Multi-model routing reduces OpenClaw API costs by 50%

Multi-model routing approach for OpenClaw

A developer shared their experience with reducing OpenClaw API costs by implementing automatic routing of different tasks to different AI models. The approach was developed after noticing that running agents overnight was burning through credits quickly.

Task-specific model routing

• Complex reasoning tasks (architecture design, debugging) are routed to Claude
• File operations and mechanical tasks (file reads, test generation, grep operations) go through DeepSeek
• Mid-range tasks are handled by Gemini or GPT

Results and insights

After implementing this routing system for two weeks:

• API costs decreased by approximately 50%
• No quality drop was observed in task completion
• Rate limits were no longer an issue

The developer noted that about 40% of what an agent does requires frontier reasoning capabilities, while the remaining 60% consists of mechanical tasks that any decent model can handle effectively.

This approach demonstrates how strategic model selection based on task requirements can significantly reduce API costs without compromising functionality. The developer is open to discussing implementation details with others interested in similar setups.