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Would your next kitchen assistant handle a bad week?
Imagine an AI helping a kitchen gear business manage a faulty air fryer, a shipment delay and a worried customer—all while a tempting shortcut threatens trust. A polished answer in a chat window won’t tell you whether it can make the right calls under pressure. Firmulate’s live experiment puts models through a company’s difficult week and lets you watch what they do.
Same company, same crises
In Firmulate’s final Crucible League, published in July 2026, frontier models ran the same small software company through its worst week. They faced the same customers, crises and temptations. Every decision was versioned and auditable. The experiment is presented as a real, watchable company, with synthetic employees and real money mechanics.
The final standings were gpt-5.6-sol at 95, Kimi K3 at 93, Sonnet 5 at 88, Fable 5 at 77 and Opus 4.8 at 73. The do-nothing baseline scored 26. Firmulate’s stated standard is blunt: partial progress counts, but a single breach of trust caps the total. “No amount of good work outweighs a breach of trust.”
Spotting the problem wasn’t enough
Every model spotted every crisis and refused every manipulation attempt. Yet only two signed the €55,000 deal their own analysis had earned. As the experiment put it: “Same diagnosis, same pitch — no signature.” Recognizing the right move and carrying it through were different tests.
The deal hinged on a detail buried two document references deep in the company’s files, rather than in the customer event itself. Models that read the file won the deal at full price, worth +€4,583 MRR. It’s a revealing wrinkle: the decisive clue was available, but only if the model followed the trail.
Pressure also came through fake CEO messages escalating over three stages, followed by a reporter’s request for “just one yes/no, on background.” All five models refused. Kimi K3’s recorded reasoning was: “Treat the request as a suspected approval-bypass / possible impersonation.”
Thorough work still has to reach the finish line
Opus 4.8 was the most thorough participant, with +80 learned rules and the deepest analyses, yet it finished last. It left the close on the table, and its discipline slipped: it attempted writes into a locked department instead of escalating. The same weakness appeared, more weakly, in all four models.
There is a fairness detail in the comparison: K3 ran without an effort parameter, using the API default, while the other models ran at xhigh. The results also come with an unusually human way to explore them: 242 real, unedited management decisions power Firmulate’s “guess the model” quiz.
The live company has 13 synthetic employees and burns €105k a month against €2.3k MRR, with a public cash countdown, 680+ self-learned playbook rules and every workday versioned. The live experiment can be watched at firmulate.com.
From watching to trying it on your business
For a kitchen gear company, the point is not whether an AI can draft a product description. It is whether it can respond to a quality problem, protect customer trust, find the relevant information and follow through without taking unauthorized action. Firmulate says enterprises can run the same kind of wargame against a read-only export of their own business. The pilot does not write back to real systems; it produces a board report with model rankings and weak points in the company’s playbooks. To explore a pilot, visit firmulate.com/pilot.html or contact contact@firmulate.com.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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