German Language LLM Index
a PeerBench project

German LLM Benchmark · Model Profile

MiMo V2.6 Pro

Xiaomi
1631

Elo

#16
preliminary

Benchmark breakdown

GermEval

85.1%

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

83.2%

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 ↗

MuSR

77.1%

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

63.4%

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

72.0%

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) ↗

Comparable models