Clearout vs DeBounce
Two of twelve models named Clearout first on the direct prompt; zero named DeBounce. Clearout was named by ten of the twelve models and DeBounce by nine and Clearout carries 33 labels and DeBounce 17, so the shares are not directly comparable.
Named in two categories 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 email verification page.
By framing
ClearoutFirst choices, of twelve modelsDeBounce
Across every category in the September 2026 Edition, Clearout and DeBounce were named in the same answer twenty-seven times, of the 101 answers naming Clearout and the 48 naming DeBounce. In those answers DeBounce took the first choice two times and Clearout zero.
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 DeBounce first choice named as an alternative argued againstblank: not namedEach cell is one answer, Clearout on the left and DeBounce on the right.The direct prompt
Clearout first, DeBounce not the choice
2 of 12 modelsDeBounce was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 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
Clearout0%#8 of 9DeBounce0%#– 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 DeBounce
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Best for: Cost-sensitive users (accuracy may vary)” Mistral Small · comparative prompt · soft negative
“DeBounce: 94.0% real accuracy” Kimi K2 · negative prompt · soft negative
“For most businesses, DeBounce or MyEmailVerifier offer the best balance of cost, accuracy, and features.” MiniMax M2.5 · budget prompt · first choice
“DeBounce – Best Overall Value ... one of the cheapest rates available” Kimi K2 · budget prompt · first choice
“DeBounce — Best for Bulk List Cleaning ... aggressive bulk pricing while maintaining industry-standard accuracy” 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.