Dealfront vs RB2B
Three of fourteen models named Dealfront first on the direct prompt; zero named RB2B. Dealfront was named by fourteen of the fourteen models and RB2B by eight and Dealfront carries 37 labels and RB2B 14, so the shares are not directly comparable.
Named in ten categories this edition.
Named in five 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.
By framing
DealfrontFirst choices, of fourteen modelsRB2B
Across every category in the October 2026 Edition, Dealfront and RB2B were named in the same answer fifty-one times, of the 171 answers naming Dealfront and the 97 naming RB2B. In those answers RB2B took the first choice seven times and Dealfront one.
Every model, every framing
| 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 | | | |
Dealfront RB2B first choice named as an alternative argued againstblank: not namedEach cell is one answer, Dealfront on the left and RB2B on the right.The direct prompt
Dealfront first, RB2B not the choice
3 of 14 modelsRB2B was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 of 14 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
Dealfront0%#– of 9RB2B0%#– of 9What the models said about Dealfront
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“For a mid-sized B2B company, Dealfront offers the best balance of intent visibility, mid-market focus, and affordability.” GLM 4.7 FlashX · paraphrase prompt · first choice
“combine Apollo.io (to find the right accounts) with Leadfeeder (to verify interest)” Qwen 3.7 Flash · budget prompt · first choice
What the models said about RB2B
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“I'd recommend RB2B if you have meaningful website traffic and want person-level identification” DeepSeek V4 Flash · budget prompt · first choice
“If you have zero budget but some website traffic: Set up RB2B (for US traffic)” Gemini 3.5 Flash · budget prompt · first choice
“the practical stack is usually Apollo.io or RB2B for coverage” Muse Glimmer 30B · budget prompt · first choice
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.