GermEval
84.8%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 recognitionnative · Native Germanpreliminary
via GermEval (via EuroEval) ↗
INCLUDE
85.5%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 choicenative · Native Germanpreliminary
via CohereLabs/include-base-44 ↗
MMLU-ProX
85.5%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 choicetranslated · Professional translationpreliminary
via li-lab/MMLU-ProX ↗
MuSR
86.5%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. Public results use the corrected v1.1 evaluation with 553 effective test cases.
2–5 option multiple choicetranslated · German translationpreliminary
via TAUR-Lab/MuSR ↗
SB10K
65.2%Native German social-media sentiment classification — positive, neutral or negative. Human-annotated German text, not translated. Run reasoning-off; headline score is MCC on the predicted label.
3-class sentimentnative · Native Germanpreliminary
via SB10K (via EuroEval) ↗
ScaLA
77.8%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 acceptabilitynative · Native Germanpreliminary
via ScaLA-de (via EuroEval) ↗