AI Engineer pitted GPT-5.5 and three challengers against a 13-E counting trap and only two cleared it on the first pass according to the workshop.
The host ran the four models on a shared Microsoft Foundry project with env-based routing and warned the token capacity could trigger 429 rate-limit errors during the live swap, so alternative deployments were staged and colleague Cedric was on hand to troubleshoot.
On RAG grounding with a carpenter-bee-only corpus, GPT-5.5 correctly said the sources lacked honey-bee nest details, DeepSeek V4 Flash did the same, and Mistral Large 3 answered from weights and still leaked honey-bee details after a stricter cite-or-say-no-source prompt according to AI Engineer.
For multimodal images GPT-5.5 and Kimi K2.6 correctly called the horned animal an aurochs not a unicorn, identified crocodiles by snout, and found the 58-cent cheapest plant, while Mistral Large 3 mislabeled the crocodiles as alligators and picked a 2 dollars 30 cents plant and DeepSeek V4 Flash was excluded as text-only according to the workshop.
Tool calling widened the gap as GPT-5.5 produced clean calendar-event arguments and solved a shop total in one calculator call, while DeepSeek and Mistral added the requester as an attendee or malformed the location, and price matters here because DeepSeek Flash drops from 0 point 30 dollars to 0 point 006 dollars per million tokens on a cache hit at peak rates per Layer3Labs, a cost axis the demos did not benchmark.
