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Evaluate an open-weight language model on the languages and tasks your users actually need—not on an English score or multilingual average alone. Compare candidates under the same reproducible test conditions, report results for each language, and include native-language quality and inference cost alongside benchmark scores.
Start with the languages and tasks you need
List the exact languages and varieties your product will serve, including German locale, register, and domain requirements. Then name the tasks: for example, question answering, summarization, extraction, translation, or instruction following. If users will ask questions in German about English documents, treat that as a separate cross-language task; a monolingual German score does not establish how well the model handles it.
Decide what counts as an acceptable answer for each task before comparing models. Use expert-written expected answers or explicit scoring rules, and reserve some representative examples for a final held-out comparison. Avoid tuning on public benchmark examples if you plan to report those same examples as test results.
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Choose complementary benchmarks
Use established datasets to provide comparable baseline evidence, then add tests that resemble your application. No single benchmark measures every capability or language variety.
#1 Best Overall
| Resource | What it helps measure | How to use it |
|---|---|---|
| EU MMLU | Knowledge across seven EU-relevant subject areas selected from the original MMLU’s 57 subjects. | The European Commission Directorate-General for Translation announced it on 22 July 2026. At that time it covered 16 EU official languages, including German, with more planned. The project says more than 1,000 questions were translated and revised with contributors from nearly 250 students at 21 universities. Check the current language list before choosing a test. |
| Belebele | Multilingual reading comprehension. | Its documentation specifies accuracy and directs users to evaluate on the test set, not use it for training or validation. It documents zero-shot and few-shot setups; keep instruction and example languages consistent between candidates or test them as separate conditions. |
| EuroEval | A framework for evaluating encoder, decoder, encoder-decoder, base, and instruction-tuned models across more than 30 European languages. | Check the current documentation for the precise tasks and model coverage before selecting a run. |
| EU20 translated benchmark suite | Translated versions of MMLU, HellaSwag, ARC, TruthfulQA, and GSM8K for 20 European languages. | The 2024 paper reports evaluating 40 models. Use translated tests for broad coverage, then investigate important results with human-reviewed and native-language examples. |
These resources are not substitutes for an application-specific set. Add realistic prompts, including difficult and ambiguous cases, and define how to score acceptable variations in generated answers.
Make each comparison reproducible
Run every candidate under the same conditions. A score without its setup can be misleading: benchmark results can shift with the prompt, number of examples, decoding, or scoring method. For each run, record:
- Exact model name and checkpoint or revision; quantization; and whether it is a base or instruction-tuned model.
- Inference software and version, hardware, context length, and system prompt.
- Prompt template, instruction language, number and source of demonstrations, and whether examples are translated.
- Decoding settings, stopping rules, and whether tools or retrieval are enabled.
- Dataset version and split, language code, metric, sample count, and scoring method.
- For generated answers, whether scoring uses exact match, a rubric, or human judgment, and how evaluators treat acceptable variants.
Belebele distinguishes zero-shot and few-shot evaluation and documents choices around instruction and example languages. Meta’s Llama 3.1 model card also reports results with named benchmarks, shot counts, and metrics. These details matter when interpreting or reproducing a score.
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Publish a language-by-task matrix with raw results for every language, not just an overall score. Add a macro average and a dispersion measure, such as standard deviation or the gap between the best and worst target language. Do not silently omit a difficult language or weight languages by dataset size or web-data availability unless that weighting represents your intended users.
The European Commission warns that a model can score well in English while underperforming in other languages. Fraunhofer’s discussion of Teuken comparisons across 21 translated European languages also notes language-level outliers. In the evaluation it describes, Maltese, Croatian, and Irish were omitted because of translation quality. That is a reminder to disclose coverage gaps and the reason for them, rather than presenting a multilingual average as universal evidence.
Check native-language and cultural quality
Include tasks written or reviewed by competent speakers of each target language. For German, check compound words, idioms, register, and domain terminology. Across European languages, examine local conventions and cultural references as well as factual accuracy.
Rank #3
The Commission’s EU MMLU announcement says an EU-ready benchmark should address EU values and cultural contexts, including idioms, humour, cultural references, date and number formats, tone, and politeness. It also recommends balanced inclusion of all 24 EU official languages. Its EU MMLU work used student translators and project managers to translate and revise more than 1,000 questions. These steps make translation quality part of test quality; a translated English question is not automatically equivalent to a locally authored one.
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Measure tokenization and runtime, not just answer quality
Run the same representative inputs through each candidate’s tokenizer. Record tokens per word or character for German and each other target language, then measure latency, memory use, throughput, and energy or cost if available. Compare quality at a common inference budget as well as at each model’s practical best settings; this shows both efficiency and achievable quality.
Tokenizer fertility—the number of tokens used to represent a word—affects compute. Fraunhofer’s Teuken project reports that German text tokenized with Teuken incurs 22% additional compute compared with its English counterpart using Llama 3. This is a project-specific comparison, not a general estimate for German models or deployments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use model cards to shortlist, not to declare a winner
Model cards can provide an initial view of benchmark coverage and reported configurations. Meta’s Llama 3.1 model card, accessed in 2026, reports German MMLU 5-shot macro accuracy of 60.59 for 8B Instruct, 79.27 for 70B Instruct, and 84.36 for 405B Instruct. These are vendor-reported figures from that card’s setup, not a cross-vendor ranking. Compare them with another publisher’s results only when datasets, prompts, and metrics align.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Fraunhofer says Teuken was trained multilingually across the 24 EU languages and describes comparisons against similarly sized models on selected translated benchmarks. Training-language coverage can help explain a candidate’s intended scope, but it does not replace tests on your tasks. Check each model’s current license and deployment constraints separately.
Choose with a language-aware comparison
For each shortlisted model, compare task quality, worst-language performance, benchmark provenance, token efficiency, runtime, and reproducibility. A high average may conceal a failure in German terminology or a required smaller language. Conversely, a small score difference may matter less than a substantial runtime or tokenization difference at your deployment’s volume.
Make the decision from the per-language results and representative failure analysis, with the protocol and coverage limits visible. The available evidence does not establish one best open-weight model for every European language, task, or deployment setting.
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