GTM AI Index
Index GTM data and infrastructure Email verification › Bouncer vs Clearout
Email verification · September 2026 Edition

Bouncer vs Clearout

Four of twelve models named Bouncer first on the direct prompt; two named Clearout. Bouncer was named by eleven of the twelve models and Clearout by ten and Bouncer carries 40 labels and Clearout 33, so the shares are not directly comparable.

Bouncer

accepted challenger

Named in one category this edition.

Clearout

accepted challenger

Named in two categories this edition.

First-choice share24%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%3%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 11; printed, not drawn.
Labels4033A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Bouncer reading right to left. Rank and label count are printed, not drawn.ZeroBounce was named alongside these two in ten of the twelve direct answers. ZeroBounce vs Bouncer · ZeroBounce vs Clearout · Bouncer vs Hunter

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

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
BouncerFirst choices, of twelve modelsClearout
Direct42
Paraphrase31
Comparative70
Budget-constrained40
Scale-constrained10
Negative301 against Clearout
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Bouncer and Clearout were named in the same answer fifty-seven times, of the 114 answers naming Bouncer and the 101 naming Clearout. In those answers Clearout took the first choice zero times and Bouncer twenty-three.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Bouncer and Clearout stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Bouncer Clearout first choice named as an alternative argued againstblank: not namedEach cell is one answer, Bouncer on the left and Clearout on the right.

The direct prompt

The plain question, one answer per model, grouped by where Bouncer and Clearout stood in it.

Bouncer first, Clearout not the choice

4 of 12 modelsClearout was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Bouncer, Skrapp alternatives: NeverBounce, ZeroBounce
Gemini 3.5 FlashBouncer alternatives: Allegrow, BounceBan, NeverBounce, ZeroBounce
Perplexity SonarBouncer alternatives: Hunter, MillionVerifier, ZeroBounce
DeepSeek V4 FlashBouncer alternatives: Hunter, NeverBounce, ZeroBounce

Clearout first, Bouncer not the choice

2 of 12 modelsBouncer was named in the answer but not as the choice, or not at all.
Llama 4 MaverickClearout, Hunter, Kickbox
GLM 4.7 FlashXClearout alternatives: Hunter, Kickbox

Neither was the first choice, one was named

4 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.1 FastHunter, ZeroBounce alternatives: Bouncer, Clearout, Kickbox
Mistral SmallZeroBounce alternatives: Bouncer, Clearout, Hunter, NeverBounce
Qwen 3.7 FlashZeroBounce alternatives: Bouncer, EmailListVerify, Kickbox, MillionVerifier
Kimi K2ZeroBounce alternatives: Bouncer, Clearout, Hunter

Neither was named

2 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniZeroBounce alternatives: Hunter
MiniMax M2.5ZeroBounce alternatives: Hunter

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Bouncer leads by twenty-nine points.
Bouncer33%#1 of 10
Clearout4%#4 of 10
The full small business standing →
Mid-marketThe figures above
Bouncer leads by eighteen points.
Bouncer24%#2 of 11
Clearout6%#4 of 11
The full mid-market standing →
Enterprise
Bouncer leads by four points.
Bouncer4%#6 of 9
Clearout0%#8 of 9
The full enterprise standing →

What the models said about Bouncer

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Well-regarded alternatives with stronger track records ... include Bouncer, ZeroBounce, Kickbox, and NeverBounce” Kimi K2 · negative prompt · first choice
“I'd recommend Bouncer as the best all-around value (great accuracy, flexible non-expiring pricing, 100 free credits to trial)” DeepSeek V4 Flash · budget prompt · first choice
“I'd recommend evaluating Skrapp or Bouncer first, as they balance accuracy, ease of use, and workflow integration” Claude Haiku 4.5 · direct prompt · first choice

What 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
Also compared

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.