100 billion tokens in one run
Ben's Executor port run reached 100 billion tokens, illustrating how subagent-heavy loops can generate astronomically large token counts.
Where this was said
Long-Running Tasks & What Actually Changed
At 36:05 · chapter starts 35:00
This chapter traces the evolution of how both hosts actually use these models. Fable was the catalyst: it was the first model that made genuinely long, complex runs feel worth attempting. When Fable was taken away, they found 5.6 could carry those same expanded workflows further than any previous OpenAI model. Theo explains the economics: a $200 Codex subscription, per Semianalysis measurements, delivers up to $14,000 worth of compute per period — and with two usage resets that restore a full week, this can reach $20,000 per month [1] — Theo "According to Semianalysis measurements, a $200/month Codex subscription can yield as much as $14,000 worth of token usage in a single perio…" 34:00 . He's careful to note that the outrageous dollar figures on their dashboards ($131,700 and $93,000 respectively) represent deliberate stress-testing experiments, not practical work. His real workflow tasks — auditing open PRs, rebasing, coordinating merges — ran on a single 5.6 thread spawning targeted subagents, and were both cheaper and more valuable.
How to Burn $65k on a Single Loop
Ben's single run to port the Executor project to Rust and Svelte ran to 100 billion tokens and cost $65,000. The real driver wasn't the orchestrating reasoning model — it was the dozens of subagents it spun up underneath, which is the only way to blow through API usage this fast.
$65k on a single loop run
Ben's single longest run — porting the Executor project to Rust and Svelte — cost $65,000 worth of API tokens via xHI reasoning model orchestrating massive subagent swarms.
Codex subagent limit bumped to 20+
Theo increased Codex's subagent limit from the default of ~3 to over 20 to enable more aggressive parallel work, which also pushed macOS to its limits.
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