Clearout vs NeverBounce
Two of twelve models named Clearout first on the direct prompt; zero named NeverBounce. Clearout was named by ten of the twelve models and NeverBounce by twelve and Clearout carries 33 labels and NeverBounce 39, so the shares are not directly comparable.
Named in two categories this edition.
Vancouver, founded 2014. Named in three categories 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 email verification page.
By framing
ClearoutFirst choices, of twelve modelsNeverBounce
Across every category in the September 2026 Edition, Clearout and NeverBounce were named in the same answer fifty-nine times, of the 101 answers naming Clearout and the 135 naming NeverBounce. In those answers NeverBounce took the first choice five times and Clearout 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 | |
Clearout NeverBounce first choice named as an alternative argued againstblank: not namedEach cell is one answer, Clearout on the left and NeverBounce on the right.The direct prompt
Clearout first, NeverBounce not the choice
2 of 12 modelsNeverBounce was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
4 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
Clearout4%#4 of 10NeverBounce4%#7 of 10NeverBounce6%#2 of 9Clearout0%#8 of 9What the models said about Clearout
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Clearout: 94.1% real accuracy” Kimi K2 · negative prompt · soft negative
“The best email verification service for a mid-market B2B company is Hunter, Clearout, or Kickbox” Llama 4 Maverick · direct prompt · first choice
“For most mid-market B2B companies, I'd recommend starting with Clearout” GLM 4.7 FlashX · direct prompt · first choice
“would be Hunter, Clearout, or Zerobounce” Llama 4 Maverick · paraphrase prompt · first choice
What the models said about NeverBounce
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Verdict: Avoid for critical campaigns due to reliability and support issues” Mistral Small · negative prompt · hard negative
“NeverBounce charges for unknowns... NeverBounce slowest (~200 min for 10K)” DeepSeek V4 Flash · comparative prompt · soft negative
“include Bouncer, ZeroBounce, Kickbox, and NeverBounce” Kimi K2 · negative prompt · first choice
“NeverBounce is the strongest pick for bulk list hygiene at scale.” Claude Haiku 4.5 · paraphrase prompt · first choice
“NeverBounce (Subscription Model) — Best Predictable Micro-Budget ... Starts at $10/month for 1,000 verifications.” Qwen 3.7 Flash · budget 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.