GermEval
83.2% Native German named-entity recognition — identify persons, locations, organisations and misc entities in German text, emitted as JSON. Scored with seqeval micro-F1 excluding the noisy MISC class. Run reasoning-off.
Named-entity recognition native · Native German
via GermEval (via EuroEval) ↗
INCLUDE
71.9% Native German exam and licensing questions covering region-specific knowledge — history, law, civics and culture. Written by humans in German, not translated.
4-option multiple choice native · Native German
via CohereLabs/include-base-44 ↗
MMLU-Pro
82.2% Hard academic questions across 14 subjects — STEM, law, health, economics, philosophy and more. Professionally translated to German, with up to ten answer options per question.
10-option multiple choice translated · Professional translation
via li-lab/MMLU-ProX ↗
MMMLU
86.8% OpenAI's multilingual MMLU, German split — general knowledge spanning STEM, the humanities, social sciences and other domains. Professionally translated to German.
4-option multiple choice translated · Professional translation
via openai/MMMLU ↗
MuSR
84.4% Multi-step soft reasoning over long narrative contexts — murder mysteries, object placement and team allocation. Requires chaining clues across several paragraphs to reach the correct answer. Translated to German from the original English MuSR benchmark.
2–5 option multiple choice translated · Professional translation
via zayne-sprague/MuSR ↗
SB10K
60.4% Native German social-media sentiment classification — positive, neutral or negative. Human-annotated German text, not translated. Run reasoning-off; scored as exact-match accuracy on the predicted label.
3-class sentiment native · Native German
via SB10K (via EuroEval) ↗
ScaLA
74.2% Native German linguistic acceptability — does the sentence read as grammatical German (ja / nein)? Built from clean vs. minimally-corrupted German sentences. Run reasoning-off.
Binary acceptability native · Native German
via ScaLA-de (via EuroEval) ↗