EmailListVerify vs NeverBounce
Zero of twelve models named EmailListVerify first on the direct prompt; zero named NeverBounce. EmailListVerify was named by ten of the twelve models and NeverBounce by twelve and EmailListVerify carries 20 labels and NeverBounce 39, so the shares are not directly comparable.
Named in one category 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
EmailListVerifyFirst choices, of twelve modelsNeverBounce
Across every category in the September 2026 Edition, EmailListVerify and NeverBounce were named in the same answer twenty-seven times, of the 49 answers naming EmailListVerify and the 135 naming NeverBounce. In those answers NeverBounce took the first choice two times and EmailListVerify 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 4.1 Fast | | | | | ||
| Mistral Small | | | | | | |
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | ||||
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | |
EmailListVerify NeverBounce first choice named as an alternative argued againstblank: not namedEach cell is one answer, EmailListVerify on the left and NeverBounce on the right.The direct prompt
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
7 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
EmailListVerify4%#6 of 10NeverBounce4%#7 of 10EmailListVerify4%#6 of 11NeverBounce2%#8 of 11NeverBounce6%#2 of 9EmailListVerify0%#– of 9What the models said about EmailListVerify
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“A classic budget option, though some independent audits show lower accuracy than top-tier competitors.” Qwen 3.7 Flash · comparative prompt · soft negative
“EmailListVerify: 93.2% real accuracy (lowest tested)” Kimi K2 · negative prompt · soft negative
“the best choice is usually EmailListVerify if you want the lowest cost per verification” Perplexity Sonar · budget prompt · first choice
“For the absolute lowest cost: Choose EmailListVerify at $24 for 10K emails.” Mistral Small · budget prompt · first choice
“the budget option on this list and delivers accordingly, with free supplementary tools adding real value” Claude Haiku 4.5 · budget prompt · alternative
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.