The prompt was released, but not the cost of the result.
And not how many times it was prompted before it returned a working solution.
Or how many prior variants of this prompt were tried.
Or if proof checking software was used to hone in on the final winning prompt / LLM output.
Assuming all 64 subagents were running for a full hour (the tweet states just under an hour):
Claude estimates that tool use / input tokens might add 10-15% on top of that depending on exactly how the model went about the task.Edit: better tok/s estimate buckets based on GPT 5.5 actual speeds since I couldn't find real benchmarks on 5.6 published anywhere. Also account for Sol Fast pricing.