Zero of fourteen models named RB2B first on the direct prompt; zero named Cognism. RB2B was named by twelve of the fourteen models and Cognism by nine and RB2B carries 20 labels and Cognism 13, so the shares are not directly comparable.
Named in six 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.
| 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. Three of three in this category shown.
“Products such as RB2B, Vector, and Warmly can have narrower geographic coverage, with contact-level identification often limited to the United States” Perplexity Sonar · negative prompt · soft negative
“Install RB2B on your website to identify individual visitors, and use Apollo\u2019s basic paid plan to run searches on company intent topics.” Gemini 3.5 Flash · budget prompt · first choice
“The Best All-Around Choice: RB2B ... widely considered the best ROI tool for small budgets” Qwen 3.7 Flash · budget 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.