Five of fourteen models named ZoomInfo first on the direct prompt; three named HubSpot Marketing Hub. ZoomInfo was named by thirteen of the fourteen models and HubSpot Marketing Hub by seven and ZoomInfo carries 44 labels and HubSpot Marketing Hub 13, so the shares are not directly comparable.
Vancouver, Washington, United States, founded 2000. Named in fifteen categories this edition.
By HubSpot, Cambridge, United States, founded 2006. Named in twenty-nine 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 market intelligence platforms page.
| 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. Two of two in this category shown.
“Best for B2B GTM teams that need company and contact intelligence, technographics, and intent to run outbound and account-based motions” Claude Haiku 4.5 · direct prompt · first choice
“For most mid-market B2B companies, ZoomInfo Sales offers the best balance of coverage, ease of use, and workflow integration.” MiniMax M2.5 · direct prompt · first choice
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.