German Artificial Analytics
a PeerBench project

German LLM Benchmark · Model Profile

GPT-5.5

OpenAI provider-internal run 2026-06-16
80 .2%

avg. German score

#3 of 26 models

+8.9pp above avg.

Benchmark breakdown

GermEval

85.9%

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

74.8%

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 ↗

MMMLU

92.1%

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

86.3%

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

62.5%

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

79.7%

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) ↗

Cost & speed

$6.427 per 1,000 questions