Profound vs Scrunch
Zero of twelve models named Profound first on the direct prompt; one named Scrunch. Profound was named by twelve of the twelve models and Scrunch by nine and Profound carries 37 labels and Scrunch 21, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
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 AI visibility and answer engine tracking tools page.
By framing
ProfoundFirst choices, of twelve modelsScrunch
Across every category in the September 2026 Edition, Profound and Scrunch were named in the same answer fifty-one times, of the 126 answers naming Profound and the 56 naming Scrunch. In those answers Scrunch took the first choice two times and Profound twenty-four.
Every model, every framing
| 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 | | | |
Profound Scrunch first choice named as an alternative argued againstblank: not namedEach cell is one answer, Profound on the left and Scrunch on the right.The direct prompt
Scrunch first, Profound not the choice
1 of 12 modelsProfound was named in the answer but not as the choice, or not at all.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.Neither was named
6 of 12 modelsThe answer made no first choice from these two in this category.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
Profound0%#9 of 9Scrunch0%#– of 9What the models said about Profound
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Price issue: $499/month for Lite plan is 48% more expensive than the category average... G2 reviews cite instability, slowness, and overwhelming dashboards” Kimi K2 · negative prompt · hard negative
“Profound: Major red flags—allegations of using browser extension clickstream data ... experts call it ethically questionable” Grok 4.1 Fast · negative prompt · hard negative
“Avoid enterprise tools like Profound ($499+/month)” Qwen 3.7 Flash · budget prompt · hard negative
“Shortlist 3-5: Profound (enterprise security), AirOps (tracking-to-content loop), Semrush/Ahrefs (if you use SEO tools).” Grok 4.1 Fast · scale prompt · first choice
“Enterprise-grade with data depth and competitive intelligence | Large enterprises needing comprehensive tracking” GLM 4.7 FlashX · comparative prompt · first choice
“"We're an enterprise—we need every possible engine covered." → Profound or Peec AI” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about Scrunch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Avoid enterprise tools like Profound ($499+/month) or Scrunch AI ($250/month)” Qwen 3.7 Flash · budget prompt · hard negative
“Profound or Scrunch AI. Both provide deep developer APIs, agent analytics” Gemini 3.5 Flash · scale prompt · first choice
“it is recommended to look at tools such as Profound, Scrunch, and AthenaHQ” Llama 4 Maverick · scale prompt · first choice
“I recommend Scrunch as the primary choice for pure-play AI visibility” Qwen 3.7 Flash · paraphrase 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.