OpenClaw creator burned through $1.3 million in OpenAI API tokens in a single month — bill covered 603 billion tokens across 7.6 million requests and 100 coding agents

OpenAI logo on a. phone
(Image credit: Getty Images)

Peter Steinberger, the Austrian developer behind OpenClaw who joined OpenAI in February, posted a screenshot of his API usage dashboard on Friday showing $1,305,088.81 in OpenAI spending over 30 days.

The bill covered 603 billion tokens across 7.6 million requests, all generated by roughly 100 Codex instances operated by a team of three people working on the open-source OpenClaw project. OpenAI, which employs Steinberger, covers the cost. The top model on the dashboard was GPT-5.5, dated April 23, 2026. On the day Steinberger posted the screenshot, his account logged $19,985.84 in spend and 206,000 requests.

Steinberger's fleet of Codex agents autonomously reviews pull requests, scans commits for security vulnerabilities, deduplicates GitHub issues, and writes fixes. Some agents open PRs based on the project's broader roadmap, while others monitor performance benchmarks and flag regressions to the team's Discord server. According to The Decoder, certain agents even attend meetings and generate PRs for features that come up in conversation.

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OpenClaw itself has had a turbulent few months in the public eye, from wiping Meta's AI Alignment director's inbox to prompting Nvidia to develop its own competitor. But Steinberger has consistently called the project a laboratory for stress-testing what AI-assisted development looks like without budget constraints.

Steinberger clarified in a follow-up post that the $1.3 million figure reflects Codex's "Fast Mode" pricing, which consumes credits at a significantly higher rate than standard execution. Disabling Fast Mode alone would reduce the raw API cost to around $300,000, he said. That itself, however, is revealing, given that a single $200-per-month Codex Pro subscription provides roughly $5,000 to $6,000 in API-equivalent value per billing cycle. By that math, Steinberger’s non-fast-mode usage would equate to approximately 60 Codex Pro subscriptions.

OpenAI estimates that Codex costs between $100 and $200 per developer per month on average, though it warns of high variance depending on factors like model choice and automation intensity. Steinberger's usage sits at the extreme end of that variance, but it puts a number on the gap between what developers pay and the underlying compute costs.

AI coding tools are currently facing growing scrutiny over their cost economics. Codex, Claude Code, and Cursor are all competing aggressively for developer adoption, and all three subsidize inference costs well below API rates to attract and retain users. OpenAI shifted Codex to token-based billing in April, a move that made such subsidies more transparent but also more variable for power users.

Steinberger appeared unconcerned about the bill — easy enough when you’re not paying for it — describing the spending as research into how software development would change if token costs weren’t a constraint. Everything his team builds, he noted, remains open source.

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Luke James
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Luke James is a freelance writer and journalist.  Although his background is in legal, he has a personal interest in all things tech, especially hardware and microelectronics, and anything regulatory. 

  • usertests
    "Burned" but actually perfect when the company is pushing its employees to use more free tokens, as we've seen stories about for weeks.
    Reply
  • vanadiel007
    He should us AI to analyze his costs and see if he could cut out some AI agents working for him, or combine them into a more efficient group to reduce cost.
    Reply
  • timsSOFTWARE
    He works for OpenAI. I'm sure they would like to see others copy his technique.
    Reply
  • badmanchillin
    At $1.3M/month ($15.6M/year) one can hire 70 senior engineers working full time. There's no way 3 engineers with unlimited access to Codex will outperform 70 equally competent engineers who also can use AI as reasonable people. This is just completely stupid.
    Reply
  • sygreenblum
    Yeah, I'm gonna have to agree with this but add that those 3 engineers may be running 100 agents (building, watching for deviations and hallucinations) but who's doing the code review and testing? Nobody is going into coding to be a tester. It's like becoming a doctor to be a brain surgeon but they're only hiring proctologists.
    Reply
  • AI_Kills
    The mortality cost of carbon (MCC), a scientifically derived and broadly accepted calculation of the cost of human life from carbon emissions, is 4,434 metric tons per human life. Carbon intensive queries like those done w ChatGPT 5.5 emit an estimated .37 grams/1k tokens or 11.73 billion tokens per human life expectancy. His GPT use this month cost the equivalent of 5 human lives in shortened life expectancy from the associated carbon emissions. This isn’t an accomplishment, it’s a felony.
    Reply
  • usertests
    AI_Kills said:
    The mortality cost of carbon (MCC), a scientifically derived and broadly accepted calculation of the cost of human life from carbon emissions, is 4,434 metric tons per human life. Carbon intensive queries like those done w ChatGPT 5.5 emit an estimated .37 grams/1k tokens or 11.73 billion tokens per human life expectancy. His GPT use this month cost the equivalent of 5 human lives in shortened life expectancy from the associated carbon emissions. This isn’t an accomplishment, it’s a felony.
    My agentic AI is a hit man? Hell yeah, gonna smoke out some orphans.
    Reply
  • ejolson
    badmanchillin said:
    At $1.3M/month ($15.6M/year) one can hire 70 senior engineers working full time. There's no way 3 engineers with unlimited access to Codex will outperform 70 equally competent engineers who also can use AI as reasonable people. This is just completely stupid.
    That's right. So how many humans would be needed to accomplish the same task as the three engineers with unlimited codex? Figure out their pay and work backwards to determine how much value all those AI tokens are worth.

    I think it's possible the three engineers would accomplish more without so many tokens. While I'm admittedly a rank amateur, my experience is that AI works well as a quick index into the documentation and simple code examples, but it quickly becomes inefficient for AI to write finished code. This could be domain dependent or a result of me not holding it right.

    Hopefully AI will become cheaper as hardware optimised for inference becomes more widely available.
    Reply
  • American2021
    ChatGPT and I argued so much that ChatGPT filed for a divorce. She's dumber than dirt. Fortunately, I kept the house but am on the hook for the duration of the annual membership. I went out to celebrate and met Miss Gemini. We are engaged now.
    Reply
  • JohnyFin
    Yes....fire people, use AI agents to create bad codes, release them to achieve catastrophic scores. People make mistakes but AI make unpredictable mistakes. Besides, these AI agents still codes from all sources, no doubt. Creepy time for humanity
    Reply