One of fourteen models named HubSpot Breeze Intelligence first on the direct prompt; zero named Cognism. HubSpot Breeze Intelligence was named by eight of the fourteen models and Cognism by nine and HubSpot Breeze Intelligence carries 11 labels and Cognism 13, so the shares are not directly comparable.
By HubSpot, Cambridge, United States, founded 2006. Named in eleven categories this edition.
Named in eight categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen 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 B2B intent data providers page.
Across every category in the November 2026 Edition, HubSpot Breeze Intelligence and Cognism were named in the same answer 135 times, of the 258 answers naming HubSpot Breeze Intelligence and the 397 naming Cognism. In those answers Cognism took the first choice eight times and HubSpot Breeze Intelligence four.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
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. One of one in this category shown.
“Premium product; reviewers note it's expensive for smaller teams” Kimi K2 · budget prompt · hard negative
No label in this category carried a quote.
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.