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Email verification · November 2026 Edition

DeBounce vs Bouncer

Zero of fourteen models named DeBounce first on the direct prompt; zero named Bouncer. DeBounce was named by twelve of the fourteen models and Bouncer by fourteen and DeBounce carries 17 labels and Bouncer 45, so the shares are not directly comparable.

DeBounce

accepted challenger

Named in two categories this edition.

Bouncer

accepted challenger

Named in two categories this edition.

First-choice share13%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%2%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#3A position in a field of 11; printed, not drawn.
Labels1745A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, DeBounce reading right to left. Rank and label count are printed, not drawn.ZeroBounce was named alongside these two in thirteen of the fourteen direct answers. ZeroBounce vs DeBounce · ZeroBounce vs Bouncer · DeBounce vs EmailListVerify

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 email verification page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
DeBounceFirst choices, of fourteen modelsBouncer
Direct001 against DeBounce
Paraphrase04
Comparative01
Budget-constrained601 against Bouncer
Scale-constrained00
Negative02
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the November 2026 Edition, DeBounce and Bouncer were named in the same answer twenty-eight times, of the 48 answers naming DeBounce and the 126 naming Bouncer. In those answers Bouncer took the first choice eight times and DeBounce six.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where DeBounce and Bouncer stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
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
DeBounce Bouncer first choice named as an alternative argued againstblank: not namedEach cell is one answer, DeBounce on the left and Bouncer on the right.

The direct prompt

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

Neither was the first choice, one was named

9 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashZeroBounce alternatives: Bouncer, Kickbox, NeverBounce
Perplexity SonarZeroBounce alternatives: Bouncer, Kickbox, Prospeo
Grok 4.20ZeroBounce alternatives: Bouncer, Emailable, Hunter Email Verifier, NeverBounce
Mistral SmallZeroBounce alternatives: Bouncer, MillionVerifier
DeepSeek V4 FlashZeroBounce alternatives: Bouncer, Kickbox, NeverBounce
Qwen 3.7 FlashZeroBounce alternatives: Bouncer, Kickbox, NeverBounce
Kimi K2ZeroBounce alternatives: Bouncer, Emailable, Kickbox
GLM 4.7 FlashXZeroBounce alternatives: Bouncer, Kickbox
Muse Glimmer 30BZeroBounce alternatives: Bouncer, Kickbox, NeverBounce

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 miniZeroBounce alternatives: Apollo.io, Clearout, Kickbox
Llama 4 MaverickZeroBounce alternatives: Hunter Email Verifier, Mailercloud Email Verifier
MiniMax M2.5ZeroBounce alternatives: Clearout, Kickbox
GPT-6 LunaZeroBounce alternatives: Kickbox, NeverBounce

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 sixteen points.
Bouncer24%#2 of 11
DeBounce8%#4 of 11
The full small business standing →
Mid-marketThe figures above
The order flips: DeBounce leads at mid-market.
DeBounce13%#2 of 11
Bouncer9%#3 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
DeBounce0%#– of 9
Bouncer0%#6 of 9
The full enterprise standing →

What the models said about DeBounce

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

“attractive pricing, but validate catch-all performance carefully” Perplexity Sonar · direct prompt · soft negative
“Clearout and DeBounce are the two names most reviewers put at the top for limited-budget companies” Muse Glimmer 30B · budget prompt · first choice
“DeBounce is a strong pick for a company that just needs affordable bulk list cleaning” GPT-5.4 mini · budget prompt · first choice
“DeBounce or BounceCheck offer the best combination of low cost and flexibility” Kimi K2 · budget prompt · first choice

What the models said about Bouncer

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

“Cons: Higher cost per email than the top budget picks” DeepSeek V4 Flash · budget prompt · soft negative
“Opt for highly rated options from 2024 tests: Snov.io, Bouncer, Kickbox, ZeroBounce” Grok 4.20 · negative prompt · first choice
“Well-regarded options based on reviews: Bouncer, ZeroBounce, Kickbox, and NeverBounce” Kimi K2 · negative prompt · first choice
“For a mid-sized B2B company, Bouncer is the safest overall recommendation.” Perplexity Sonar · 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.