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Session replay and heatmaps · November 2026 Edition

Hotjar vs Dealfront

Five of fourteen models named Hotjar first on the direct prompt; zero named Dealfront. Hotjar was named by fourteen of the fourteen models and Dealfront by ten and Hotjar carries 68 labels and Dealfront 10, so the shares are not directly comparable.

Hotjar

criticized challenger

By Contentsquare, Paris, France, founded 2008. Named in nine categories this edition.

Dealfront

accepted challenger

Named in seven categories this edition.

First-choice share16%12%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate25%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 16; printed, not drawn.
Labels6810A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Hotjar reading right to left. Rank and label count are printed, not drawn.Microsoft Clarity was named alongside these two in eleven of the fourteen direct answers. Microsoft Clarity vs Hotjar · Microsoft Clarity vs Dealfront · Hotjar vs Fullstory

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 session replay and heatmaps page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
HotjarFirst choices, of fourteen modelsDealfront
Direct50
Paraphrase161 against Hotjar
Comparative60
Budget-constrained003 against Hotjar
Scale-constrained203 against Hotjar
Negative0010 against Hotjar
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 Hotjar and Dealfront 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
Hotjar Dealfront first choice named as an alternative argued againstblank: not namedEach cell is one answer, Hotjar on the left and Dealfront on the right.

The direct prompt

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

Hotjar first, Dealfront not the choice

5 of 14 modelsDealfront was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Hotjar, Mouseflow alternatives: Fullstory, Microsoft Clarity
Perplexity SonarHotjar alternatives: Amplitude Session Replay, Microsoft Clarity, Mouseflow
Grok 4.20Hotjar alternatives: Microsoft Clarity, Mouseflow
Mistral SmallHotjar alternatives: Fullstory, LogRocket, Microsoft Clarity, PostHog
DeepSeek V4 FlashHotjar alternatives: Amplitude Session Replay, Fullstory, LogRocket, Microsoft Clarity, Mouseflow

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniFullstory alternatives: Contentsquare, Glassbox, Hotjar, LogRocket
Gemini 3.5 FlashPostHog alternatives: Fullstory, Hotjar, LogRocket, Microsoft Clarity
Llama 4 MaverickMouseflow alternatives: Fullstory, Hotjar, Microsoft Clarity, UXCam
Qwen 3.7 FlashLogRocket alternatives: Hotjar, Microsoft Clarity, PostHog
MiniMax M2.5Microsoft Clarity alternatives: Fullstory, Hotjar, LogRocket
Muse Glimmer 30BFullstory alternatives: Contentsquare, Hotjar, Microsoft Clarity

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Kimi K2PostHog alternatives: LogRocket, Mouseflow
GLM 4.7 FlashXMicrosoft Clarity alternatives: Contentsquare, Glassbox, LogRocket, UXCam
GPT-6 LunaFullstory alternatives: Contentsquare, PostHog

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
Hotjar leads by four points.
Hotjar12%#2 of 11
Dealfront8%#– of 11
The full small business standing →
Mid-marketThe figures above
Hotjar leads by four points.
Hotjar16%#2 of 16
Dealfront12%#3 of 16
The full mid-market standing →
Enterprise
Hotjar leads by two points.
Hotjar2%#11 of 12
Dealfront0%#– of 12
The full enterprise standing →

What the models said about Hotjar

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

“Very common but often criticized for broad data capture... Many privacy-focused users block it outright.” Grok 4.20 · negative prompt · hard negative
“Named in active CIPA lawsuits; $5,000 statutory damages per violation possible under California law” Kimi K2 · negative prompt · hard negative
“Documented data exfiltration issues; research found it recorded content beyond input fields” MiniMax M2.5 · negative prompt · hard negative
“Hotjar and Crazy Egg are the category leaders, with Hotjar suited for teams needing comprehensive user behavior analytics with strong heatmaps and session playback” Claude Haiku 4.5 · comparative prompt · first choice
“Hotjar: Strong all-in-one for product/UX teams. Excellent heatmaps, surveys, and easy replays. Great for non-technical users; affordable entry point.” Grok 4.20 · scale prompt · first choice
“For most mid-market B2B companies, Hotjar is the best balance of features, ease of use, and value.” Mistral Small · direct prompt · first choice

What the models said about Dealfront

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.