Gender name – Determine if a name is male or female
Namsor develops the world's leading AI for morphological name analysis, recognized by the international scientific community. Our revolutionary gender checker API leverages billions of analyzed names and cutting-edge AI algorithms to classify names as male or female with unmatched precision.
600+Research contributions
99.99%Names availability
13B billionsNames processed
Explore the advanced features of our gender name API
Identify the most likely gender name by analyzing a first name and (optionally) a surname or an unsplit full name. Specify a local context to improve accuracy. This helps capture cultural nuances.
When you use our gender finder API or interface, you receive several indicators. Here are the main ones:
Presumed Gender (male, female) This estimate shows if the name is usually male or female.
Calibrated probability (Between 50% and 100%) This percentage shows how reliable the estimate is. If the percentage is 91% the estimated gender is very reliable. If it is 50% then it has as much chance of being male as female (common for unisex names).
Script (Latin, Cyrillic, etc.) Shows the writing system used for the name. This helps identify its linguistic and cultural roots.
What is gender, and how to infer it from a name?
Gender refers to the social or cultural classification of an individual as male or female. This classification is based on their identity, roles and expressions. Most cultures use a binary system in which gender is divided into two categories: boys/men/males and girls/women/females.
By studying names, called onomastics, we can guess a person's gender. This is done through a morphological analysis of the name.
Our gender checker API looks at the likelihood of the name being masculine or feminine. It considers cultural, linguistic, and regional differences identified during the name analysis.
If the probability of the identified gender is between 50% and 60%, it means the name is not clearly linked to one gender. This is simply because the name is used by men and women in equal proportions.
How do we determine the gender of the name?
We develop specialized AI to estimate gender. It combines morphological name analysis with advanced NLP techniques. We design each step, from assembling data sets to continuous learning, to optimize identifying male and female names.
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Extensive data gathering and preparation
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Onomastic model training for gender name estimation
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Rigorous model evaluation and validation
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Continuous updates and cultural adaptation
Is Namsor the best tool to estimate gender?
Discover how Namsor's specialized onomastics outperforms LLMs, static databases, and other name analysis tools in accurately identifying whether a name is male or female.
Analyze names to determine gender using our API documentation, CSV/Excel tools, or developer resources. Choose the method that best fits your needs.
CSV and Excel Tool
Process small to medium-sized name lists without coding. Upload your document and select the gender analysis options you need. This tool provides quick results for batch processing.
For large-scale integration or dynamic applications, use our REST API to add gender analysis features to your systems. Complete documentation includes step-by-step guides and code examples in JavaScript, Python, Java, and Shell.
Access advanced gender analysis capabilities through our Software Development Kits (SDK) or Command Line Interface (CLI). Available for Java, Python, GoLang, and JavaScript to integrate seamlessly into your development workflow.
Analysis of first names, surnames and full names to determine information such as gender is used in a wide range of fields.
Marketing
The success of a marketing campaign depends on personalisation.
With Namsor, you can instantly segment your databases according to customer gender.
Research
How can gender-related patterns be measured reliably at scale?
Namsor enables researchers to analyze gender distributions across large-scale datasets
Population analysis
Biases can influence datasets, but they should not drive individual decisions.
Recruitment organizations use Namsor to analyze population-level applicant patterns for technical roles.
Historical analysis
A genealogist used Namsor to sort thousands of names by gender.
This sped up family tree building and revealed unknown historical trends.
Streamlined contact forms
Nobody likes filling in endless forms.
By automatically pre-filling the salutation based on the name, a hotel booking platform has reduced the abandonment rate of its forms by 7%.
Brands and influencers
Brands and creators analyze their audience's name origin and gender through Namsor.
They tweak marketing campaigns and collaboration strategies to connect with cultural details. This boosts engagement and loyalty.
Population flows analysis
Understanding Population flows composition is key to adapting public services and infrastructures.
By estimating gender, an international airport has been able to better calibrate its services to suit travellers.
Frequently asked questions about gender detection
What is the most accurate name gender detection API?
Namsor is the most accurate name-to-gender inference API, as confirmed by independent peer-reviewed studies.
Proven #1 in multicultural benchmarks
A peer-reviewed study published in Internal and Emergency Medicine (Springer) compared three leading gender detection APIs on 11,999 runners from seven major international marathons (New York, Berlin, Paris, Shanghai, Tokyo, Dubai, Abu Dhabi). Namsor achieved the lowest overall error rate at 4.8%, nearly half that of the next best tool at 8.0% (p < 0.001). Namsor also classified 100% of names with zero left unclassified, while competing tools left up to 7% of names without a result.
The only tool that doesn't break on uncommon names
Most gender detection APIs rely on lookup tables of known first names. They perform well on common names but fail on rare or unfamiliar ones. A peer-reviewed study (Journal of the Medical Library Association) demonstrated this clearly: when duplicate first names were removed from the dataset, leaving only unique names, one major competing tool's error rate jumped from 17.7% to 28.2%. Namsor's error only moved from 2.0% to 3.1%.
The reason: Namsor doesn't just look up names in a list. It uses morphological analysis to decode the internal structure of a name (roots, suffixes, phonetic patterns), which means it can accurately classify names it has never seen before, including newly invented names.
Accurate without geographic context, unlike alternatives
The same study showed that adding country information improved the accuracy of competing tools but had no statistically significant effect on Namsor (p = 0.41). Namsor's morphological models already capture the cultural and linguistic signals embedded in the name itself.
This matters in practice: in most real-world datasets (CRM databases, research datasets, historical records) the person's country is unknown. Competing tools, already less accurate than Namsor on benchmarks that include country data, become significantly less reliable without it. They require collecting additional personal data (country, nationality) to approach a level of accuracy that Namsor achieves from the name alone, adding both friction and privacy cost.
Trusted by the scientific publishing industry
Namsor is the reference gender detection solution used by the leading scientific publishers. Elsevier and Springer Nature rely on Namsor internally for their own bibliometric gender analyses, including author demographics, editorial board composition and reviewer diversity.
Namsor was selected by Elsevier and Science-Metrix to power the gender statistics in the European Commission's SheFigures reports, measuring women's contribution to scientific research across Europe (read the report).
Gender detection across 22 writing systems
Namsor classifies gender from names written in 22 alphabets and writing systems, including Latin, Cyrillic, Arabic, Devanagari, Han (Chinese), Hangul (Korean), Hiragana, Katakana, Hebrew, Thai, Bengali, Tamil, Telugu, Gujarati, Kannada, Malayalam, Georgian, Armenian, Ethiopic, Khmer, Myanmar and Greek. Most competing tools only support Latin script.
How does geographic context improve name gender prediction?
Namsor delivers high accuracy from a name alone. But for culturally ambiguous names, where the same name can be male in one country and female in another, providing the country of residence sharpens the prediction significantly.
Why some names are gender-ambiguous across cultures
The same name can carry opposite gender signals depending on the cultural context:
Andrea: male in Italy, usually female in English-speaking countries
Nikita: male in Russia and other Slavic contexts, often female in Western Europe and North America
Kim: traditionally male in Scandinavia, often female in English-speaking countries, but can be unisex
Dominique: unisex in French, mostly female in English
Noor: unisex, with gender usage varying by country and language
Without country information, Namsor returns the statistically most likely gender globally. With country information, Namsor applies the local cultural norm and resolves the ambiguity.
How to use it in the API
Namsor's gender feature offers two modes at the same cost (1 credit per name):
Standard mode (Genderize Name / Genderize Full Name): provide a name, get the globally most likely gender
Geo mode (Genderize Name Geo / Genderize Full Name Geo): provide a name plus a country code, get the locally calibrated gender
When to use which
Use Standard mode when country data is unavailable, incomplete or unreliable. Namsor's morphological analysis already captures most cultural signals from the name structure itself.
Use Geo mode when you have reliable country information and your dataset includes names from cultures with known gender ambiguities. This is particularly valuable for international CRMs, multilingual contact databases and cross-border compliance workflows.
No accuracy penalty without context
A peer-reviewed study on 11,999 names showed that adding country information had no statistically significant effect on Namsor's overall accuracy (p = 0.41). Geo mode doesn't fix a broken model. It resolves edge cases that the standard model already handles well in aggregate.
How does Namsor handle unisex or gender-neutral names?
Namsor returns a gender scale value for every name, not a binary male/female label. For unisex or gender-neutral names, this score reflects the degree of ambiguity, giving you the information to make your own decision.
How the gender scale works
Namsor returns a gender scale value from -1 (strongly male) to +1 (strongly female). A name like "James" might score -0.99 (very likely male). A name like "Maria" might score +0.98 (very likely female). A genuinely unisex name like "Dominique" will score close to 0 (genderScale -0.04), reflecting genuine statistical ambiguity.
Examples of unisex names
Dominique (French): almost perfectly neutral, genderScale -0.04
Morgan (English): traditionally male, now widely used for both genders, genderScale +0.12
Taylor (English): used for both genders in the US, genderScale +0.36
Yuki (Japanese): can be male or female depending on the kanji used, genderScale +0.49
How to use the score in practice
High confidence (score below -0.5 or above +0.5): the name is strongly gendered, safe to use for personalization (salutation, pronoun)
Low confidence (score between -0.5 and +0.5): the name is ambiguous. Use a gender-neutral salutation ("Dear Taylor Smith" instead of "Dear Mr/Mrs Smith"), or flag the record for manual review
Set your own threshold: depending on your tolerance for error, adjust the cutoff. A marketing campaign might accept a wider range than a compliance workflow
Geographic context can help, but not always
For some names, adding a country code shifts the probability. But for names that are genuinely unisex within a single culture (Taylor in the US, Dominique in French), geographic context won't resolve the ambiguity. The gender scale remains the best signal.
Why this matters
Treating gender as a probability rather than a binary label is both more accurate and more respectful. It avoids misgendering people with ambiguous names and lets you design workflows that handle uncertainty gracefully.
