The words that pay off first

Lemmas placed by the expert-validated process (Synchak et al. 2025) against the project's per-level target. Complete through B1, preliminary above it.

A frequency rank is not a proficiency level

The validated A1 to A2 core (2,363 lemmas) versus a same-size core taken straight off this frequency list, by running-text coverage. The two are measured on different corpora, so read the spread as indicative: pedagogical selection deliberately trades raw coverage for teachability.

Why they diverge: the validated profile front-loads communicatively useful words that are not the most frequent (борщ borscht, зошит workbook) and pushes high-frequency abstract nouns (наука science, освіта education) up to B1 for conceptual load. A frequency list optimizes coverage; a CEFR level encodes teachability. That is why you cannot mechanically relabel one as the other, and why a real CEFR overlay here waits on open data above B1.

This is the headline of thread two: the one corpus-based, expert-validated CEFR profile for Ukrainian (Synchak et al. 2025, the PULS platform), joined onto this frequency list per lemma. A co-author granted permission to redistribute the per-lemma A1 to C1 levels; the join is POS-aware, so a homograph is dropped rather than mislabeled. Each lemma's level is its onset level, the earliest at which the profile introduces it.

It is a dated snapshot, not the final map. The profile is still being extended (the live list already carries more B2 and C1 lemmas than the published paper), so treat these levels as validated but provisional.

6,207 of the top 12,000 lemmas now carry a validated CEFR level, A1 to C1

The validated map above needs the authors' permission to redistribute. This overlay is the fully open alternative: it labels our lemmas from the learn-ukrainian open course (Krisztian Koos, CC-BY-SA), an expert selection based on Ukraine's 2024 State Standard, no permission needed. A lemma's level here is the first course lesson that introduces it, so treat it as a rough teaching-order signal, not a proficiency label.

Its role now is a cross-check, not a fallback. The validated overlay above measures the two against each other on the full overlap; this card keeps the open overlay's own calibration against the published gold points. Neither the open map alone nor the paper's frozen tables is the finished thing a learner deserves, and closing that gap is why this series exists.

2,326 of the top 12,000 lemmas carry an open CEFR label, A1 to B2

The field census below used to list the "1000 i 1 slovo" A1 lexical minimum (Kseniia Borodin and Oksana Turkevych, School of Ukrainian Language and Culture, Ukrainian Catholic University) as walled: licensed CC-BY-4.0 but published only as a prose PDF an extractor could not reach, so the one independent, empirically grounded A1 guideline for Ukrainian could not be joined and the overlay's rigor model called that missing consensus a seam. This page asked the authors for a machine-readable table, and co-author Kseniia Borodin sent the word list herself. This card is that list, joined onto the frequency list and republished as attributed open data under the guide's own license.

The join is honest about its grain. The authors' unit is the published entry (a single word, an aspect pair, a masculine and feminine pair, sometimes a whole phrase), the list carries no part of speech, and the join is by lemma string at the best rank, coarser than the POS-aware overlays above. Phrase entries are counted, never force-joined, and a beginner word the corpus does not rank stays honestly off-list.

838 of the 929 unique A1 entries join the top 12,000, reaching 833 distinct lemmas

Of the 768 joined lemmas the validated PULS map also covers, 75.4 percent are validated A1 and 96.5 percent sit at A1 or A2. Two independent efforts, a corpus-validated profile and an expert lexical minimum, converge on what beginner Ukrainian is. The handful above A2 is mostly the price of a coarser join: автомат the A1 ticket machine shares a spelling with the C1 rifle, and this list cannot tell them apart without a part of speech.

The A1 card above closed a seam. Its provenance note also recorded an open one, in plain words: the A2 volume of the same series existed, but no machine-readable A2 list had been shared. That volume has since been published open access under CC-BY-4.0 by Kseniia Borodin and Olesia Lazarenko at the Europa-Universitat Viadrina, so the seam closes by publication rather than by favor. This card is its 1,000-entry register, extracted from the publication's own PDF, joined onto the frequency list the same coarse way, and republished as attributed open data.

Two things become answerable that were not before. Because this is the same authors' next volume, the two lists together measure what one CEFR step actually costs, in frequency ranks rather than in word counts. And because the A2 book also prints its whole vocabulary regrouped under its own 51 topic headings, this is the first source on this page that can say WHICH semantic fields a news-heavy frequency list misses, not merely how many words it misses.

708 of the 1,000 published A2 entries join the top 12,000, reaching 707 distinct lemmas

Of the 656 joined lemmas the validated PULS map also covers, 70.3 percent sit at A1 or A2 and 29.7 percent sit above A2, nearly all at B1. That is a looser agreement than the A1 card found, and it should be: A2 is where two expert efforts start to disagree about the boundary, and the coarse lemma-string join adds noise of its own. The page reports the disagreement rather than tuning it away.

The card above says 229 word entries of the A2 minimum do not rank in this list's top 12,000, and reads that as the ceiling on learning by frequency. That reading rests on something untested. This list is built on UberText 2.0's COMBINED corpus, and the largest thing in that corpus by a wide margin is news. So an off-list word might be rare in Ukrainian, or it might be ordinary and simply absent from journalism, which does not often discuss forks, pumpkins or neckties. Those two readings give opposite advice to anyone building a word list, and nothing above can tell them apart.

So we built two frequency lists instead of one, from UberText's own fiction and news subcorpora: same collection, same cleaning, same lemmatizer, same sampling shape, both capped to the same token count. Genre is the only thing that differs. The ranks below are internally comparable and deliberately NOT comparable to the main list on this page, which a different lemmatizer produced.

News reaches 658 of the entries (70.3 percent). Fiction reaches 648 (69.2 percent). Almost the same amount, and not the same words: together they reach 744 (79.5 percent). A second genre of the same size buys 9.2 points that more of the first genre would not have bought. For a pedagogical target, corpus diversity is worth more than corpus size.

The exchange is close to symmetric, which is what makes it a real result rather than a bigger hammer: fiction reaches 30.9 percent of what news misses, and news reaches 33.3 percent of what fiction misses. Neither genre is better. They are looking at different parts of the language.

Read the two rows together and the mechanism is plain. Journalism supplies the institutional and the geographic; fiction supplies the domestic, the bodily and the sensory. Neither is a description of the language on its own.

Why this is so thin: there is still no open Ukrainian CEFRLex

Thread two now joins a validated profile (by the authors' permission), an author-shared A1 lexical minimum, and an open course overlay. That it took two direct asks to get here is the point: the open, per-lemma, CEFR-graded word list, a standard resource for learning a language, has been built for about a dozen languages and never published as open Ukrainian data. Here is the whole field.

Read this the way you read thread two's open CEFR overlay: it is an OPEN, joinable signal, but an unvalidated, prescriptive one. It is what a style checker would replace, an editorial opinion, not a settled linguistic verdict. Its core is derussification, but the tooling's scope is broader than strictly-Russian loans, and a few entries (поліцейський, безкоштовний) are live usage debates. Proper-noun and spelling fixes are dropped. Every suggested form is the tooling's own, kept verbatim.

The finding: the calque is often the common one

Of the 74 flagged forms whose native replacement is a single word, 48 (64.9%) have the flagged form at least as common in real Ukrainian text as its replacement: the idiomatic word is rarer, or not in the top 12,000 at all. That is the empirical face of older materials teaching the calque, and the corpus still carrying it.

One row per frequency band, lowest rank first. Each flagged form, the native alternative the tooling recommends, and where that alternative sits on the list. The full list is in the download.

11 of the flagged adjectives are the active-participle calque (діючий, існуючий): the -ючий forms Russian has and standard Ukrainian rewrites as a relative clause (що діє). The tooling lists that rewrite first.

The inflection tax, counted in forms

The 8,339 lemmas in the top 12,000 that inflect expand to 125,380 distinct wordforms, an average of 15 each. Thread one measured the inflection tax as slow reading coverage; this is the same tax counted in the surface forms you actually have to recognize. A noun runs about ten forms, a verb closer to thirty.

See a paradigm

A few high-frequency words, declined or conjugated, with IPA and open native audio. Pick a word; the forms are Wiktionary's own.

This is what turns the thread-one Anki seed from a bare headword list into a deck that teaches each word with its forms and its sound.

Part of a standing effort to close the digital-tooling gap for the Ukrainian language.