---
title: "Best Employee Headcount Data API 2026: Accuracy | Openbenchmarks"
description: "Best employee headcount data API: nine providers compared on 280 human-reviewed companies for headcount accuracy, coverage and price per record."
url: https://openbenchmarks.com/company-enrichment/best-employee-headcount-data-api
---

[](https://openbenchmarks.com/) 

[Read the benchmark methodology →](https://openbenchmarks.com/company-enrichment#methodology) 

[benchmarks](https://openbenchmarks.com/) / [company enrichment](https://openbenchmarks.com/company-enrichment) / employee headcount data api 

 company enrichment · employee count

# Best employee headcount data API 2026

Nine APIs—People Data Labs, Apollo, Parallel, Predict Leads, Explorium, CompanyEnrich, ZoomInfo, Exa, and Seltz—receive the same company domain and enrich it by API calls from their official docs.

**Explorium** has the highest measured employee-band correct-field yield: **88.9%**, with 249/280 eligible company headcounts correct. The providers with the most complete headcount data and the fewest incorrect returned values may differ.

Exact employee counts and ranges were normalized and evaluated with a dedicated numeric policy. The benchmark separates correct yield, coverage, and accuracy when returned rather than treating every populated estimate as reliable workforce data.

evaluated companies

280 

providers

11 

full cohort

282 

\[01\] measured headcount results

## Employee-count correctness, completeness, and writeback risk.

Correct-field yield is the primary ranking because it penalizes both missing and incorrect employee bands. Coverage measures whether an evaluable headcount was returned. Accuracy when returned measures how often a populated count or range passed the numeric policy.

benchmarks/firmographic/by-attribute/headcount live 

__Company enrichment providers ranked by correct employee headcount__
| Provider | Correct-field yield | Field coverage | Accuracy when returned |
| --- | --- | --- | --- |
| [Explorium](https://www.explorium.ai "Open Explorium in a new tab") | **88.9%** 249/280 correct | 95.0% 266/280 returned | 93.6% |
| [People Data Labs](https://www.peopledatalabs.com "Open People Data Labs in a new tab") | **86.4%** 242/280 correct | 98.2% 275/280 returned | 88.0% |
| [Parallel](https://parallel.ai "Open Parallel in a new tab") | **80.7%** 226/280 correct | 94.3% 264/280 returned | 85.6% |
| [PredictLeads](https://predictleads.com "Open PredictLeads in a new tab") | **78.2%** 219/280 correct | 91.1% 255/280 returned | 85.9% |
| [Apollo](https://www.apollo.io "Open Apollo in a new tab") | **78.2%** 219/280 correct | 95.4% 267/280 returned | 82.0% |
| [CompanyEnrich](https://companyenrich.com "Open CompanyEnrich in a new tab") | **54.6%** 153/280 correct | 97.9% 274/280 returned | 55.8% |
| [ZoomInfo](https://gtm.ai "Open ZoomInfo in a new tab") | **47.9%** 134/280 correct | 94.6% 265/280 returned | 50.6% |
| [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | **47.1%** 132/280 correct | 65.0% 182/280 returned | 72.5% |
| [Nimble](https://www.nimbleway.com "Open Nimble in a new tab") | **44.3%** 124/280 correct | 48.2% 135/280 returned | 91.8% |
| [Exa](https://exa.ai "Open Exa in a new tab") | **33.2%** 93/280 correct | 99.3% 278/280 returned | 33.5% |
| [Seltz](https://seltz.ai "Open Seltz in a new tab") | **7.9%** 22/280 correct | 30.4% 85/280 returned | 25.9% |

\[02\] all field results

## Compare headcount performance across every scored field.

This is the main benchmark's per-field table for HQ location, industry, employee band, founded year, and LinkedIn URL. Each value is correct-field yield against the eligible reviewed references for that attribute. Read the [main benchmark methodology](https://openbenchmarks.com/company-enrichment#methodology) for the frozen cohort and public evidence.

benchmarks/firmographic/by-attribute all fields 

__Correct values per attribute, as a share of the companies carrying a verified reference for that attribute__
| Provider | Official docs | HQ location | Industry | Employee band | Founded year | LinkedIn URL |
| --- | --- | --- | --- | --- | --- | --- |
| [ Apollo ](https://www.apollo.io) | [Apollo official docs ↗ ](https://docs.apollo.io/reference/organization-search) | 87.3% 240/275 | 95.4% 269/282 | 78.2% 219/280 | 91.8% 191/208 | 90.8% 256/282 |
| [ CompanyEnrich ](https://companyenrich.com) | [CompanyEnrich official docs ↗ ](https://docs.companyenrich.com/reference/get%5Fcompanies-enrich) | 57.8% 159/275 | 75.9% 214/282 | 54.6% 153/280 | 90.0% 189/210 | **91.8%** 259/282 |
| [ Exa ](https://exa.ai) | [Exa official docs ↗ ](https://docs.exa.ai/reference/search) | 82.9% 228/275 | **96.1%** 271/282 | 33.2% 93/280 | **97.6%** 203/208 | 5.3% 15/282 |
| [ Explorium ](https://www.explorium.ai) | [Explorium official docs ↗ ](https://explorium-api.readme.io/reference/match%5Fbusinesses) | 73.5% 202/275 | 84.8% 239/282 | **88.9%** 249/280 | 0.0% 0/208 | 86.5% 244/282 |
| [ Nimble lite ](https://www.nimbleway.com) | [Nimble lite official docs ↗ ](https://docs.nimbleway.com/api-reference/search/search) | 52.0% 143/275 | 70.9% 200/282 | 44.3% 124/280 | 36.7% 77/210 | 63.1% 178/282 |
| [ Nimble standard ](https://www.nimbleway.com) | [Nimble standard official docs ↗ ](https://docs.nimbleway.com/api-reference/search/search) | 68.0% 187/275 | 85.8% 242/282 | 47.1% 132/280 | 63.3% 133/210 | 82.3% 232/282 |
| [ Parallel ](https://parallel.ai) | [Parallel official docs ↗ ](https://docs.parallel.ai/api-reference/responses-api/create-response) | **90.9%** 250/275 | 92.9% 262/282 | 80.7% 226/280 | 76.4% 159/208 | 86.2% 243/282 |
| [ People Data Labs ](https://www.peopledatalabs.com) | [People Data Labs official docs ↗ ](https://docs.peopledatalabs.com/docs/reference-company-enrichment-api) | 90.2% 248/275 | 94.0% 265/282 | 86.4% 242/280 | 93.8% 195/208 | 84.0% 237/282 |
| [ Predict Leads ](https://predictleads.com) | [Predict Leads official docs ↗ ](https://docs.predictleads.com/guide/extended%5Fcompanies%5Fdataset) | 68.4% 188/275 | 88.3% 249/282 | 78.2% 219/280 | 89.9% 187/208 | 84.8% 239/282 |
| [ Seltz ](https://seltz.ai) | [Seltz official docs ↗ ](https://docs.seltz.ai/api-reference/answer) | 35.6% 98/275 | 37.6% 106/282 | 7.9% 22/280 | 34.8% 73/210 | 35.5% 100/282 |
| [ ZoomInfo ](https://gtm.ai) | [ZoomInfo official docs ↗ ](https://gtm.ai/docs/mcp/tools/enrich-companies) | 73.1% 201/275 | 74.5% 210/282 | 47.9% 134/280 | 61.4% 129/210 | 62.4% 176/282 |

\[03\] questions

### How much does employee headcount data cost per record, and which API is cheapest?

At each provider's public entry self-serve rate, Nimble lite was the cheapest for the full 282-company run at $0.31, about $0.00 per company record. Pricing is for the whole enrichment call, not this field alone; no provider prices employee headcount data separately. Cheapest is not best: read cost beside this page's accuracy table.

### What is the best API for employee headcount data?

Explorium has the highest measured end-to-end employee-band yield at 88.9% (249/280 eligible company headcounts correct). Coverage and accuracy when returned are reported separately because the best choice changes when missing data and wrong writebacks have different costs.

### How is employee headcount accuracy judged?

The benchmark compares normalized numeric ranges and optional exact counts. It accepts matching bands, equivalent 1–10 and 2–10 bands, an exact count inside the reviewed range, a reviewed exact count inside the provider range, exact counts within 5%, and finite ranges whose bounds differ by at most one. Broad overlap alone does not pass.

### How do I enrich a company list with employee headcount?

Send the same normalized company domain to each candidate provider, store both exact count and range when available, retain source date and entity scope, and evaluate missing and incorrect values separately. Decide first whether the workflow needs a segmentation band, a directional estimate, or payroll-grade current headcount.

open/benchmarks [company enrichment](https://openbenchmarks.com/company-enrichment) [methodology](https://openbenchmarks.com/company-enrichment#methodology) © 2026 Openbenchmarks