GTM AI Index
Index Marketing ABM › ZenABM vs 6sense
Account-based marketing platforms · September 2026 Edition

ZenABM vs 6sense

Zero of twelve models named ZenABM first on the direct prompt; one named 6sense. ZenABM was named by seven of the twelve models and 6sense by twelve and ZenABM carries 11 labels and 6sense 51, so the shares are not directly comparable.

ZenABM

accepted challenger

Named in two categories this edition.

6sense

criticized challenger

Named in nineteen categories this edition.

First-choice share5%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%45%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 11; printed, not drawn.
Labels1151A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, ZenABM reading right to left. Rank and label count are printed, not drawn.AdRoll ABM was named alongside these two in twelve of the twelve direct answers. AdRoll ABM vs ZenABM · AdRoll ABM vs 6sense · HubSpot ABM vs ZenABM

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.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
ZenABMFirst choices, of twelve models6sense
Direct013 against 6sense
Paraphrase005 against 6sense
Comparative09
Budget-constrained306 against 6sense
Scale-constrained011 against 6sense
Negative008 against 6sense
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where ZenABM and 6sense stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
ZenABM 6sense first choice named as an alternative argued againstblank: not namedEach cell is one answer, ZenABM on the left and 6sense on the right.

The direct prompt

The plain question, one answer per model, grouped by where ZenABM and 6sense stood in it.

6sense first, ZenABM not the choice

1 of 12 modelsZenABM was named in the answer but not as the choice, or not at all.
GPT-5.4 mini6sense alternatives: AdRoll ABM, Demandbase

Neither was the first choice, one was named

5 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5AdRoll ABM, ZoomInfo alternatives: 6sense
Gemini 3.5 FlashAdRoll ABM alternatives: 6sense, Abmatic AI, HubSpot ABM
Perplexity SonarAdRoll ABM alternatives: 6sense, Demandbase
Qwen 3.7 FlashAdRoll ABM alternatives: 6sense, Demandbase, Terminus
Kimi K2AdRoll ABM alternatives: 6sense, DemandScience, HubSpot Marketing Hub

Neither was named

6 of 12 modelsThe answer made no first choice from these two in this category.
Grok 4.1 FastAdRoll ABM alternatives: Demandbase, HubSpot Marketing Hub, Terminus
Mistral SmallAdRoll ABM alternatives: Factors.ai, HockeyStack, HubSpot Native ABM, Terminus
DeepSeek V4 FlashAdRoll ABM, Terminus alternatives: HubSpot ABM
Llama 4 MaverickAdRoll ABM alternatives: 6sense Revenue AI, Demandbase, Metaflow AI, ZoomInfo MarketingOS
GLM 4.7 FlashXAdRoll ABM, HubSpot ABM alternatives: 6sense Revenue AI, Demandbase
MiniMax M2.5AdRoll ABM alternatives: 6sense Revenue AI, Demandbase, Terminus

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
ZenABM leads by thirteen points.
ZenABM13%#4 of 13
6sense0%#12 of 13
The full small business standing →
Mid-marketThe figures above
ZenABM leads by two points.
ZenABM5%#4 of 11
6sense4%#7 of 11
The full mid-market standing →
Enterprise
ZenABM is not named for this buyer.
6sense30%#2 of 8
ZenABMnot named
The full enterprise standing →

What the models said about ZenABM

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“start with RollWorks or ZenABM ... ZenABM if you're LinkedIn-focused and want to start as low as $59/month” Kimi K2 · budget prompt · first choice
“Cheapest full-featured ABM entry point; all core features included” DeepSeek V4 Flash · budget prompt · first choice
“ZenABM Starter: Low-cost LinkedIn ABM engagement” Qwen 3.7 Flash · budget prompt · first choice

What the models said about 6sense

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.

“### 6Sense - Opaque Intent Scores ... Black Box Approach ... Long Implementation: 6+ month implementations common” GLM 4.7 FlashX · negative prompt · hard negative
“avoid Demanbase, 6sense, or Terminus before you have a defined ICP... The most criticized "leader." Opaque, high pricing” DeepSeek V4 Flash · negative prompt · hard negative
“the best ABM platform is usually 6sense if your priority is intent data, predictive account selection, and revenue orchestration at scale” GPT-5.4 mini · direct prompt · first choice
“Deepest predictive intelligence in the category... Best for: Sales-led revenue teams, $100M+ revenue enterprises” DeepSeek V4 Flash · comparative prompt · first choice
“Go with Demandbase or 6sense. They provide the deepest intelligence and cross-channel orchestration.” Qwen 3.7 Flash · comparative prompt · first choice
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.