I want to start by stating that this looks exactly like Claude Code. It has the same basic structure, the same memory files, and the same agentic loops. If you’ve seen that architecture, this will feel very familiar.
But here is the reality check on the cost. I’ve spent almost $1,100 in the last two weeks, maybe a little over $1,100 actually. Strictly on API keys and OpenClaw.
That sounds like a huge amount for just “chatting” with AI. But when I actually looked at the breakdown, it started to make sense. I’m essentially hiring a junior developer who works 24/7. But the lesson here is that it is super easy to overspend. You have to always keep track of your context window. If you aren’t paying attention, you can burn $50 in an hour without even realizing it.
The “Wrapper” Problem
I’ve been using OpenRouter to switch between models, and honestly, it’s just okay. It’s not the best. For me, it hasn’t been reliable enough to use in production. It adds latency, and sometimes it just fails when I need it to work. If you’re going to do this seriously, you need to set up something proper with a direct connection to Google or Anthropic.
The Models (My Experience)
If you are going to use this architecture, you have to use it on good models. Otherwise, it is useless in most cases. You can use lower models for administration, but for the core intelligence, you need the heavy lifters. I tried everything, and there is no way around this. At least not today.
- Claude Opus: My favorite. These are my daily drivers. Opus is cold, but I like cold. It is precise. It’s not as dismissive as the other models can be. It doesn’t ignore your question if the context window gets tight or just hallucinate random stuff.
- Gemini 3 Pro: If you want Google, this is the floor. Good workhorse can sometimes give random errors with no indications but overall very usable.
- Kimi k2.5: It’s okay. Only okay. It’s very functional for automated tools, but talking to it feels like talking to a wall. It’s not a daily driver.
The Real Cost
You must be technical if you’re going to go into this. Otherwise, just don’t do it for now. Find a ready deployment with easy limits.
I’ve been building local agents on this stack, and it works because I treat him like a system, not a chatbot. It has my entire operational context loaded, which means he can check my work against my own rules and catch me when I’m about to repeat old mistakes.
That level of consistency is what made the $1,100 worth it. I’m not paying for text generation. I’m paying for “someone” who remembers everything I’ve said, holds me to it, and doesn’t let me drift when things get chaotic. With that said, you must also have the discipline and patience to double-check the AI’s work because many times, it says it “remembers” but it’s just trying to be nice. Frustrating, but also part of the price.
But if you aren’t careful with your usage, you’ll burn through cash and have nothing to show for it. Track everything. Know what you’re spending. And use the smart models when it matters, because the cheap ones will cost you more in wasted time than you save in API fees.
