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API provider comparison

GenderAPI.io vs Namsor

Compare GenderAPI.io V2 and Namsor for name-based gender inference: request shapes, country context, batch limits, confidence signals and migration decisions.

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Published by GenderAPI Reviewed

What changes between these APIs?

Namsor documents separate name and country-aware gender routes, including structured first/last names and full names. GenderAPI.io V2 uses type and value across name, email and username workflows. This page compares gender integration only, not Namsor’s broader name-analysis products.

What changes between these APIs?
Decision pointGenderAPI.io V2NamsorSources
Name inputsNames, email addresses and usernames use type and value on /api/v2/gender.firstName with optional lastName, or an unsplit name on full-name routes. firstName explicitly accepts nicknames.[1][10]
AuthenticationAuthorization: Bearer with a GenderAPI.io key. Query keys are available for simple GET examples; prefer headers in applications.X-API-KEY header with a Namsor credential.[2][11]
Country and batchUp to 50 keyed items or 10 IP-trial items. Each item can have its own type, country and options. Optional country on each item.genderBatch / genderFullBatch and Geo variants with countryIso2; up to 100 names.[5][10]
Confidence signalsdata.confidence is 0–1 or null. confidence_kind distinguishes stored-count frequency from an AI score; sample_count is null for AI.likelyGender, probabilityCalibrated (0–1), unnormalized score and genderScale (−1 male to +1 female).[3][10]
Unknown handlingdata.result_status: unknown; data.gender and data.confidence are JSON null. Inspect data.reason.The reviewed gender enum lists female and male. A general JSON-null unknown contract is not established by these references.[3][9]
AI routingExplicit off, fallback or always. Singles default to fallback; batches default to off. Optional forceToGenderize uses the dataset first, then nickname-aware AI.The reviewed gender request tables do not document an equivalent off/fallback/always switch.[4][10]
Usage accountingOrdinary successful lookups cost 1 credit, even unknowns and fallback AI. Always-AI costs 2. forceToGenderize costs 1 for a resolved dataset result or 2 when AI runs.Pricing lists 1 credit per standard gender operation; costs vary for other features and plan overages.[6][12]

Before you switch an integration

For a GenderAPI.io integration, change X-API-KEY to Authorization: Bearer and use a GenderAPI.io credential. Map the name intentionally into type: name and value; map countryIso2 to country. Replace personalNames with items and split batches to the V2 limit. Keep per-item IDs and handle item-level errors.

Do not transform Namsor score or genderScale into GenderAPI.io confidence. Even probabilityCalibrated and a 0–1 GenderAPI.io score should not share an acceptance threshold without evaluation. Record unknowns separately from an application decision to abstain on a low score.

Sources:[11][10][2][1][3][5]

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

Does Namsor support nicknames?

Yes. Its firstName documentation explicitly includes nicknames. That input support does not establish that its behavior is identical to GenderAPI.io forceToGenderize, which can infer an alias association without returning a real name.

Sources:[10][4]

Is a low score the same as a failed request?

No. Keep transport errors, an unknown API result and an application confidence threshold separate. Inspect each provider’s documented fields and evaluate your own abstention policy.

Sources:[3][10]

Sources and review dates

Use the linked documentation to confirm current terms and behavior before choosing a provider.

  1. GenderAPI.io V2 request parametershttps://www.genderapi.io/docs/v2/request-parametersReviewed
  2. GenderAPI.io V2 authenticationhttps://www.genderapi.io/docs/v2/authenticationReviewed
  3. GenderAPI.io V2 responseshttps://www.genderapi.io/docs/v2/responsesReviewed
  4. GenderAPI.io V2 AI optionshttps://www.genderapi.io/docs/v2/ai-optionsReviewed
  5. GenderAPI.io V2 batch guidehttps://www.genderapi.io/docs/v2/batchReviewed
  6. GenderAPI.io V2 credits and usagehttps://www.genderapi.io/docs/v2/credits-and-usageReviewed
  7. GenderAPI.io accuracy methodologyhttps://www.genderapi.io/accuracy-methodologyReviewed
  8. GenderAPI.io pricinghttps://www.genderapi.io/priceReviewed
  9. Namsor API enumeratorshttps://namsor.app/api-enumerators/Reviewed
  10. Namsor Gender API: Predict gender from first or full nameshttps://namsor.app/api-documentation/gender-api/Reviewed
  11. Namsor API documentation introductionhttps://namsor.app/api-documentation/introduction/Reviewed
  12. Namsor prices and feature credit costshttps://namsor.app/prices/Reviewed