← All IntelClip / AI AgentsReal usage data: token spend and model mix
From Multiplayer agentic engineering — Arjun Singh, Superconductor · ≈15:59
“essentially 100% like 99.9% of our pull requests are like heavily agent generated”
“Codex had four times as many sessions, and it was cheaper overall.”
“the vast majority of our work currently is merged through Codex”
What’s in it
- Real token-cost breakdown: Claude Code vs Codex vs GLM in production
- Shows why one team routes 99.9% of PRs through AI agents
- Teases a system that auto-picks the best model per coding task
Clip transcript
Let me go back. So, what that kind of turned into us for us is, you know, essentially 100% like 99.9% of our pull requests are like heavily agent generated. We know that quality and reliability and security are really important, so we still have humans look at everything. We have agents help with it all, but everything's human reviewed. Um you know, for our our our our relatively small team, we had 1 and 1/2 billion tokens over the past month. And you can kind of see what we're saying about Claude here. It's a little small, so I apologize, but we had 3,300 Claude code runs that cost $10,000 in tokens daily. We have plans, so we didn't spend $10,000 on it. And Codex had four times as many sessions, and it was cheaper overall. And so again, the vast majority of our work currently is merged through Codex. We still use the other models. More and more is happening through GLM 5.2. Going to invest in that. And the one thing that we're really excited to do going forward with this benchmarking is automatically like like you've probably heard about people, you know, routing tasks to the right models and all that, but how do how does like some third party know what to route for your code base? Like this is a way that you can know what's going to work best for which task for your for your project, and we're going to kind of automatically routing that for you.
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