Chinese name gender
Han characters and pinyin both work. Coverage is near total and accuracy is the lowest of any major market — that inversion is a property of how Chinese given names are formed, and this page says so rather than averaging it away.
100 free credits every day. No card.
Measured 28 August 2026 on a fixed, reproducible fixture. Not a population sample — a fixed input anyone can rerun against us or against a competitor.
Why accuracy is lower here than in Europe
European given names come from a finite inherited stock: a name carries its gender as membership in a list. Chinese given names are composed — a parent picks one or two characters for meaning, sound and balance — so the pool is effectively open and the signal lives in connotation rather than membership.
Many characters are genuinely used for both. 静 (jìng, "quiet") leans female, but only at 68. Reading the gender field without the probability turns a real 68 into a fake certainty.
Real output, country hint CN
These return total_names: 0 and confidence: unverified, because China publishes no name registration counts. That label is about auditability, not correctness — the measured accuracy above is what speaks to correctness.
| Input | Gender | Probability |
|---|---|---|
| 伟 (Wei) | male | 85 |
| 静 (Jing) | female | 68 |
| 芳 (Fang) | female | 85 |
| 强 (Qiang) | male | 87 |
| 李伟 (Li Wei) | male | 92 |
| 王芳 (Wang Fang) | female | 93 |
Send characters rather than pinyin
Romanisation loses information twice. Tone marks are dropped, so wei collapses 伟, 薇, 微, 威 and 唯 into one string. Character identity is dropped even at the same tone. Pinyin is a lossy hash of the thing you actually wanted to look up, and the answer you get back is the blend.
If your source system stores characters, send characters. If it stores pinyin, send pinyin and lower your threshold.
The surname-first trap
Chinese names are written surname first. Sent as characters, 张伟 is matched as a unit and behaves. Sent romanised, Zhang Wei hits a first-token-wins rule and classifies Zhang — a surname, which carries a weak population-level gender lean and therefore returns a plausible answer with no error at all.
Reversed, Wei Zhang classifies Wei and resolves on 136 observations at high confidence. Same person, two orderings, two entirely different evidential bases. Put the given name in the name field, or read the name field back to see which token won.
Single characters are whole names
A one-letter token in J. Smith is an initial and should be skipped; a single Han character is a complete given name. A Latin-centric normaliser drops exactly the names this market is made of, which is a bug you will not see unless your test fixtures are non-Latin.
Questions
Do you support traditional characters?
Both simplified and traditional inputs reach the lookup; the entry matched is reported in the name field.
Should I pass country=CN?
Pass it when you know it. It narrows the pool, though for China the answer will still carry confidence unverified because no registration counts are published.
Why is my Chinese answer unverified but correct?
Because confidence reports counted evidence, not correctness. Countries without published registration volumes always return unverified, whatever the answer.
What about Hong Kong, Taiwan and Singapore?
Names resolve the same way; pass the relevant country code when you have it, and expect romanisation conventions to differ from mainland pinyin.
Related pages
Predict gender from a Japanese name. Our measured accuracy here is the lowest of any market we publish, the sample is small, and this page shows both.
Predict gender from a Korean name in Hangul or romanised form. Measured coverage and accuracy, competing romanisation systems, and surname-first handling.
Every name lookup returns a probability, a sample size and a confidence tier. What each one measures, how the tiers are set, and where to put your threshold.
Coverage on Han names is 99.5% but accuracy is 86.9% — the inverse of the usual pattern. Why pinyin is weaker evidence, and the name-order trap.
A reproducible benchmark across 4,610 names, 25 countries and nine writing systems. Two scripts return almost nothing, and we publish those numbers too.
Check it against your own list
Every number on this page is reproducible with a free key. If your data breaks it, that is the more interesting result.