iubenda vs Ketch
One of fourteen models named iubenda first on the direct prompt; zero named Ketch. iubenda was named by fourteen of the fourteen models and Ketch by thirteen and iubenda carries 34 labels and Ketch 33, so the shares are not directly comparable.
Italy. 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 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 consent and privacy management page.
By framing
iubendaFirst choices, of fourteen modelsKetch
Across every category in the November 2026 Edition, iubenda and Ketch were named in the same answer twenty-one times, of the 83 answers naming iubenda and the 87 naming Ketch. In those answers Ketch took the first choice one time and iubenda two.
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 4.20 | | | | | ||
| 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 | | | |
iubenda Ketch first choice named as an alternative argued againstblank: not namedEach cell is one answer, iubenda on the left and Ketch on the right.The direct prompt
iubenda first, Ketch not the choice
1 of 14 modelsKetch was named in the answer but not as the choice, or not at all.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.Neither was named
7 of 14 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
iubenda18%#3 of 11Ketch0%#– of 11Ketch8%#2 of 9iubenda0%#– of 9What the models said about iubenda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Limited advanced developer capabilities & publisher controls.” Gemini 3.5 Flash · negative prompt · soft negative
“Be especially cautious with iubenda's TCF activator” GLM 4.7 FlashX · negative prompt · soft negative
“Tools like Iubenda get mixed feedback for complexity.” Grok 4.20 · negative prompt · soft negative
“Iubenda: Best for small to mid-sized SaaS teams that need attorney-quality compliance without a legal vendor.” Llama 4 Maverick · direct prompt · first choice
“For most small businesses with a limited budget, CookieYes or iubenda will be the best fit” DeepSeek V4 Flash · budget prompt · first choice
“Cookiebot, CookieYes, and iubenda are top choices” Mistral Small · budget prompt · first choice
What the models said about Ketch
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“its market share and user preference have declined significantly (from 24% to 10% in recent evaluations), and it has faced criticism for not being as user-friendly or effective as competitors” Mistral Small · negative prompt · soft negative
“I recommend Ketch or Cookiebot by Usercentrics as the top cookie consent and privacy compliance tools” Mistral Small · paraphrase prompt · first choice
“Ketch is API-first CMP that handles consent propagation to downstream systems like Salesforce through API-driven architecture” Claude Haiku 4.5 · comparative prompt · alternative
“Choose Ketch when consent must be enforced consistently across backend systems” Perplexity Sonar · comparative prompt · alternative
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.