Three of twelve models named CaptivateIQ first on the direct prompt; one named QuotaPath. CaptivateIQ was named by twelve of the twelve models and QuotaPath by eight and CaptivateIQ carries 44 labels and QuotaPath 23, so the shares are not directly comparable.
Named in three categories this edition.
Named in two categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the territory and quota planning page.
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Mid-market RevOps tools (~$20K+/year or $2,400+/user/year).” DeepSeek V4 Flash · budget prompt · hard negative
“End-to-End Revenue Plan | CaptivateIQ, Forma.ai | Combines territory carving, quota setting, and variable comp in a single agile platform.” Qwen 3.7 Flash · scale prompt · first choice
“CaptivateIQ is widely considered the strongest option for mid-market teams because it eliminates the need for multiple disconnected tools.” Qwen 3.7 Flash · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Accessible pricing (~$35/user/mo) ... but light on territory design/capacity modeling.” DeepSeek V4 Flash · direct prompt · soft negative
“Fullcast (if Salesforce-native) or QuotaPath (for best value and flexibility) would be the strongest choices” Mistral Small · direct prompt · first choice
“I'd recommend QuotaPath or CaptivateIQ for a mid-sized B2B company” GLM 4.7 FlashX · paraphrase prompt · first choice
Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.