One of twelve models named HubSpot ABM first on the direct prompt; zero named Apollo.io. HubSpot ABM was named by eleven of the twelve models and Apollo.io by ten and HubSpot ABM carries 28 labels and Apollo.io 15, so the shares are not directly comparable.
By HubSpot, Cambridge, United States, founded 2006. Named in one category this edition.
San Francisco, United States, founded 2015. Named in twenty 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 account-based marketing 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 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 |
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 four in this category shown.
“HubSpot if you want to integrate ABM into your existing CRM workflow with minimal additional complexity. Both offer the best balance of cost, ease of use, and mid-market suitability.” GLM 4.7 FlashX · direct prompt · first choice
“or HubSpot if you already use its CRM and want the lowest-friction option” Perplexity Sonar · budget prompt · first choice
“| HubSpot ABM | Bundled in Premium | Existing HubSpot users |” Grok 4.1 Fast · scale 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.