Zero of fourteen models named Microsoft Clarity first on the direct prompt; one named Contentsquare. Microsoft Clarity was named by fourteen of the fourteen models and Contentsquare by twelve and Microsoft Clarity carries 59 labels and Contentsquare 21, so the shares are not directly comparable.
By Microsoft, Vancouver, Canada, founded 1975. Named in five categories this edition.
Paris, France, founded 2008. Named in six categories this edition.
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.
Across every category in the October 2026 Edition, Microsoft Clarity and Contentsquare were named in the same answer sixty times, of the 229 answers naming Microsoft Clarity and the 169 naming Contentsquare. In those answers Contentsquare took the first choice eight times and Microsoft Clarity twenty-four.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Both Hotjar (now part of Contentsquare) and Microsoft Clarity are consent-agnostic by default: paste the snippet in and they start recording on the first pageview.” Muse Glimmer 30B · negative prompt · hard negative
“"Free" but with a substantial tracking footprint that privacy teams should scrutinize; starts recording immediately without waiting for consent” Kimi K2 · negative prompt · hard negative
“"Free" option with aggressive tracking... Limited default redaction; privacy teams flag its footprint. US-hosted; consent bypass risks in EU.” Grok 4.1 Fast · negative prompt · hard negative
“Microsoft Clarity is the best free option for behaviour analytics, with unlimited session recordings and heatmaps and no paid tier required.” Muse Glimmer 30B · paraphrase prompt · first choice
“Microsoft Clarity delivers unlimited session replays and heatmaps at no cost, making it a favorite for startups and high-traffic sites.” Muse Glimmer 30B · comparative prompt · first choice
“The best session replay and heatmap tool for a company with a limited budget is Microsoft Clarity, as it is free with no session cap.” Llama 4 Maverick · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“Heavy enterprise pricing and complexity; overkill for most mid-market needs” Kimi K2 · direct prompt · hard negative
“enterprise tools only if you need deeper product analytics, governance, or enterprise workflows, because they're generally much more expensive” GPT-5.4 mini · budget prompt · soft negative
“without paying the $50k+ annual contract values required by enterprise-only suites (like FullStory or Contentsquare)” Gemini 3.5 Flash · direct prompt · soft negative
“my default recommendation is Contentsquare if you want the most complete, scalable experience-analytics platform” GPT-5.4 mini · direct prompt · first choice
“Go with FullStory or Contentsquare for deep session replay and journey analytics.” GLM 4.7 FlashX · comparative prompt · first choice
“The sweet spot is usually FullStory, Contentsquare, or LogRocket” DeepSeek V4 Flash · scale prompt · first choice
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.