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B2B intent data providers · November 2026 Edition

RB2B vs Cognism

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.

RB2B

accepted challenger

Named in six categories this edition.

Cognism

accepted challenger

Named in eight categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate5%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#8A position in a field of 12; printed, not drawn.
Labels2013A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, RB2B reading right to left. Rank and label count are printed, not drawn.Bombora was named alongside these two in thirteen of the fourteen direct answers. Bombora vs RB2B · Bombora vs Cognism · Apollo.io vs RB2B

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
RB2BFirst choices, of fourteen modelsCognism
Direct00
Paraphrase01
Comparative00
Budget-constrained201 against Cognism
Scale-constrained00
Negative011 against RB2B · 1 against Cognism
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where RB2B and Cognism stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
RB2B Cognism first choice named as an alternative argued againstblank: not namedEach cell is one answer, RB2B on the left and Cognism on the right.

The direct prompt

The plain question, one answer per model, grouped by where RB2B and Cognism stood in it.

Neither was the first choice, one was named

1 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashZoomInfo alternatives: Apollo.io, Bombora, Cognism, G2 Buyer Intent

Neither was named

13 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Bombora, G2 Buyer Intent
GPT-5.4 miniBombora alternatives: 6sense, Demandbase
Perplexity SonarBombora alternatives: Dealfront, G2 Buyer Intent, ZoomInfo
Grok 4.20Apollo.io alternatives: Bombora, Dealfront, Factors.ai, G2 Buyer Intent, ZoomInfo
Mistral SmallBombora alternatives: Apollo.io, G2 Buyer Intent, Warmly
DeepSeek V4 FlashAdRoll ABM alternatives: Bombora, G2 Buyer Intent, ZoomInfo + Intent Add-on
Llama 4 MaverickBombora alternatives: 6sense, Apollo.io, G2 Buyer Intent
Qwen 3.7 FlashBombora, Factors.ai alternatives: Apollo.io, Prospeo
Kimi K2G2 Buyer Intent, ZoomInfo Streaming Intent alternatives: Apollo.io, Bombora, Warmly
GLM 4.7 FlashXFactors.ai alternatives: Apollo.io, Dealfront, HubSpot Breeze Intelligence
MiniMax M2.5Bombora alternatives: G2 Buyer Intent, Salesmotion, Sona, TrustRadius
GPT-6 LunaBombora alternatives: 6sense, G2 Buyer Intent
Muse Glimmer 30BHubSpot Breeze Intelligence alternatives: 6sense, Bombora, Salesmotion, TechTarget Priority Engine, ZoomInfo

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
RB2B leads by three points.
RB2B3%#4 of 13
Cognism0%#11 of 13
The full small business standing →
Mid-marketThe figures above
RB2B leads by two points.
RB2B4%#6 of 12
Cognism2%#8 of 12
The full mid-market standing →
Enterprise
The order flips: Cognism leads at enterprise.
Cognism4%#5 of 9
RB2B0%#– of 9
The full enterprise standing →

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.

“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

What the models said about Cognism

No label in this category carried a quote.

Also compared

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.