NameAPI is a web API
to handle people's names
in your software.

News

18.09.2026

Azerbaijani Names: Oglu, Qizi and a Century of Script Changes

One Azerbaijani name can tell several stories at once: family lineage, gender, linguistic heritage...


13.08.2026

Expanded Hungarian Coverage in the NameAPI Database

Few European naming traditions pack as much structure into a name as Hungarian. Besides using the...


22.06.2026

Software Version 11.4.0 released - NameAPI's New Business Detection Module

Telling a company from a person sounds trivial, until you have to do it reliably across millions of...


15.05.2026

Identifying Titles in Global Name Data

We have updated our NameAPI database with titles from more than 80 cultures. Our services identify...


20.04.2026

Database Update: Georgian Names Added

We are pleased to announce the latest expansion of our name database with a comprehensive...


   

Name Genderizer


       
Attempts to detect the person's gender based on the inputs, especially the person's name.
See also the Swagger specification.
                    
POST
       
application/json (you must set the content-type as http header)
       
We have integrated Swagger directly into our API.
Visit https://api.nameapi.org/rest/swagger-ui/.

   

Input

               
See Context.
   
{
  "context" : {
     "priority" : "REALTIME",
     "properties" : [ ]
   },
  "inputPerson" : {
    "type" : "NaturalInputPerson",
    "personName" : {
      "nameFields" : [ {
        "string" : "Andrea",
        "fieldType" : "GIVENNAME"
      }, {
        "string" : "Bocelli",
        "fieldType" : "SURNAME"
      } ]
    },
    "gender" : "UNKNOWN"
  }
}      
   

   

Output

       
Possible values:

The person is clearly 'male'.

The person is clearly 'female'.

Can be either male or female. See malePercent.

No gender could be computed, but better intelligence should be able to tell the gender. An example is a name input of which we have never heard before.

From the given data it is or seems impossible to tell the gender.
For example all terms are gender-inapplicable, or there are no names at all. Thus this differs from NEUTRAL where something is clearly known to be neutral.

There are conflicting genders in the given data.
Example: "Mr Daniela Miller" (salutation vs. given name).
The input data must be manually reviewed. It is impossible and useless to make a guess (garbage in would only cause garbage out).

       
If neutral (otherwise null) then this may be specified (but does not have to be), 0-1, the remaining % are for female.
       
0-1 where 1 is the best.
   
{
  "gender" : "MALE",
  "confidence" : 0.9111111111111111
}