Bombora vs ZoomInfo
Eight of twelve models named Bombora first on the direct prompt; one named ZoomInfo. Both were named by all twelve models and Bombora carries 56 labels and ZoomInfo 37, so the shares are not directly comparable.
Named in six categories this edition.
Vancouver, Washington, United States, founded 2000. Named in eighteen 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 B2B intent data providers page.
By framing
BomboraFirst choices, of twelve modelsZoomInfo
Across every category in the September 2026 Edition, Bombora and ZoomInfo were named in the same answer 148 times, of the 225 answers naming Bombora and the 675 naming ZoomInfo. In those answers ZoomInfo took the first choice twenty times and Bombora thirty-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 4.1 Fast | | | | | | |
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | | |
| MiniMax M2.5 | | | | |
Bombora ZoomInfo first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bombora on the left and ZoomInfo on the right.The direct prompt
Both were the first choice
1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.Bombora first, ZoomInfo an alternative
7 of 12 modelsZoomInfo was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 12 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
Bombora35%#1 of 14ZoomInfo4%#5 of 14What the models said about Bombora
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid: Enterprise platforms like ... Bombora ($25K-$75K/year)” GLM 4.7 FlashX · budget prompt · hard negative
“What to Avoid on a Limited Budget ... Bombora (~$25K+/yr)” Kimi K2 · budget prompt · hard negative
“Skip the enterprise platforms (Bombora, 6sense, Demandbase, ZoomInfo) unless you have five-figure annual budget” DeepSeek V4 Flash · budget prompt · soft negative
“For pure lead generation/scoring: Go with Bombora. It provides the cleanest, most standardized dataset for most mid-market ABM strategies.” Qwen 3.7 Flash · paraphrase prompt · first choice
“The industry standard for third-party cooperative intent... The "market standard" co-op network used by most large enterprises.” Qwen 3.7 Flash · comparative prompt · first choice
“Bombora is widely considered the industry standard and best starting point due to its data quality, coverage, and integrations” Qwen 3.7 Flash · direct prompt · first choice
What the models said about ZoomInfo
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“here are the B2B intent data providers ... you should approach with caution ... ZoomInfo (Specifically "Streaming Intent") ... High False Positive Rate” Qwen 3.7 Flash · negative prompt · hard negative
“ZoomInfo Intent: Streaming signals criticized as lowest quality (Bombora-resold + web), inaccurate revenue/employee data, buggy.” Grok 4.1 Fast · negative prompt · hard negative
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.