on your machine
• High volume: Thousands of requests with no API costs
• Offline operation: No internet required
• Customization: Fine-tune for specific tasks

My use case: Analytics Agent uses local Llama 3. Processes lots of data, no privacy concerns.

My Model Routing Setup

Here's how I route tasks in my 5-agent team:

• Research Agent: GPT-4 (complex analysis worth the cost)
• Content Agent: Claude 3.5 (better writing, long context)
• Editor Agent: Claude 3.5 (natural language processing)
• Scheduler Agent: GPT-3.5 (simple task, low cost)
• Analytics Agent: Local Llama 3 (high volume, data privacy)

Result: $300/month → $80/month. Same output quality.

How to Implement Model Routing

In OpenClaw, you can specify models per agent in your configuration:

# agent.json
{
  "name": "ContentAgent",
  "model": "claude-3-5-sonnet-20241022",
  "temperature": 0.7,
  "max_tokens": 2000
}

# For cheaper tasks
{
  "name": "SchedulerAgent", 
  "model": "gpt-3.5-turbo",
  "temperature": 0.3,
  "max_tokens": 500
}

You can also route dynamically based on task complexity:

• Simple formatting → GPT-3.5
• Standard content → Claude
• Complex analysis → GPT-4

Start Saving Today

You don't need to switch everything at once. Start with one agent:

1. Identify your highest-volume, simplest agent
2. Switch it to GPT-3.5 or local model
3. Monitor quality for a week
4. Gradually migrate other agents

Even switching one agent from GPT-4 to Claude can save $50+/month.