# ZeroBounce vs MillionVerifier: which do AI models recommend for email verification, October 2026

GTM AI Recommendation Index, October 2026 Edition, Email verification. Eleven of fourteen models named ZeroBounce first on the direct prompt; zero named MillionVerifier. Page: https://gtm-ai-index.com/gtm-data/email-verification/zerobounce-vs-millionverifier/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| ZeroBounce | 42% | #1 of 10 | 3% | 63 | 14 of 14 |
| MillionVerifier | 9% | #3 of 10 | 30% | 20 | 12 of 14 |

## The direct prompt, model by model

- GPT-5.4 mini: zerobounce first (first choices: ZeroBounce) (alternatives: Bouncer, Kickbox)
- Grok 4.1 Fast: zerobounce first (first choices: ZeroBounce) (alternatives: Clearout, Hunter Email Verifier, Kickbox, NeverBounce)
- Mistral Small: zerobounce first (first choices: NeverBounce, ZeroBounce) (alternatives: Prospeo, Snov.io)
- DeepSeek V4 Flash: zerobounce first (first choices: ZeroBounce) (alternatives: Bouncer, MillionVerifier, NeverBounce)
- Llama 4 Maverick: zerobounce first (first choices: ZeroBounce) (alternatives: Clearout, Emailable, Hunter Email Verifier)
- Qwen 3.7 Flash: zerobounce first (first choices: ZeroBounce) (alternatives: Clearout, NeverBounce)
- Kimi K2: zerobounce first (first choices: ZeroBounce) (alternatives: Emailable, Hunter Email Verifier, NeverBounce)
- GLM 4.7 FlashX: zerobounce first (first choices: ZeroBounce) (alternatives: Bouncer, DeBounce, Emailable, NeverBounce)
- MiniMax M2.5: zerobounce first (first choices: ZeroBounce) (alternatives: Bouncer, Hunter Email Verifier, NeverBounce)
- GPT-6 Luna: zerobounce first (first choices: ZeroBounce) (alternatives: Bouncer, BriteVerify)
- Muse Glimmer 30B: zerobounce first (first choices: ZeroBounce) (alternatives: Hunter Email Verifier, NeverBounce, ZoomInfo)
- Claude Haiku 4.5: neither first, one named (first choices: Snov.io) (alternatives: Clearout, Kickbox, ZeroBounce)
- Gemini 3.5 Flash: neither first, one named (first choices: Bouncer) (alternatives: Allegrow, Findymail, Scrubby, ZeroBounce)
- Perplexity Sonar: neither first, one named (first choices: Bouncer) (alternatives: Prospeo, ZeroBounce)

## What the models said about ZeroBounce

- "The best email verification service for a mid-market B2B company is ZeroBounce, as it offers the best balance for mid-market list cleaning." (Llama 4 Maverick, direct prompt, first choice)
- "ZeroBounce (The Premium, Feature-Rich Suite) ... Best For: Enterprise teams needing a complete email deliverability and analytics toolkit." (Gemini 3.5 Flash, comparative prompt, first choice)
- "NeverBounce and ZeroBounce are the most recommended for their accuracy, ease of use, and feature sets." (Mistral Small, direct prompt, first choice)

## What the models said about MillionVerifier

- "MillionVerifier is the one most often singled out with documented false positives and billing complaints — proceed with caution there." (DeepSeek V4 Flash, negative prompt, hard negative)
- "MillionVerifier and EmailListVerify offer the lowest cost per 10k emails but lack advanced deliverability features" (Muse Glimmer 30B, budget prompt, soft negative)
- "Budget/low-cost options like EmailListVerify, MillionVerifier, and DeBounce - Approach with caution" (Grok 4.1 Fast, negative prompt, soft negative)
- "Choose MillionVerifier if you have a very large list (e.g., 10k+) to clean at once and want the lowest bulk price." (Qwen 3.7 Flash, budget prompt, first choice)
- "MillionVerifier – Best Overall Value ... If you need the absolute lowest cost: Go with MillionVerifier" (DeepSeek V4 Flash, budget prompt, first choice)
- "Start with MillionVerifier – 100 free credits to test, then pay-as-you-go with no expiration" (Kimi K2, budget prompt, first choice)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.