Contact dataSeptember 2026

B2B data provider benchmark, September 2026

This benchmark measures B2B contact data providers on coverage, accuracy, stale records and results by segment, using one shared list of LinkedIn profiles. The September 2026 edition compares Apollo, BlitzAPI, GetLeads.io, MoltSets, Prospeo and QuickEnrich.

Run by BlitzAPI, one of the 6 providers measured. The reference every answer is scored against comes from none of them. How to check us

Measured on September 17, 2026 on 3,445 profilesQueried on September 17 and 18, 20266 providers rankedProtocol v1

01

The ranking: accurate records per 100 contacts

One number ranks these providers, and it is the only one you can budget against: out of 100 contacts you send, how many come back with the right current employer and a matching title. It is coverage multiplied by accuracy, so nobody can lift it by answering less often, or by answering more loosely.

Out of 100 contacts, the strongest provider hands back 82.4 usable records and the weakest 31.9. The rest were either returned but not verified accurate, or never returned.

out of 100 contacts queried

AccurateReturned, not verified accurateNever returned

1BlitzAPI90% coverage × 91% accuracy82.4±1.3
2Apollo97% coverage × 72% accuracy70.0±1.5
3Prospeo95% coverage × 56% accuracy52.7±1.7
4QuickEnrich69% coverage × 64% accuracy43.8±1.7
5MoltSets85% coverage × 52% accuracy43.7±1.7
6GetLeads.io81% coverage × 39% accuracy31.9±1.6
Each track is the same 100 contacts. Solid is right, hatched came back but was not verified accurate (different title, stale employer or unverifiable), empty never came back at all. The two serifs on the solid part are the confidence interval.n = 3,445 · September 17, 2026 · test 2026-09-17_combined

The exact numbers

Coverage, accuracy and accurate records per 100
ProviderRankCoverage% of queriedAccuracy% of answeredAccurate per 100Interval
BlitzAPI190.4%91.2%82.481.1 to 83.7
Apollo296.8%72.3%70.068.4 to 71.5
Prospeo394.9%55.5%52.751.0 to 54.4
QuickEnrich468.8%63.7%43.842.2 to 45.5
MoltSets584.6%51.7%43.742.1 to 45.4
GetLeads.io681.1%39.3%31.930.3 to 33.4

Coverage

69% to 97%

How often a provider answers at all. A stale answer still counts here.

Accuracy

39% to 91%

How often that answer carries the right current employer and a matching title.

Accurate records per 100

31.9 to 82.4

The only measure nobody can improve by answering less often, or more loosely.

Apollo answers most often, on 96.8% of the list, and 72.3% of those answers are right. Which side matters depends on whether an unusable row costs you more than a missing one.

02

Reach against accuracy

A record is lost in one of two places: the provider never returns it, or the answer it returns is not verified accurate, because the title differs, the employer is one the person has left, or the answer cannot be verified.

Answering more often and answering correctly pull against each other: the provider with the widest reach is not the one that is right most often.

every provider on one frame: how often it answers, and how often that answer is right

BlitzAPI82.4 per 100Apollo70.0 per 100Prospeo52.7 per 100QuickEnrich43.8 per 100MoltSets43.7 per 100GetLeads.io31.9 per 100

How often it answers at allVertical axis: how often that answer is right

Bands, accurate records per 100under 2020 to 4040 to 6060 to 80over 80

  • BlitzAPIanswers on 90%, right on 91% of those, 82.4 accurate per 100
  • Apolloanswers on 97%, right on 72% of those, 70.0 accurate per 100
  • Prospeoanswers on 95%, right on 56% of those, 52.7 accurate per 100
  • QuickEnrichanswers on 69%, right on 64% of those, 43.8 accurate per 100
  • MoltSetsanswers on 85%, right on 52% of those, 43.7 accurate per 100
  • GetLeads.ioanswers on 81%, right on 39% of those, 31.9 accurate per 100
Further right means it answers more often. Higher means the answer is right more often. The dashed curves join the pairs that produce the same number of accurate records, so two providers on one curve are worth the same.n = 3,445 · September 17, 2026 · test 2026-09-17_combined
Show the exact numbers
Funnel, from the list queried to the records that survive
ProviderQueriedReturnedRight employerRight employer and title
BlitzAPI3,445100.0%3,11590.4%2,91984.7%2,84082.4%
Apollo3,445100.0%3,33496.8%2,91984.7%2,41170.0%
Prospeo3,445100.0%3,27194.9%2,18863.5%1,81652.7%
QuickEnrich3,445100.0%2,37068.8%1,78651.8%1,51043.8%
MoltSets3,445100.0%2,91584.6%1,91755.6%1,50743.7%
GetLeads.io3,445100.0%2,79381.1%1,89254.9%1,09831.9%

The top right corner is the only place worth being: answering often, and answering correctly. Nobody is there. The distance from it is what a provider costs you, in rows you never get plus rows you cannot use.

03

What a wrong answer is made of

Each provider's answers are broken down into four verdicts: right employer and title, right employer with a different title, stale employer, and unverifiable.

Accuracy is the green band on the left, from 39% to 91%. For 5 of the 6, the widest band after it is an employer the person has left; only Apollo loses more to titles that do not match.

Right employer and titleRight employer, different titleStale employerUnverifiable

BlitzAPI3,115 returned

91.2%

Apollo3,334 returned

72.3%15%11%

Prospeo3,271 returned

55.5%11%18%16%

QuickEnrich2,370 returned

63.7%12%24.6%

MoltSets2,915 returned

51.7%14%33.2%

GetLeads.io2,793 returned

39.3%28.4%32.2%
Shares of what each provider returned, so the four verdicts always add up to 100%. Rows keep the order of section 01, which ranks on accurate records per 100: a provider can be right more often than the row above it and still sit lower, because it answers less often.n = 17,798 · September 17, 2026 · test 2026-09-17_combined
Show the exact numbers
Verdict composition
ProviderReturnedRight employer and title% of returnedRight employer, different title% of returnedStale employer% of returnedUnverifiable% of returned
BlitzAPI3,11591.2%2,8402.5%794.4%1381.9%58
Apollo3,33472.3%2,41115.2%50810.8%3601.6%55
Prospeo3,27155.5%1,81611.4%37217.5%57415.6%509
QuickEnrich2,37063.7%1,51011.6%27624.6%5820.1%2
MoltSets2,91551.7%1,50714.1%41033.2%9691.0%29
GetLeads.io2,79339.3%1,09828.4%79432.2%9000.0%1

A stale employer is usually lag, not invention: 84% to 94% of stale answers name a company the person really worked for, just not now.

04

Data quality after a job change

On accurate records per 100, every provider is at its best on jobs older than a year, and all of them bottom out no later than 4 to 6 months after the move. The drop runs from 23 points for the steadiest to 56 for the most brittle.

Measure

accurate records per 100 contacts queried, by months since the current job started

accuracy, the share of returned contacts carrying the right employer and title, by months since the current job started

coverage, the share of contacts the provider returned at all, by months since the current job started

stale rate, the share of returned contacts naming an employer the person has left. Lower is better

Every provider by months since the current job started
Provider0 to 1n = 7852 to 3n = 9164 to 6n = 2557 to 12n = 44812+n = 1,041Shape
BlitzAPI657983117386868939895095989719699970
Apollo5660941867709510747499107878991179801006
Prospeo35389232313395345659951374769737579961
QuickEnrich22395746193260493143733368887767592813
MoltSets293681502733824921248752485686287383887
GetLeads.io222974511722765320238445384487245361875

Reading aidweakest to strongest within a column. Bold marks the strongest value of the column.

Color is a reading aid only, computed inside each column, and every value is written in the cell. The four measures cover the same people, so a row can be compared from one measure to the next.Color is a reading aid only, computed inside each column, and every value is written in the cell. This printed copy shows accurate records per 100; accuracy, coverage and the stale rate cover the same people and are on the web page, behind the measure selector.n = 3,445 · September 17, 2026 · test 2026-09-17_combined

05

Results by segment: country, seniority, company size

Six dimensions, and for each one the count of segments a provider leads before any single row is read. A row is ranked only above 100 contacts, and a row whose top two values sit inside each other's confidence interval is marked as too close to separate.

Measure

accurate records per 100 contacts queried

coverage, the share of the segment the provider answered at all

accuracy, the share of answered contacts carrying the right employer and title

Cohort6 of 6 segments, 1 too close to separate

Segments led, of 6 ranked

1 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI5
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by cohort, every provider
CohortContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
New jobs · Americastop two too close to separate83571.685.384.067.396.969.539.692.642.823.262.037.531.590.135.020.879.426.2
New jobs · Europe72362.479.778.352.489.558.622.893.824.316.249.932.423.969.234.616.666.824.8
Large cos · US/UK49493.196.097.077.399.278.067.896.670.262.679.878.457.789.164.841.789.346.7
SMB industry · US49096.197.198.980.499.680.770.296.372.964.379.680.863.994.567.645.186.752.0
SMB · EMEA45494.996.798.278.999.679.271.696.074.568.380.285.251.378.265.646.385.754.0
Micro cos · US44995.597.697.974.899.875.070.496.972.659.076.477.353.590.259.337.287.342.6

Country5 of 5 segments, 2 too close to separate

Segments led, of 5 ranked

2 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI3
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by country, every provider
CountryContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
US1,64985.792.392.973.398.474.556.794.460.146.673.063.947.292.551.029.883.935.6
GB49383.290.791.768.895.172.352.194.755.046.974.063.343.484.851.233.982.441.1
DEtop two too close to separate20470.682.485.762.395.165.542.693.645.538.766.258.512.726.548.126.573.036.2
FR15265.884.278.150.093.453.528.399.328.527.040.167.234.278.343.717.870.425.2
CAtop two too close to separate11883.994.189.279.7100.079.758.598.359.550.071.270.255.191.560.239.889.044.8

Seniority5 of 5 segments, 1 too close to separate

Segments led, of 5 ranked

1 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI4
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by seniority, every provider
SeniorityContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
Director1,12189.393.595.574.098.874.962.096.364.455.077.471.149.186.456.838.785.545.3
C-level/Founder86084.790.893.275.597.177.760.693.664.742.862.368.749.787.356.933.781.041.6
VP64294.998.396.576.599.876.667.197.069.263.682.477.155.991.061.541.388.946.4
IC/Staff54260.578.876.848.989.754.520.393.021.813.147.627.520.872.328.813.365.920.2
Managertop two too close to separate28062.181.476.362.994.366.721.192.922.716.463.925.720.778.226.513.275.017.6

Company size9 of 9 segments, 3 too close to separate

Segments led, of 8 ranked

3 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI5
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by company size, every provider
Company sizeContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
(unknown)93978.086.590.167.394.970.950.194.053.235.961.358.539.483.747.129.477.737.8
51-20059987.391.795.373.197.874.757.495.360.253.173.572.349.284.658.235.282.142.9
1001-500049984.892.691.672.197.673.954.396.656.248.972.967.047.185.055.435.983.443.0
11-5042990.794.695.873.498.874.359.494.662.850.873.769.050.888.657.432.983.739.3
201-50037488.093.993.774.197.176.360.494.963.752.175.968.748.785.357.138.284.045.5
10001+top two too close to separate20265.384.777.255.094.158.424.894.626.219.357.433.625.276.732.915.873.321.6
1-10top two too close to separate18380.391.388.073.298.474.453.695.156.342.166.163.642.189.147.231.185.836.3
501-1000top two too close to separate14875.791.982.466.996.669.247.396.649.040.570.957.139.283.846.826.481.832.2
5001-10000small sample, not ranked7273.684.786.962.597.264.344.491.748.530.666.745.829.279.236.827.877.835.7

LinkedIn followers5 of 5 segments, 2 too close to separate

Segments led, of 5 ranked

2 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI3
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by linkedin followers, every provider
LinkedIn followersContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
1k-4.9k2,00286.793.393.073.598.674.655.895.558.548.973.266.847.286.754.535.083.841.7
5k-9.9k50390.796.494.072.699.073.361.495.064.650.976.566.548.189.953.535.487.140.6
500-999top two too close to separate38465.982.679.864.194.368.036.794.339.029.958.351.335.477.945.525.875.034.4
10k+31291.795.895.773.499.473.966.798.168.043.968.963.745.290.150.229.886.534.4
< 500top two too close to separate24444.760.274.141.078.352.416.487.718.79.833.229.617.660.729.111.548.823.5

Industry8 of 22 segments, 3 too close to separate

Segments led, of 7 ranked

3 too close to call

No leader is declared on this measure

Ties are only tested on accurate records per 100

No leader is declared on this measure

Ties are only tested on accurate records per 100

  • BlitzAPI4
  • Apollo0
  • Prospeo0
  • QuickEnrich0
  • MoltSets0
  • GetLeads.io0
Results by industry, every provider
IndustryContactsBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
Software Development67688.893.594.975.099.175.759.595.162.555.075.772.745.781.855.935.584.841.9
Technology, Information and Internet24588.294.393.572.797.174.859.295.961.748.672.267.246.188.252.333.184.938.9
IT Services and IT Consultingtop two too close to separate17979.989.489.472.697.274.754.295.057.143.067.064.243.685.551.033.583.240.3
Higher Education15978.091.285.562.395.665.144.795.047.031.464.249.040.379.950.430.875.540.8
Financial Services13281.891.789.366.797.768.253.095.555.641.771.258.546.286.453.522.784.127.0
Business Consulting and Servicestop two too close to separate11978.291.685.366.498.367.545.495.047.833.663.053.336.189.140.626.179.033.0
(unknown)top two too close to separate10269.682.484.565.788.274.439.290.243.527.547.158.337.388.242.224.561.839.7
Hospitals and Health Caresmall sample, not ranked6374.685.787.066.795.270.046.092.150.033.361.953.834.988.939.333.377.842.9

Where each provider falls behind, including us. On accurate records per 100, BlitzAPI is behind on 1 of the 36 ranked segments, Apollo on 35, Prospeo, QuickEnrich, MoltSets and GetLeads.io on all 36. The table above carries every value behind that sentence; switch the measure to read the same rows on coverage or on accuracy, where the order is not the same.

Where each provider falls behind, including us. On accurate records per 100, BlitzAPI is behind on 1 of the 36 ranked segments, Apollo on 35, Prospeo, QuickEnrich, MoltSets and GetLeads.io on all 36. The tables above carry every value behind that sentence. This printed copy shows accurate records per 100 only; the same rows on coverage and on accuracy, where the order is not the same, are on the web page.

The counts above are on accurate records per 100. Switch the measure to coverage and the picture moves: Apollo returns more contacts than every other provider on 5 of the 6 cohorts, and Prospeo returns more on New jobs · Europe (93.8%).

The counts above are on accurate records per 100. On coverage, which this printed copy leaves out, the picture moves: Apollo returns more contacts than every other provider on 5 of the 6 cohorts, and Prospeo returns more on New jobs · Europe (93.8%).

06

Head to head: every provider pair

If you are replacing one provider with another, this is the comparison that matters. Each pair is scored only on the contacts they both returned, so a provider cannot look good here by staying silent.

On the contacts both providers returned, 1 of the 15 pairs is too close for this test to separate.

points of accuracy, row against column, on the contacts both returned

Accuracy gap on the contacts both providers returned
Row is ahead byBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.ioAhead on
BlitzAPI +18.13,081BlitzAPI is ahead of Apollo by 18.1 points on 3,081 shared contacts+33.43,033BlitzAPI is ahead of Prospeo by 33.4 points on 3,033 shared contacts+28.72,287BlitzAPI is ahead of QuickEnrich by 28.7 points on 2,287 shared contacts+39.82,730BlitzAPI is ahead of MoltSets by 39.8 points on 2,730 shared contacts+52.42,683BlitzAPI is ahead of GetLeads.io by 52.4 points on 2,683 shared contacts5 of 5
Apollo−18.13,081Apollo is behind BlitzAPI by 18.1 points on 3,081 shared contacts +16.73,213Apollo is ahead of Prospeo by 16.7 points on 3,213 shared contacts+14.32,366Apollo is ahead of QuickEnrich by 14.3 points on 2,366 shared contacts+24.32,889Apollo is ahead of MoltSets by 24.3 points on 2,889 shared contacts+37.12,784Apollo is ahead of GetLeads.io by 37.1 points on 2,784 shared contacts4 of 5
Prospeo−33.43,033Prospeo is behind BlitzAPI by 33.4 points on 3,033 shared contacts−16.73,213Prospeo is behind Apollo by 16.7 points on 3,213 shared contacts too close+7.92,895Prospeo is ahead of MoltSets by 7.9 points on 2,895 shared contacts+24.32,762Prospeo is ahead of GetLeads.io by 24.3 points on 2,762 shared contacts2 of 51 too close
QuickEnrich−28.72,287QuickEnrich is behind BlitzAPI by 28.7 points on 2,287 shared contacts−14.32,366QuickEnrich is behind Apollo by 14.3 points on 2,366 shared contactstoo close +10.12,176QuickEnrich is ahead of MoltSets by 10.1 points on 2,176 shared contacts+22.72,292QuickEnrich is ahead of GetLeads.io by 22.7 points on 2,292 shared contacts2 of 51 too close
MoltSets−39.82,730MoltSets is behind BlitzAPI by 39.8 points on 2,730 shared contacts−24.32,889MoltSets is behind Apollo by 24.3 points on 2,889 shared contacts−7.92,895MoltSets is behind Prospeo by 7.9 points on 2,895 shared contacts−10.12,176MoltSets is behind QuickEnrich by 10.1 points on 2,176 shared contacts +13.02,578MoltSets is ahead of GetLeads.io by 13.0 points on 2,578 shared contacts1 of 5
GetLeads.io−52.42,683GetLeads.io is behind BlitzAPI by 52.4 points on 2,683 shared contacts−37.12,784GetLeads.io is behind Apollo by 37.1 points on 2,784 shared contacts−24.32,762GetLeads.io is behind Prospeo by 24.3 points on 2,762 shared contacts−22.72,292GetLeads.io is behind QuickEnrich by 22.7 points on 2,292 shared contacts−13.02,578GetLeads.io is behind MoltSets by 13.0 points on 2,578 shared contacts 0 of 5

Row ahead, darker is a wider gapRow behindGap inside the combined intervalSmall figure in each cell: contacts both providers returned

Read a row against the columns. Coverage drops out: each pair is measured only on the people both providers answered for. A cell is tinted only when the row is ahead, so a full row is a provider ahead of everyone.n = 3,213 · September 17, 2026 · test 2026-09-17_combined
Show the exact numbers
Head to head
PairBoth returnedFirst is rightSecond is rightGap
BlitzAPI vs GetLeads.io2,68392.1%39.7%52.4 pts
BlitzAPI vs MoltSets2,73092.1%52.3%39.8 pts
Apollo vs GetLeads.io2,78476.4%39.3%37.1 pts
BlitzAPI vs Prospeo3,03391.3%57.9%33.4 pts
BlitzAPI vs QuickEnrich2,28793.4%64.7%28.7 pts
Apollo vs MoltSets2,88976.0%51.7%24.3 pts
Prospeo vs GetLeads.io2,76263.3%39.0%24.3 pts
QuickEnrich vs GetLeads.io2,29264.5%41.8%22.7 pts
BlitzAPI vs Apollo3,08191.4%73.3%18.1 pts
Apollo vs Prospeo3,21373.1%56.4%16.7 pts
Apollo vs QuickEnrich2,36678.0%63.7%14.3 pts
MoltSets vs GetLeads.io2,57852.3%39.3%13.0 pts
QuickEnrich vs MoltSets2,17664.2%54.1%10.1 pts
Prospeo vs MoltSets2,89559.5%51.6%7.9 pts
Prospeo vs QuickEnrich2,32766.8%63.8%too closewithin the interval

One page per pair against BlitzAPI. Each one sets out, in order, the main measures side by side (accurate records per 100, coverage, accuracy, the wrong title rate and the stale employer rate), each counted on the whole list or on what each provider returned, with the count of published segment comparisons the other provider leads or ties; each provider's answers split into four verdicts; the pair scored on the contacts both returned; and how both hold up after a job change: vs Apollo, vs Prospeo, vs QuickEnrich, vs MoltSets and vs GetLeads.io.

07

Waterfall: what a second provider adds

The usual answer to a coverage gap is to buy a second provider and fall back to it. Here is what that buys, measured rather than assumed: each step only queries the contacts no earlier step returned.

Querying a second provider on the misses lifts the run from 82.4 to 86.9 accurate records per 100. A third adds 0.3.

accurate records per 100 contacts, cumulative

StartBlitzAPI3,445 contacts queried

82.4start

ThenApollo330 misses queried, 253 returned

86.9+4.4

ThenMoltSets77 misses queried, 20 returned

87.1+0.3

ThenProspeo57 misses queried, 15 returned

87.2+0.0

ThenQuickEnrich42 misses queried, 1 returned

87.2+0.0

Best possible with all providers: 91.2. Reaching it would mean knowing which answers are wrong without a reference.

Each step queries only the contacts no earlier step returned, which is what a waterfall can really recover. The chain starts from the top of the ranking, a sorting rule rather than a recommendation.n = 3,445 · September 17, 2026 · test 2026-09-17_combined
Show the exact numbers
Cascade
ChainQueried at this stepCoverageAccurate per 100Records gained
BlitzAPI3,44590.4%82.4
BlitzAPI then Apollo33097.8%86.9+153
BlitzAPI then Apollo then MoltSets7798.3%87.1+9
BlitzAPI then Apollo then MoltSets then Prospeo5798.8%87.2+1
BlitzAPI then Apollo then MoltSets then Prospeo then QuickEnrich4298.8%87.2+1

The ceiling for any combination is 91.2 per 100, the share of contacts where at least one of the 6 providers is right. Reaching it would mean knowing which answers are wrong without a reference, which is the whole problem.

08

Every measure, side by side

The measures that rank, in one table, including the rows that flatter nobody. Colour is a reading aid only, computed inside each row: green towards the better end, amber towards the weaker one. Rows that describe a run rather than score it, such as the query date, carry no colour and no mark.

Full scorecard
MeasureBlitzAPIApolloProspeoQuickEnrichMoltSetsGetLeads.io
Accurate records per 100 queried82.470.052.743.843.731.9
Coverageanswered, % of queried90.4%96.8%94.9%68.8%84.6%81.1%
Accuracyright employer and title, % of answered91.2%72.3%55.5%63.7%51.7%39.3%
Right employer, any title% of answered93.7%87.6%66.9%75.4%65.8%67.7%
Stale rateemployer the person has left, % of answered4.4%10.8%17.5%24.6%33.2%32.2%
Work history recall% of reference roles96.1%86.1%69.6%17.3%16.5%17.1%
Queried on2026-09-172026-09-172026-09-172026-09-18 (+1d)2026-09-17 (+1d)2026-09-17 (+1d)
Separate recomputation9 of 9 pass9 of 9 pass8 of 9, 1 warning9 of 9 pass9 of 9 pass9 of 9 pass

No price appears anywhere on this page, and no billing unit either. None of the providers tested publishes a per-unit list price we could verify and date, and a cost comparison built on quoted rates would not be checkable. Ask each provider for the rate it quotes you, and apply it to the volume you actually send.

09

How we ran the test

The reference

On the day of the test, the public LinkedIn profile of every contact was read from a source that is none of the providers tested, one row per role held. 3,916 profiles came back, 100.0% of those requested, producing 39,836 experience rows.

It comes from none of the providers being tested. A benchmark scored against one vendor's database measures similarity to that vendor, not accuracy.

The rules

Every provider received the same list, one query per contact, through its own public API with the same input. Employers are matched by LinkedIn company id, then company URL slug, then normalized company name; then the title on the matched role, after normalization.

Before publication, a separate script recomputes every verdict and aggregate from the raw answers: 9 checks per provider. Prospeo passes with a warning, on a field its answers never fill. The reference itself passes 12 of 13 gates.

Who was left out

  • 435started the current job in the month of the test, so no index could reasonably have seen it yet
  • 20have no current role on their profile, so there is nothing to be right or wrong about
  • 15have fewer than 30 connections, our floor for an active account
  • 1have no start date on the current role, so job age cannot be computed

They were removed before any provider was queried, never after. Excluding contacts once the answers are in is the easiest way to shape a benchmark.

The population

Cohorts
CohortContactsWhat it isolates
New jobs · Americas835Recent job changes, Americas (Sales Navigator export, 17 Sept 2026)
New jobs · Europe723Recent job changes, Europe (Sales Navigator export, 17 Sept 2026)
Large cos · US/UK494Sample 1.1: marketing leaders, large companies (1,001-5,000+ employees), mostly US/UK
SMB industry · US490Sample 1.4: VP / Director marketing, 51-500 employees, US, manufacturing / medtech / pharma
SMB · EMEA454Sample 1.3: marketing leaders, 51-500 employees, EMEA (UK, DE, IL, FR, ES)
Micro cos · US449Sample 1.2: marketing / growth leaders, very small companies (1-50 employees), mostly US

The providers, and how each was queried

  • BlitzAPIPOST https://api.blitz-api.ai/v2/enrichment/personQueried 2026-09-17, same day as the reference API docs for BlitzAPI
  • ApolloPOST https://api.apollo.io/api/v1/people/matchQueried 2026-09-17, same day as the reference API docs for Apollo
  • ProspeoPOST https://api.prospeo.io/enrich-personQueried 2026-09-17, same day as the reference API docs for Prospeo
  • QuickEnrichGET https://app.quickenrich.io/api/employees/searchQueried 2026-09-18, one day after the reference API docs for QuickEnrich
  • MoltSetsPOST https://api.moltsets.com/api/v1/reverse_linkedin_lookupQueried 2026-09-17, one day after the reference API docs for MoltSets
  • GetLeads.ioDirect API route not published hereQueried 2026-09-17, one day after the reference API docs for GetLeads.io

Each provider keeps the same colour in every figure and in every edition, from the first report it appears in. Colours follow the order providers first appear, then the alphabet, never the ranking, and the provider publishing the test never takes the colour closest to its own brand.

Full protocol, definitions, uncertainty rules, limits and the corrections policy: the methodology page.

10

Download the report

The report as one printable document, with every figure and every number drawn from the same data as this page.

You are reading it. This document is the report page, printed: every figure and every number in it comes from the same data as the page. The live version is at blitz-api.ai/benchmarks/contact-data-september-2026.

Download the full reportPDF, 896 KB, no email, no form

Figures with a measure selector print on their default measure; the other measures stay on this page.

sha256 31f12affc4d9a9a2

No row in this report identifies a person. If you appear in a reference and want out of future runs, write to antoine@blitz-api.ai.

About this test. Designed, funded and run by BlitzAPI, a provider included in the comparison. Figures measured on 3,445 LinkedIn profiles queried on September 17 and 18, 2026 against a reference built on September 17, 2026 from each contact's public LinkedIn profile, read from a source that is none of the providers tested. Matching rules and definitions are on the methodology page, protocol v1. No price and no billing unit are published, because none of the providers tested publishes a per-unit rate we can verify and date. Providers update their data continuously, so these results describe September 17 and 18, 2026, nothing more. BlitzAPI, Apollo, Prospeo, QuickEnrich, MoltSets, GetLeads.io are trademarks of their respective owners, used here only to identify the services tested; no affiliation or endorsement is implied. Found something wrong? Write to antoine@blitz-api.ai with the test id 2026-09-17_combined and we will correct or retract within five business days. The figures on this page are published under CC BY 4.0: reuse them anywhere, with attribution.

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