Gender Prediction API Benchmark 2026
On a fixed fixture of 4,610 labelled names across 25 countries, NameGender answered 87.87% of rows, was correct on 96.03% of answered rows, and returned a correct answer for 84.38% of all rows. The country and script tables below are generated from the published report.
Measured August 28, 2026 · fixed fixture · fuzzy matching enabled
Overall result
- Labelled names
- 4,610
- Coverage
- 87.87%
- Accuracy when answered
- 96.03%
- End-to-end accuracy
- 84.38%
This is a NameGender product measurement, not a head-to-head vendor ranking. It measures the current lookup pipeline on one disclosed fixture. Use the same three measures on your own population before choosing a provider.
Results by country
The aggregate is not portable to every market. Israel and Thailand, for example, expose coverage gaps that the overall figure cannot show.
| Country | Names | Coverage | Answered accuracy | End-to-end |
|---|---|---|---|---|
| BR | 200 | 99.50% | 100.00% | 99.50% |
| CN | 174 | 100.00% | 89.66% | 89.66% |
| CZ | 160 | 100.00% | 99.38% | 99.38% |
| DE | 200 | 99.00% | 100.00% | 99.00% |
| DK | 200 | 99.50% | 98.99% | 98.50% |
| EG | 200 | 93.50% | 87.70% | 82.00% |
| ES | 194 | 100.00% | 98.97% | 98.97% |
| FI | 200 | 96.00% | 95.83% | 92.00% |
| FR | 189 | 100.00% | 98.94% | 98.94% |
| GB | 200 | 100.00% | 100.00% | 100.00% |
| GR | 200 | 59.50% | 96.64% | 57.50% |
| IL | 200 | 0.00% | 0.00% | 0.00% |
| IN | 200 | 97.50% | 96.92% | 94.50% |
| IT | 200 | 95.50% | 99.48% | 95.00% |
| JP | 51 | 98.04% | 78.00% | 76.47% |
| KR | 200 | 99.50% | 91.96% | 91.50% |
| NL | 200 | 100.00% | 97.00% | 97.00% |
| NO | 200 | 95.00% | 97.89% | 93.00% |
| PL | 197 | 99.49% | 100.00% | 99.49% |
| RU | 190 | 97.89% | 98.92% | 96.84% |
| SE | 200 | 100.00% | 99.50% | 99.50% |
| TH | 200 | 3.00% | 66.67% | 2.00% |
| TR | 61 | 98.36% | 93.33% | 91.80% |
| US | 200 | 99.50% | 92.96% | 92.50% |
| VN | 194 | 86.60% | 79.76% | 69.07% |
Results by writing system
| Script | Names | Coverage | Answered accuracy | End-to-end |
|---|---|---|---|---|
| arabic | 200 | 93.50% | 87.70% | 82.00% |
| cyrillic | 190 | 97.89% | 98.92% | 96.84% |
| greek | 200 | 59.50% | 96.64% | 57.50% |
| han | 222 | 99.55% | 86.88% | 86.49% |
| hangul | 200 | 99.50% | 91.96% | 91.50% |
| hebrew | 200 | 0.00% | 0.00% | 0.00% |
| kana | 3 | 100.00% | 100.00% | 100.00% |
| latin | 3,195 | 97.97% | 97.28% | 95.31% |
| thai | 200 | 3.00% | 66.67% | 2.00% |
How to interpret this report
Coverage is the percentage of all fixture rows that received a male or female answer. Accuracy when answered removes unknown rows from its denominator. End-to-end accuracy counts every unknown or wrong row as unsuccessful.
The fixture is a product regression set, not proof that the same percentages apply to your customers. Some country samples are smaller than others, and the three-name Kana row is far too small for a general claim. Read the sample size beside every percentage.
For sampling rules, holdout measurements and commands, read the benchmark methodology.