Fraud Blocker vs Clixtell
Zero of fourteen models named Fraud Blocker first on the direct prompt; zero named Clixtell. Fraud Blocker was named by fourteen of the fourteen models and Clixtell by ten and Fraud Blocker carries 34 labels and Clixtell 13, 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 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 click fraud protection page.
By framing
Fraud BlockerFirst choices, of fourteen modelsClixtell
Across every category in the October 2026 Edition, Fraud Blocker and Clixtell were named in the same answer twenty-six times, of the 92 answers naming Fraud Blocker and the 43 naming Clixtell. In those answers Clixtell took the first choice four times and Fraud Blocker one.
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 | | | ||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | | |
Fraud Blocker Clixtell first choice named as an alternative argued againstblank: not namedEach cell is one answer, Fraud Blocker on the left and Clixtell on the right.The direct prompt
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.Neither was named
10 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
Clixtell8%#2 of 10Fraud Blocker6%#3 of 10Fraud Blocker8%#3 of 9Clixtell6%#4 of 9Fraud Blocker4%#5 of 12Clixtell0%#11 of 12What the models said about Fraud Blocker
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Fraud Blocker lacks Microsoft Ads coverage; watch for channel gaps matching your needs” Grok 4.1 Fast · negative prompt · soft negative
“Worth comparing on price, but less consistently recommended” Perplexity Sonar · budget prompt · soft negative
“Microsoft Ads important? | Avoid Fraud Blocker” Kimi K2 · direct prompt · soft negative
“Fraud Blocker and ClickPatrol stand out as top choices for small businesses with limited budgets” Mistral Small · budget prompt · first choice
“The best click fraud protection tool for a company with a limited budget is Fraud Blocker.” Llama 4 Maverick · budget prompt · first choice
“or Fraud Blocker if you want more comprehensive features at a slightly higher cost” Claude Haiku 4.5 · budget prompt · first choice
What the models said about Clixtell
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“the fraud detection capabilities are more basic compared to dedicated solutions” DeepSeek V4 Flash · negative prompt · soft negative
“such as standard tiers of ClickCease, ClickGuard, or Clixtell) with caution” Gemini 3.5 Flash · negative prompt · soft negative
“If you need the absolute lowest monthly spend, Clixtell is the cheapest named entry in current round-ups.” Muse Glimmer 30B · budget prompt · first choice
“Clixtell at $10/month offers the best entry point if you need basic protection” Claude Haiku 4.5 · budget prompt · first choice
“Top Recommendation: Clixtell (from ~$10–15/month)” DeepSeek V4 Flash · budget 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.