Hey everyone, I’m pretty new to OpenClaw and agent setups, and I’m realizing I probably configured things in the most expensive way possible.
Right now I’m using GPT-5.2 as my default model for basically all daily tasks (research, writing, light automation, small debugging), and I’m noticing my API costs are climbing very fast even with normal usage.
From what I’m reading here and in other threads, it sounds like the smarter approach is model routing instead of using one high-end model for everything. For example:
• Strong model for complex reasoning / coding
• Mid-tier model for routine automation
• Cheap/light model for summaries, checks, and simple tasks
My main confusion is the actual implementation inside OpenClaw.
Specifically:
1. Do you set a cheaper model as the main agent default and only escalate to GPT-5 when needed?
2. Are you using routing rules, separate agents, or tool-based model selection?
3. Where in OpenClaw config do you usually define model routing (config.yaml, agents, or provider settings)?
4. Do you also limit max iterations and max tokens to control loop costs?
My use case is mostly daily productivity workflows (not 24/7 autonomous agents):
• Research & summaries
• Marketing-related tasks
• Writing and planning
• Light coding/debugging
• Automation loops
I’m not trying to nerf performance, just stop burning tokens on tasks that don’t need a top-tier reasoning model.
If anyone has a sample config, routing strategy, or “cost-efficient stack” (like default + escalation model), I’d really appreciate seeing how you structured it.
Bonus question:
Did switching to routing actually cut your monthly API cost significantly, or was the biggest win from reducing agent loops/context size?
Any tutorial or somebody can help me with this setup. I’m new to this space.