Gender API comparison: Namsor, Genderize, Gender-API.com and NameGender measured

A gender API comparison is easy to make and easy to make useless. Collecting homepage claims into a table rewards incompatible definitions of accuracy, coverage and database size. A defensible comparison begins with one input fixture and one decision rule.

We operate NameGender, so read what follows as a vendor's comparison. To keep it checkable, every number below comes from a published measurement or from each vendor's own pricing page, with the date, and we say where another service is the better choice.

The short answer

• Highest measured accuracy, especially outside Latin script: Namsor. It answered 100% of the names in our test and was right on 96.1% of them.
• Western first names at small volume, with the fewest confident mistakes: Genderize. 91.1% end to end, and only 0.5% of its answers were confidently wrong.
• High monthly volume, spreadsheets, or an answer you can audit: NameGender. 1 million names a month cost €79.20, against €249 at Gender-API.com and $999 at Namsor; every answer carries the number of records behind it and the source it came from.
• Thai or Hebrew names: not NameGender. We scored 37.5% and 70.0% end to end on those scripts. Namsor scored 92.5% and 95.0%.
• Overall, NameGender and Genderize are level: 91.6% against 91.1% end to end, a gap well inside the margin of error of a 991-name sample. Namsor is clearly ahead of both.

Measured accuracy on the same names

We sent the same 991 names, with the same country hint, to three services and scored the answers against the known gender. The sample takes 20 names per country and gender from 25 countries. The fixture, method and per-country results are on the benchmark page.

Genderize and Namsor answers were collected on 18 September 2026. The NameGender column was re-measured on 7 October 2026, after Thai and Arabic given-name data was added to our engine. Gender-API.com is not in the table: we did not buy enough credits to run the full sample, and we would rather leave a column out than fill it with a guess. The script groups other than Latin have 40 to 77 names each, so treat a difference of a few points there as noise.

Two things the table shows that a single accuracy figure hides. Genderize answers fewer names than Namsor, but when it does answer it is rarely confidently wrong. And our weak spots are concentrated: overall we are level with Genderize (the 95% confidence intervals, 89.7%–93.2% for us and 89.2%–92.7% for Genderize, overlap almost entirely) and clearly behind Namsor. We are within two points of the others on Latin names, level with them on Arabic, and ahead of Genderize on Chinese and Korean; Thai and Hebrew are where we still lose. We now answer 40% of the Thai sample; answers drawn from Wikidata person names have no birth counts behind them, so they are capped at 95% probability and marked unverified.

Price at the same monthly volume

What each volume costs on the cheapest public plan that covers it. Prices are from each vendor's own pricing page on 14 October 2026, in the vendor's own currency; nothing is converted. Our column is the monthly subscription.

Namsor charges less for repeated names; the table assumes every name is different. Genderize's plans jump from 250,000 names ($60) to 2.5 million ($180), so a one-million workload pays for the larger plan; yearly billing lowers its prices further. The live version of this table, recalculated from the price lists, is on the pricing page.

Which one to choose

Choose Namsor when accuracy matters more than price, when your names span many writing systems, or when you need something we do not offer, such as origin or ethnicity estimates. It is the most accurate service we have measured, and the most expensive at volume.

Choose Genderize when your data is mostly Western first names, your volume is modest, and you want the lowest rate of confident mistakes. Its response is minimal: gender, probability and count.

Choose Gender-API.com if you want a long-established service with plans from 5,000 names. We have not measured its accuracy, so we cannot tell you how it compares.

Choose NameGender when you process large monthly volumes, when your input is full names, email addresses, usernames or spreadsheets rather than clean first names, or when you need to show why a record was classified the way it was: each answer returns sample_size, confidence and source, so a threshold can be set and audited. Do not choose us for Thai or Hebrew names until those numbers improve; we publish them on the benchmark page when they do.

Whichever you pick, test it on your own records first. The rest of this page is a worksheet for doing that.

Comparison worksheet

Fill this table with links to each vendor's current documentation and the date you checked it. Do not copy a value without its definition.

Build the fixture

Draw a sample from your actual records. Two hundred manually labelled rows is enough to reject a poor fit; a higher-stakes rollout deserves more. Keep the real proportions of countries and writing systems, including the segments you expect to be hard.

Add deliberate edge cases: a genuinely ambiguous name, spelling variants, accented and non-Latin input, empty values, a company name and nonsense. These rows measure whether the API can decline to answer, which is part of accuracy rather than a nuisance to remove.

Send the same first-name value and country hint to each provider. If a vendor accepts a full name or email directly, measure that as a second workflow. Otherwise you would be comparing one service's parser with another service's clean first-name endpoint.

Calculate three quality numbers

Suppose 1,000 rows produce 900 answers, of which 855 are correct:

• Coverage is 900 / 1,000 = 90%
• Accuracy when answered is 855 / 900 = 95%
• End-to-end accuracy is 855 / 1,000 = 85.5%

All three are necessary. Reporting only 95% hides the 100 unresolved rows. Reporting only 90% hides whether the answers were right. End-to-end makes the tradeoff visible but still needs the other two to explain why it moved.

Calculate the same measures per country and script. A large English segment can conceal a near-zero result on a smaller market in the global average.

Inspect the evidence model

Genderize documents probability and count. Gender-API.com documents probability and detailed result metadata, with fields varying by API version. NameGender returns probability, sample_size, confidence and source. Other services use their own vocabulary.

Map these into an internal response model without pretending they mean exactly the same thing. An observed ratio derived from counted registrations is different from a model confidence score. A count of supporting records is different from a vendor's total database size.

Test whether an unknown result is explicit. Your internal schema should allow null and preserve the raw provider status. Do not convert unknown to female, male or an empty string.

Test country handling

Use names whose distribution changes across countries and verify that the response changes for a reason you can inspect. Country should narrow the relevant evidence pool; it should not be a decorative echo of the request.

Record where the country comes from in your system. A declared market or address field may be appropriate. Request IP and interface locale often are not. A precise answer to the wrong population is still wrong.

Compare throughput with one real job

Run the same 10,000-row file or chunked API job through each candidate in a non-production environment. Measure elapsed time, retries, rate responses, unknown handling, output ordering and the effort required to resume after interruption.

Batch limits affect architecture. Genderize's official reference currently documents up to 100 names in one request; NameGender's current bulk limit is also exposed in its documentation and may be configured over time. Read limits at integration time rather than hard-coding an assumption from a comparison article.

For a spreadsheet task, include upload, column mapping and download in the measurement. The fastest endpoint can still create the slowest human workflow if every error requires manual reconstruction.

Calculate total price

Model at least three months: a normal month, a peak and an idle month. Record whether units expire or reset, whether unknown results and duplicate names consume units, and what happens after the limit.

Genderize's FAQ says usage is counted by name, including names inside a batch. NameGender sells purchased credits that remain until used and deduplicates repeated lookup keys in managed batch files.

Translate each model into the annual cost of your pattern. A monthly plan often fits continuous usage; a one-time balance often fits periodic cleanup. Neither is universally cheaper.

Review privacy before sending the fixture

Names can be personal data when linked to identifiable records. Send only the fields required for the lookup, use synthetic or appropriately authorised data during vendor evaluation, and read the current privacy notice and data-processing terms.

Ask about request logging, retention, deletion, subprocessors, training use, processing region and incident notification. For file workflows, ask how long source and result files remain available. Document the answers with dates because policies change independently of API code.

Finally, decide whether name-based inference is appropriate at all. It can support aggregate analysis or optional low-risk personalisation with a neutral fallback. It should not determine employment, medical, financial, insurance, legal or eligibility outcomes.

Make the decision reproducible

Keep the fixture, adapter code, raw responses, calculation script, pricing assumptions and decision threshold together. Record the test date and API versions. Repeat the run before a large renewal or after a material data-version change.

The result may not be one provider for every task. A spreadsheet team can choose a managed file workflow while a product team keeps a simpler endpoint. The goal is not one universal winner; it is an auditable reason for each production choice.

Per-vendor guides: Genderize alternatives, Gender-API.com alternatives and Namsor alternatives.