What Hunter's Email Finder returns on a hundred names

A hundred names sit in your spreadsheet with no email addresses next to them. Hunter's free plan is open in the other tab, offering 50 credits a month, and the question is whether that gets you a working list or half of one.

Half of one, and Hunter has published the number.

In a post benchmarking language models against its own database, published 1 September 2026 and updated on the 4th, Hunter writes: "Hunter, on the same 305 tasks, returned a verified, valid contact 64% of the time." That figure appears on no product page, no pricing page and no help article. It is the single most useful thing Hunter has published about the Email Finder, and it is sitting in a competitive post about Llama and Gemini.

64%, and what it is a percentage of

The population matters, so take it exactly as it is. The 305 tasks were requests for a named contact at a named company, run as a benchmark against consumer and self-hosted language models. Hunter's own scoreboard from the same test, counting both figures against the same set of requests: Llama returned an address for 33% of requests and a valid one for 1%, Qwen 15% and 1%, Gemma 2% and 0.1%. Hunter's 64% is the top line of a test Hunter designed and published, so treat it as a vendor's best case rather than a neutral audit.

Read it precisely: 64% is how often Hunter returned an address that its own verifier then judged valid, not how often it returned anything at all. That is the number that matters to you, because an address Hunter cannot vouch for is not a row you can send to. On a hundred names, budget for around sixty-four usable addresses and a gap of thirty-six that no amount of credits will close. The gap is not a billing problem. It is the part of your list that needs a different route, whether that is a second data source, a LinkedIn message, or dropping the account.

A miss is free, which is not the same as cheap

Hunter's charging rule is simple and unusually fair. One search that returns an email costs one search credit. If the Email Finder fails to return a result, it is free, and the API documentation says the same thing: no email found, no credit charged. Webmail searches are free too, because Hunter only looks for professional addresses.

Verification comes bundled. For the Email Finder and the Bulk Email Finder, the verification is included in the same credit that found the address, so you are not paying twice for one row.

Two things about that rule cost real money anyway.

The first is what a credit buys when the address is a guess. Hunter returns pattern-derived addresses under an "Inferred" tag, built from public data at the domain level and then run through its own verifier. Hunter's pricing rule makes no distinction: one credit is one email found, whether Hunter can show you a public source for it or worked it out from the company's naming pattern. No Hunter page we read states that distinction explicitly, so read it as arithmetic from the two rules rather than as something Hunter says.

The second is a deduplication gap that only bites at scale. Repeat searches are free within a billing period, and free across your whole team, which is generous. But Hunter's help centre states that it does not detect duplicates across different bulk CSV uploads. Upload the same list twice in two files and you pay twice.

On the free plan, 50 credits a month clears about 78 names at that rate, and that is an upper bound rather than a promise: credits are charged on every address returned, including accept-all rows that never enter the 64%, so a list with many accept-all domains clears fewer. Your hundred does not fit in one month. Hunter's paid entry tier, Starter, is $49 monthly or $34 a month billed annually, and carries 2,000 credits a month. Worth knowing because the comparison pages get this wrong: Instantly's alternatives post, which ranks on this same query, describes Starter as 500 searches.

The score Hunter tells you not to use

Every result comes back with a confidence score, and it is the number your eye goes to. Hunter's own instruction is to ignore it on most rows.

The help centre is direct about it: "We recommend using confidence scores only for accept-all emails, and relying on the email status for other types such as valid or invalid emails."

The reason is what the score is made of. Hunter defines it as an indication of "how likely an email address is to be valid", updated continuously "based on the quality and quantity of public data points associated with each email address". Its other help article describes the same score as coming from how well the address format matches the naming conventions Hunter has seen at that domain, plus whether that address has been seen engaging elsewhere. Neither is a live check of the mailbox.

Follow that through and the counterintuitive case falls out. A company with a rigid firstname.lastname pattern will produce high-scoring addresses for people whose mailbox may not exist, because the score is partly measuring the strength of the pattern rather than the existence of the person. A high score on a guessed address is a statement about the domain.

Where the score does work, Hunter gives two numbers for two different jobs, in the same paragraph. Accept-all addresses at 90 to 95% or higher are "generally more likely to be deliverable", and 85% is the floor below which it suggests filtering to reduce bounces. One is a guide, the other is a cutoff. They are not in conflict, whatever the review pages make of them.

Accept-all is the case nobody can answer

Accept-all domains take delivery of everything at the SMTP door and sort it out afterwards, which means no verifier can tell you whether a specific mailbox exists. Hunter says so about itself: no verification tool, including Hunter, can fully confirm deliverability on these domains.

This is the one place the confidence score earns its keep, and also the place where the bounce risk concentrates. Hunter's own figure is that accept-all addresses are roughly 27 times more likely to bounce than its verified-valid ones, 27% against 1%. If a meaningful share of your hundred names sit on accept-all domains, that is where your bounce rate is coming from, and what to do with those rows is a separate decision from which finder you buy.

What the 91.3% number actually measured

The most-cited accuracy figure for Hunter right now comes from a review at growthhacksuite, last modified 14 August 2026, reporting a 91.3% valid rate across 1,000 B2B email addresses. It is a real test and it does not answer the question on this page.

Its 1,000 addresses were collected from company websites and LinkedIn profiles, then run through Hunter's Email Verifier. That is a test of the verifier's verdicts on a list somebody else built by hand. It reports no find rate, because finding was never part of it. The two numbers measure opposite halves of the job: 64% is how often Hunter produced an address it could vouch for, 91.3% is how often it judged an address someone else had already found. A tool can be excellent at the second and leave a third of your list empty.

A hundred names, priced

Start from 64 addresses and 36 blanks. On the free plan, at best 50 credits gets you roughly 78 of the hundred processed and about 50 usable addresses, spread across two months, and fewer if the list is heavy with accept-all domains. On Starter at $34 a month billed annually, the hundred costs at least 64 credits out of 2,000, which is to say the credits are not the constraint. The constraint is the 36.

That is the honest shape of every finder, not a complaint about this one. What the misses cost you is a decision per row, and it is worth making it by rule rather than one at a time.

The rule that works at this size: sort the 36 by whether you would take the meeting. For the handful you would clear a morning for, spend the time on LinkedIn or the company's own contact page, both free. For the rest, leave them. A second data source costs another subscription to recover names you were ambivalent about, and the arithmetic on a hundred names rarely justifies it. At a few thousand names it does, which is where running one list through several sources in sequence starts to pay, and Hunter names two reasons for a blank: no public data on the domain, or no match its algorithm would stand behind. Only the second is specific to Hunter. What they should not cost you is a bounce, and the difference between an address that exists and one that verifies is where that risk lives.

One caveat on the free plan itself. Hunter's pricing page and its free-plan help article describe the same plan's saved-lead limits differently, one listing 100,000 saved leads and the other capping saved companies at 100. We could not resolve which governs, so plan around the 50 credits, which both agree on.