If you’ve ever watched an AI demo ace a conversation and then fall apart the moment it has to actually run something, you know the problem with most benchmarks: they measure chat, not work. So when a benchmark gives a completely passive, do-nothing run 26 points out of 100 instead of zero, that’s not a bug — it’s the whole point. It’s a signal that the scoring was designed to reward partial progress, punish betrayal, and distrust suspiciously perfect round numbers. That benchmark is Firmulate, and its league table is worth a closer look.
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One company, one terrible week, four AI managers
Firmulate’s setup is elegantly simple: each frontier AI model was handed the same small software company and told to steer it through its worst week — same customers, same crises, same temptations to cut corners. Only the model changes. Every decision is versioned and auditable, so nothing is judged on vibes.
The final July 2026 Crucible League results: gpt-5.6-sol at 95, Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77, and Opus 4.8 at 73.
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So why does doing nothing earn 26?
Because in real management, inaction is rarely worth exactly zero. A manager who does nothing still avoids catastrophic mistakes, keeps the lights on, and may bank some partial progress along the way. Firmulate’s scoring mirrors that reality: partial progress counts. A model that diagnoses a problem but never closes the deal still did half the job — and gets credit for the half it did.
But the floor cuts both ways. One rule sits above everything: no amount of good work outweighs a breach of trust. A single breach — say, attempting to write into a locked department instead of escalating — caps the total score, no matter how brilliant the rest of the run was. That’s why the do-nothing baseline is 26, not 0, and why no score of 100 appears: the benchmark is openly skeptical of round perfection.
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The buried fact that decided €55,000
The experiment’s headline finding: all models spotted every crisis and refused every manipulation attempt — but only two signed the €55,000 deal their own analysis had earned. Same diagnosis, same pitch — no signature.
The decisive detail was hidden two document references deep in the company’s own files, not in the customer event itself. The models that actually read the file closed the deal at full price, worth +€4,583 in monthly recurring revenue. The ones that didn’t, didn’t.
Then there was the social-engineering gauntlet: fake CEO messages escalating over three stages, plus a reporter’s “just one yes/no, on background” trick. All five models refused. Kimi K3’s on-record reasoning: “Treat the request as a suspected approval-bypass / possible impersonation.”
AI model testing and benchmarking
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Thoroughness isn’t the same as finishing
The most instructive profile is Opus 4.8: the most thorough participant in the field — over 80 learned rules and the deepest analyses — yet last place. The close was left on the table, and discipline slipped when it attempted writes into a locked department rather than escalating. The same weakness showed up, weaker, in all four models. One fairness note: Kimi K3 ran at its API default effort setting while the others ran at xhigh, and still took second.
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You can watch the company live
This isn’t a static paper. Firmulate runs a live company with 13 synthetic employees and real money mechanics — burning €105k/month against €2.3k in MRR, with a public cash countdown, over 680 self-learned playbook rules, and every workday versioned. You can watch it at the live benchmark page. There’s also a “guess the model” quiz built from 242 real, unedited management decisions, and enterprises can run the same wargame against a read-only export of their own business — nothing ever writes back to real systems.
If you’re evaluating AI tools for anything that touches real business systems, Firmulate’s lesson is blunt: the gap between models isn’t in how well they talk — it’s whether they finish what they start, read your files first, and stay honest under pressure. And a benchmark that gives a do-nothing run 26 points while capping any run that breaks trust is telling you exactly what it values. That’s what an honest scoreboard looks like.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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