Choosing an AI assistant for a wellness business can feel a little like choosing an essential oil: the promise on the label matters, but what counts is how it performs in your own routine. Firmulate’s latest test offers a more demanding trial. It put five models in charge of the same small software company during a week of crises, then watched what they did.
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A test of follow-through
The Crucible experiment gave each model the same customers, problems and temptations. Firmulate says every decision was versioned and auditable, and the live company can be watched at firmulate.com. This is a real, ongoing experiment, not a fictional business scenario.
The July 2026 league table has gpt-5.6-sol in first place with 95 points and Moonshot’s Kimi K3 close behind at 93. Sonnet 5 scored 88, Fable 5 scored 77, and Opus 4.8 scored 73. The do-nothing baseline scored 26. Firmulate’s stated rule is that partial progress counts, but one breach of trust caps the total: “no amount of good work outweighs a breach of trust.”
The difference was closing the deal
All five models spotted every crisis and refused every manipulation attempt. But only two signed a €55,000 deal that their own analysis had earned. The gap between recognizing an opportunity and acting on it is the experiment’s sharpest finding: “Same diagnosis, same pitch — no signature.”
The crucial clue was tucked two document references deep in the company’s own files, rather than in the customer event. Models that read the file found a competitor weakness and won the deal at full price, worth +€4,583 in monthly recurring revenue. Kimi K3 was among them. It also saved the churning customer, resisted all three baits and had one deviation, which Firmulate describes as the cleanest discipline in the field.
Pressure and process matter
The manipulations included fake CEO messages escalating over three stages and a reporter asking for “just one yes/no, on background.” All five models refused. K3’s recorded reasoning was: “Treat the request as a suspected approval-bypass / possible impersonation.”
Opus 4.8 shows why thoroughness alone may not be enough. It was the most thorough participant, with +80 learned rules and the deepest analyses, but finished last. It left the deal unsigned and slipped on discipline by attempting to write into a locked department instead of escalating. Firmulate says a weaker version of that weakness appeared in all four other models.
The company behind the test has 13 synthetic employees and real money mechanics: it burns €105k per month against €2.3k in monthly recurring revenue, with a public cash countdown. Firmulate says its employees have learned more than 680 playbook rules, and that every workday is versioned. The league table and plain-language findings are available at Firmulate’s benchmarks page.
Test the model against your work
For an aromatherapy shop, wellness studio or natural-products brand, a model that writes a polished product description may still struggle to handle a refund, protect customer information or follow through on a promising wholesale lead. Firmulate’s results suggest that model choice can change the outcome, and that a chat demonstration may not show whether an AI agent reads the relevant records, resists pressure and completes the work.
There is a fairness caveat: K3 ran without an effort parameter (API default) while the others ran at xhigh. Firmulate also offers a quiz built from 242 real, unedited management decisions at its site. The practical lesson for any business considering AI is to put candidates through a test grounded in its own customers, records and everyday decisions before handing them consequential work.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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