Start with the data you actually have
Choose a provider around your input format, required evidence and operational constraints. A name-only lookup, email-name extraction and nickname inference are different tasks. A familiar confidence field or a large database does not establish accuracy on your records.
| Provider | Documented starting point | Detailed comparison | Sources |
|---|---|---|---|
| GenderAPI.io V2 | Unified name, email and username requests; explicit dataset/AI options. | Current V2 documentation | [1][4] |
| Genderize.io | Name or full-name GET lookups with optional country context. | Name-focused interface and migration checks | [11] |
| Gender-API.com | V2 name/full-name/email requests, including name extraction fields. | Separate service, contract and unknown billing | [10][9] |
| Namsor | Structured or full-name gender routes, with country-aware variants. | Name inputs, score meanings and batch mapping | [14] |
| NameGender.com | Separate name/email/username routes with optional AI fallback. | AI defaults, score scales and batch structure | [13] |
Use a checklist before comparing prices
These are selection questions, not universal promises about every provider. The linked comparisons identify which behavior is documented for each service. Review security, retention and contractual requirements directly with the provider when those affect your decision.
| Question | What to check | Sources |
|---|---|---|
| Can it process the input I have? | Test first names, full names, scripts, aliases and missing values separately. Country context must be known, not guessed from a person’s name. | [1] |
| What does an answer mean? | Separate an inferred gender association from identity. Inspect unknown behavior, evidence counts and score definitions. Do not carry a threshold between providers untested. | [3][7] |
| What happens in a batch? | Check item limits, input types, per-item country, preserved IDs, partial errors and the account quota. One HTTP request may consume many credits. | [5] |
| When is AI involved? | Choose an explicit policy; defaults and opt-in controls differ. Review what input reaches an inference service and the published processing terms. | [4] |
| What will the job cost? | Count ordinary, AI and unknown operations; include retries and overages. Compare the same workload and payment period, then consult current prices. | [6][8] |
Estimate workload cost before buying a plan
Illustrative GenderAPI.io V2 calculation: 1,000 successful ordinary lookups cost 1,000 credits, including unknowns and ordinary fallback AI. If all 1,000 use forceToGenderize and 200 require AI, the cost is 800 × 1 + 200 × 2 = 1,200 credits. These invented proportions are not measured match rates.
Apply each other provider’s own charging rules to the same workload; do not equate a name, HTTP request, answered result and AI operation. Retries can add usage. This review does not calculate a cheapest-provider ranking.
Sources:[6]
Evaluate the workflow you will deploy
Choose a representative sample you are permitted to process and keep the original inputs, preprocessing and country context fixed. Evaluate name, email and username workflows separately. Record provider, endpoint, date, configuration and whether AI was used.
Measure coverage (answered / eligible), accuracy among answered results (correct / answered), and correct results across all eligible inputs (correct / eligible). Report unknowns, errors, costs and latency separately. A threshold should be selected on validation data and then checked on a held-out sample.
GenderAPI.io does not publish an independent reference-labelled accuracy benchmark here. Database counts, contract tests and one provider's published study cannot establish which service will work best on your data. An inferred association does not establish a person's gender identity.
Sources:[7]
Frequently asked questions
Which gender API is the most accurate?
This documentation review cannot establish a winner. Compare providers on the same permitted, reference-labelled sample, report coverage and errors as well as accuracy, and keep country and AI settings explicit.
Sources:[7]
Can I compare all confidence scores directly?
No. Scores can differ in units, calibration and evidence. Some fields are categories, some are probabilities and some are unnormalized scores. A numeric conversion alone does not make them comparable.
Can I treat an inferred result as a person’s identity?
No. Name-related inference is an association. Keep unknown outcomes and let information supplied by the person take precedence.
Sources:[7]
Sources and review dates
Use the linked documentation to confirm current terms and behavior before choosing a provider.
- GenderAPI.io V2 request parametershttps://www.genderapi.io/docs/v2/request-parametersReviewed
- GenderAPI.io V2 authenticationhttps://www.genderapi.io/docs/v2/authenticationReviewed
- GenderAPI.io V2 responseshttps://www.genderapi.io/docs/v2/responsesReviewed
- GenderAPI.io V2 AI optionshttps://www.genderapi.io/docs/v2/ai-optionsReviewed
- GenderAPI.io V2 batch guidehttps://www.genderapi.io/docs/v2/batchReviewed
- GenderAPI.io V2 credits and usagehttps://www.genderapi.io/docs/v2/credits-and-usageReviewed
- GenderAPI.io accuracy methodologyhttps://www.genderapi.io/accuracy-methodologyReviewed
- GenderAPI.io pricinghttps://www.genderapi.io/priceReviewed
- Gender-API.com v2: Query by email addresshttps://gender-api.com/en/api-docs/v2/query-by-email-addressReviewed
- Gender-API.com v2: Query by full namehttps://gender-api.com/en/api-docs/v2/query-by-full-nameReviewed
- Gender API referencehttps://genderize.io/documentation/api/referenceReviewed
- NameGender API OpenAPI 3.1 contract, info.version 1.0.0https://namegender.com/openapi.jsonReviewed
- REST API Reference — NameGenderhttps://namegender.com/docsReviewed
- Namsor Gender API: Predict gender from first or full nameshttps://namsor.app/api-documentation/gender-api/Reviewed