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

6sense vs Factors.ai

One of twelve models named 6sense first on the direct prompt; zero named Factors.ai. 6sense was named by twelve of the twelve models and Factors.ai by six and 6sense carries 51 labels and Factors.ai 11, so the shares are not directly comparable.

6sense

criticized challenger

Named in nineteen categories this edition.

Factors.ai

accepted challenger

Named in seven categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate45%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 11; printed, not drawn.
Labels5111A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, 6sense 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 6sense · AdRoll ABM vs Factors.ai · HubSpot ABM vs 6sense

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.
6senseFirst choices, of twelve modelsFactors.ai
Direct103 against 6sense
Paraphrase005 against 6sense
Comparative90
Budget-constrained006 against 6sense
Scale-constrained111 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 6sense and Factors.ai 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
6sense Factors.ai first choice named as an alternative argued againstblank: not namedEach cell is one answer, 6sense on the left and Factors.ai on the right.

The direct prompt

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

6sense first, Factors.ai not the choice

1 of 12 modelsFactors.ai 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

6 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
Mistral SmallAdRoll ABM alternatives: Factors.ai, HockeyStack, HubSpot Native ABM, Terminus
Qwen 3.7 FlashAdRoll ABM alternatives: 6sense, Demandbase, Terminus
Kimi K2AdRoll ABM alternatives: 6sense, DemandScience, HubSpot Marketing Hub

Neither was named

5 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
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
Factors.ai leads by two points.
Factors.ai2%#6 of 13
6sense0%#12 of 13
The full small business standing →
Mid-marketThe figures above
The order flips: 6sense leads at mid-market.
6sense4%#7 of 11
Factors.ai2%#8 of 11
The full mid-market standing →
Enterprise
6sense leads by thirty points.
6sense30%#2 of 8
Factors.ai0%#– of 8
The full enterprise standing →

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

What the models said about Factors.ai

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

“Best for: A 500-person company wanting a balance of power and operational simplicity.” Gemini 3.5 Flash · scale prompt · first choice
“Startups and small teams should start with affordable, focused tools like Factors.ai, RollWorks, or HubSpot.” Claude Haiku 4.5 · budget prompt · alternative
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.