Baby Name Generator
Draw baby-name ideas from real U.S. registration data — not an invented list. Filter the SSA's 2025 top 1,000 (or a decade snapshot back to 1955) by gender, starting letter, length, and popularity tier, then spin a cryptographically random sample. Every result shows its actual rank and how many babies received it.
Spin real names from the 2025 data
Results are drawn from real U.S. Social Security registration data — filtered sampling, not AI generation. Adjust any filter and the list respins.
What the generator actually does
There is no name-inventing algorithm here. The generator holds the SSA's 2025 top 1,000 names per gender plus 7 decade snapshots (1955–2015, top 200 per gender each), filters that pool by your choices, and then draws a uniform random sample with your browser's cryptographic random number generator — rejection-sampled, so every matching name is exactly equally likely. In 2025, that pool runs from Olivia (13,544 girls) and Liam (20,818 boys) at rank #1 down to names registered a few hundred times at rank #1,000 — including 146 girl names of four letters or fewer.
Filters that admit what the data can't answer
The popularity tiers are rank ranges in the selected era's own list: top 10, top 100, ranks 101–500, and ranks 501–1,000. The decade eras are single-year SSA snapshots covering the top 200 names, so the deeper tiers simply do not exist there — the generator tells you that instead of quietly substituting different names. A name missing from the results is not necessarily unused: the SSA excludes names given fewer than 5 times in a year, and spelling variants are ranked separately.
Frequently asked questions
Where do these names come from?
Every name is drawn from the U.S. Social Security Administration's national baby-names dataset — the latest year available is 2025, where the top girl name is Olivia (13,544 babies) and the top boy name is Liam (20,818). The decade eras use SSA snapshot years from 1955 onward. It is public-domain U.S. government data and we show its real ranks and counts.
Are these names AI-generated?
No. The generator performs filtered random sampling of real registration data: it narrows the SSA lists by your filters, then draws a uniform random sample using your browser’s cryptographic random number generator. Every name shown was registered for at least 5 U.S. babies in the displayed year — nothing is invented, remixed, or generated.
Why does "spin again" give different names each time?
Each spin draws a fresh uniform sample from the filtered pool using crypto.getRandomValues with rejection sampling, so every matching name is exactly equally likely on every spin — there is no weighting, ordering trick, or bias toward any name.
Why do I sometimes get fewer names than I asked for — or none?
Because the data is real, some filter combinations have small or empty pools (for example, uncommon-tier names in a decade era, where the SSA snapshot only covers the top 200). When that happens the generator says so and shows what actually exists rather than padding the list.
Where are the name meanings?
Deliberately absent. We have no provenance-clean source for meanings or etymologies, so we publish none rather than fabricate any. What we can state honestly — popularity ranks and birth counts from the SSA — is exactly what we show.
Source: every name, rank, and count on this page comes from the U.S. Social Security Administration's national baby-names dataset (1955–2025 data, from Social Security card applications). It is public-domain U.S. government data; names given fewer than 5 times in a year are excluded by the SSA, spelling variants are counted separately, and the data covers U.S. births only. Full caveats on the methodology page.