hsk30

Grade Chinese text against HSK — against either document that gets called "HSK 3.0", and it will tell you which one it used.

pip install hsk30

import hsk30

hsk30.grade("我每天早上七点起床，然后去公园跑步。").label
# '3'      ← graded against the 2026 examination syllabus, the default

hsk30.grade("...", standard="2021").label
# the GF0025-2021 national grading standard instead

"HSK 3.0" is two different documents, and the choice changes the answer. They disagree on 41.5% of shared vocabulary and 40.7% of shared characters. Regrading 102 authentic graded readers against one rather than the other changes the level of 48% of them, almost always upward. This library defaults to the examination syllabus in force since July 2026 and records profile.standard on every result. See Versions.

$ hsk30 "我每天早上七点起床，然后去公园跑步。" --curve --target 2
HSK 3  (16 characters, 0 ungraded)
  HSK 1      68.8%  ############################
  HSK 2      87.5%  ###################################
  HSK 3     100.0%  ########################################
  target HSK 2: misses the 95% bar (12.5% above target)
    步  HSK 3      6.2%  <- over budget on its own
    每  HSK 3      6.2%  <- over budget on its own

No dependencies. Python 3.9+.

Why this exists

Most Chinese-learning tools report an "HSK 3.0 level" without saying which document produced it. There are two, published four years apart:

Judged against the 2021 standard, HSK 2.0 looks almost entirely regraded. Judged against the examination syllabus, barely half moved. The syllabus is markedly more conservative, and it is the document learners are actually tested on.

Every figure in this README is produced by python3 scripts/reproduce.py.

What it does

Answers one question: what HSK level does a reader need to read this text? The answer is the level at which cumulative character coverage reaches 95% — the point at which a reader can follow a passage and infer the rest.

p = hsk30.grade("这项研究揭示了神经网络的内在缺陷。")
p.level        # 6
p.label        # '6'
p.chars        # 16
p.ungraded     # characters outside the 3,000
p.curve()      # cumulative coverage at every level

Three decisions that matter

Character-level, not word-level. Word-level grading is unusable on segmented Chinese. Real segmenters emit phrase tokens (我的, 七点, 蓝色) that are not entries in any graded word list, pushing "unknown" past 95% at every level and reporting ordinary beginner text as off-scale. HSK 3.0 grades 3,000 characters separately from its words precisely because the character inventory is what gates reading.

The official character list, not a derived one. Deriving character levels from the lowest-level word containing each character agrees with the 2021 official list on 2,962 of 2,969 characters — and gets the family terms wrong. 哥, 妈, 妹, 弟 are level-1 characters whose only listed words (哥哥, 妈妈) sit at level 4. Also wrong: 王, 第, 零. This package ships the official list.

Proper nouns are excluded when identifiable. A reader does not need the puppy's name in their vocabulary; it is glossed in place. Counting names as difficulty graded a story called "My Puppy Doudou" at HSK 4 on a beginner shelf, entirely on the strength of 豆豆. Detection needs pinyin, so it is available through grade_tokens:

hsk30.grade_tokens([
    {"hz": "我", "py": "wǒ"},
    {"hz": "李明。", "py": "Lǐ Míng"},   # excluded
]).chars   # 1

Levels 7, 8 and 9 are one band

Neither document splits them — in the 2021 standard they share a single 5,599-word list and 1,200 characters. This package carries the band as level 7 and renders it "7-9". A vendor advertising an "HSK 8 word list" invented the split.

Character budgets

Reaching a 95% bar means keeping the above-target share under 5%, so a single character over that budget blocks the target on its own:

share, offenders = hsk30.budget_violations(text, target=3)

This is how a short passage silently regresses when an otherwise harmless edit repeats one hard character a fourth time.

Grading collections

shelf = hsk30.profile_shelf([hsk30.grade(t) for t in texts])
shelf.label        # median text — not the pooled figure
shelf.span_label   # 'HSK 2-3', the interquartile range

Reports the median text. Pooling every character in a shelf lets a handful of hard texts speak for all of them: it reported "HSK 3" for a beginner shelf on which 16 of 22 texts individually read at HSK 1–2, describing nothing actually on the shelf.

What's in this repository

HSKBench

Generating text at a level turns out to be much harder than grading it. Human authors writing to an explicit target hit it 61.8% of the time, overshooting at the easy end and undershooting at the hard end. HSKBench scores that task objectively — the grader is the metric, the way a compiler is the metric for generated code. See benchmark/README.md.

Versions

Three documents are routinely conflated, including by commercial HSK sites. They are different, and it matters which one a tool grades against.

The 2021 document is a national language standard (语言文字规范) from the Ministry of Education and the State Language Commission. The 2025 document is the examination syllabus from the Center for Language Education and Cooperation (中外语言交流合作中心) and governs the test learners actually sit — which is why it is the default.

HSK 2.0 graded no characters separately, so characters("2.0") raises.

The 2025 lists are extracted from the official 406-page PDF by scripts/extract_syllabus_2025.py, which self-validates: parsed per-level entry counts reproduce the published cumulative totals (300 / 500 / 1,000 / 2,000 / 3,600 / 5,400 / 11,000) exactly. Two notes from doing it — the syllabus numbers 11,000 entries but only 10,896 distinct words (homographs like 所/所2 get their own rows), and it grades 3,088 recognition characters, not the 3,079 widely reported.

Data sources

Both are transcriptions of 《国际中文教育中文水平等级标准》. Regenerate the shipped tables with python3 scripts/gen_data.py (needs network).

Limitations

• Simplified characters only. Convert traditional text with OpenCC first.
• Coverage is not comprehension. 95% character coverage is a necessary condition for fluent reading, not a sufficient one; grammar, register and world knowledge are not modelled.
• Proper-noun detection needs pinyin. grade() on a bare string cannot identify names; pass them via exclude=, or use grade_tokens().
• CJK Extension A–F characters are treated as ungraded, which is correct under the standard but means literary text scores off-scale readily.
• Authored segmentation in the corpus groups some phrases a segmenter would split.

Development

git clone https://github.com/harukicoder/hsk30 && cd hsk30
pip install -e ".[dev]"
pytest                          # or: python3 tests/test_hsk30.py
python3 scripts/reproduce.py    # every figure in the paper

The library is a port of the implementation that runs pinyora.com. It reproduces that implementation's output on all 102 corpus texts exactly (test_python_reproduces_the_javascript_reference_exactly), with one deliberate fix: the original's ASCII-only ^[A-Z] proper-noun test missed names romanised with an accented capital — Ōuzhōu, Ōuyà, Ā Q Zhèngzhuàn — and since 欧 and 洲 are both HSK 7–9 characters, missing one place name moved a text two levels. The legacy behaviour remains available as is_proper_noun_ascii.

Citation

@misc{serrano2026hsk30,
  title  = {hsk30: Grading Chinese Text Against the HSK 3.0 Standard},
  author = {Serrano, Alvaro},
  year   = {2026},
  url    = {https://github.com/harukicoder/hsk30}
}

Licence

MIT for the code and the derived level tables; CC BY 4.0 for the corpus (see corpus/LICENSE).