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Audience and consumer insights platforms · November 2026 Edition

Audiense vs 6sense

Three of fourteen models named Audiense first on the direct prompt; three named 6sense. Audiense was named by thirteen of the fourteen models and 6sense by eight and Audiense carries 27 labels and 6sense 12, so the shares are not directly comparable.

Audiense

accepted challenger

Named in one category this edition.

6sense

accepted challenger

Named in twenty-eight categories this edition.

First-choice share8%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate11%17%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#5A position in a field of 12; printed, not drawn.
Labels2712A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Audiense reading right to left. Rank and label count are printed, not drawn.SparkToro vs Audiense · SparkToro vs 6sense · GWI vs Audiense

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 audience and consumer insights platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
AudienseFirst choices, of fourteen models6sense
Direct33
Paraphrase102 against 6sense
Comparative10
Budget-constrained003 against Audiense
Scale-constrained00
Negative00
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 Audiense and 6sense 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
Audiense 6sense first choice named as an alternative argued againstblank: not namedEach cell is one answer, Audiense on the left and 6sense on the right.

The direct prompt

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

Audiense first, 6sense not the choice

3 of 14 models6sense was named in the answer but not as the choice, or not at all.
Mistral SmallAudiense, ReachStream alternatives: Wynter
Llama 4 MaverickAudiense
Qwen 3.7 FlashAudiense alternatives: CleverX, ReachStream, SparkToro, Wynter

6sense first, Audiense not the choice

3 of 14 modelsAudiense was named in the answer but not as the choice, or not at all.
GPT-5.4 mini6sense alternatives: Brandwatch Consumer Intelligence, GWI, Meltwater
Perplexity Sonar6sense alternatives: Bombora, ZoomInfo
Kimi K26sense alternatives: Apollo.io, Demandbase, ZoomInfo

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashApollo.io, SparkToro alternatives: 6sense, Bombora, Factors.ai, Koala, ZoomInfo
Grok 4.20GWI alternatives: Audiense, Cognism, HG Insights, Resonate, ZoomInfo
DeepSeek V4 FlashGWI alternatives: 6sense, Apollo.io, Brandwatch Consumer Intelligence, Meltwater
Muse Glimmer 30BSparkToro alternatives: Audiense, Brandwatch Consumer Intelligence, GWI, ReachStream, ZoomInfo

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5LinkedIn Analytics alternatives: Bombora, Factors.ai, GWI, HG Insights, HubSpot ABM
GLM 4.7 FlashXAdRoll ABM alternatives: Apollo.io, Bombora, Cognism, G2 Buyer Intent, GWI, N.Rich, UpLead
MiniMax M2.5Apollo.io alternatives: LinkedIn Sales Navigator, ZoomInfo
GPT-6 LunaWynter alternatives: AdRoll ABM, SparkToro

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
Audiense leads by two points.
Audiense2%#7 of 18
6sense0%#– of 18
The full small business standing →
Mid-marketThe figures above
Audiense leads by two points.
Audiense8%#3 of 12
6sense6%#5 of 12
The full mid-market standing →
Enterprise
The order flips: 6sense leads at enterprise.
6sense14%#3 of 16
Audiense2%#7 of 16
The full enterprise standing →

What the models said about Audiense

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

“Avoid enterprise-tier tools like Brandwatch, Sprinklr, Audiense, or Resonate” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid enterprise platforms such as GWI, Brandwatch, and Audiense” Perplexity Sonar · budget prompt · hard negative
“far cheaper than enterprise tools like Audiense (~$8K+/yr)” GLM 4.7 FlashX · budget prompt · soft negative
“Audiense is described as "a social intelligence powerhouse… uses advanced psychographic profiling through IBM Watson integration."” Muse Glimmer 30B · comparative prompt · first choice
“Audiense and ReachStream are the top choices due to their strong focus on B2B audience segmentation and data accuracy” Mistral Small · direct prompt · first choice
“Best for Audience Composition & Social Insight: Audiense ... widely recommended for B2B” Qwen 3.7 Flash · direct 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. One of one in this category shown.

“the best choice is usually 6sense if your main goal is account-based audience insights tied to pipeline and revenue” GPT-5.4 mini · direct 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.