MOEVE - Question Answering: leaderboard
Metric: Mean overall question-answering score x 100 (0-100) over three German datasets (German-QuAD, KIKC QA, FAQ Law), combining embedding similarity with LLM-judged factual correctness, faithfulness and noise sensitivity; German prompts, default decoding, open-weight models on vLLM or Ollama at the quantization listed in the paper's model table, others by API; the paper prints only the top 10 of 39 evaluated models; higher is better. Source: arxiv.org. Saturation forecast: Around August 2027. 10 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Mistral Large 2 (Nov) Instruct (2411) | 70.4 |
| 2 | GPT-OSS-120B | 70.3 |
| 3 | GPT-4o | 69.2 |
| 4 | Llama 3.3 70B | 69.1 |
| 5 | DeepSeek R1 | 68.9 |
| 6 | Phi-4 | 68.9 |
Interactive version: theaggregate.ai/benchmark?slug=moeve-question-answering · How It Works · Data refreshed daily, snapshot 2026-09-29.