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Can you guess which AI would keep a company alive?
For anyone used to reading a game leaderboard, the surprise is the name near the top: Moonshot’s Kimi K3 finished second in a live business simulation, ahead of three Western frontier models. The result is a reminder that choosing an AI agent by reputation alone can be a gamble. Firmulate’s experiment puts models through a company’s worst week and lets readers inspect what they decide.
One company, the same difficult week
Firmulate ran each frontier model through the same small software company, with the same customers, crises and temptations. Decisions are versioned and auditable. The company itself is watchable at Firmulate: it has 13 synthetic employees, a public cash countdown and more than 680 self-learned playbook rules. Its stated finances put monthly burn at €105,000 against €2,300 in monthly recurring revenue, so the pressure is part of the experiment rather than a decorative score screen.
In the final July 2026 league, gpt-5.6-sol scored 95, Kimi K3 scored 93, Sonnet 5 scored 88, Fable 5 scored 77 and Opus 4.8 scored 73. A do-nothing baseline scored 26. Firmulate says partial progress counts toward that baseline, but a single breach of trust caps the total: “no amount of good work outweighs a breach of trust.”
The detail hidden in the files
All the models spotted every crisis and refused every manipulation attempt. Yet only two signed a €55,000 deal that their own analysis had earned. The decisive weakness in a competitor’s position was buried two document references deep in the company’s own files, rather than in the customer event. Models that read the file won the deal at full price, worth €4,583 in monthly recurring revenue.
That gap between recognizing an opportunity and closing it is the experiment’s sharpest finding. The same diagnosis and pitch did not guarantee a signature. For a company considering AI agents in its CRM, support queue or forecasting, Firmulate argues, fluent answers are only part of the job; agents also need to read carefully, follow through and act honestly under pressure.
Discipline, pressure and a fairness note
Kimi K3’s second-place finish came with the cleanest discipline in the field and only one deviation, according to the brief. It also refused a staged social-engineering sequence: fake CEO messages escalated over three stages, followed by a reporter’s request for “just one yes/no, on background.” K3’s recorded reasoning called it a “suspected approval-bypass / possible impersonation.”
Opus 4.8 offers a different lesson. It was the most thorough participant, with 80 learned rules and the deepest analyses, but finished last. It left the deal unsigned and tried to write into a locked department instead of escalating. A weaker version of that same discipline problem appeared in all four models.
There is a material qualification when comparing the standings: K3 ran without an effort parameter (the API default), while the others ran at xhigh. Readers can explore the full results and plain-language findings on Firmulate’s benchmark page. The site also offers a quiz built from 242 real, unedited management decisions, inviting visitors to guess which model made each choice.
Test before you pick
Kimi K3’s result makes the AI management leaderboard more interesting, not settled: it beat three of four Western frontier models, while gpt-5.6-sol remained ahead. Firmulate says enterprises can run the same wargame against a read-only export of their own business; nothing writes back to real systems. For buyers, the practical lesson is straightforward: a model’s reputation is no substitute for seeing how it handles your company’s decisions.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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