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Evaluate each candidate on the tasks it must perform, and report results separately for German and every other target language. An English score or multilingual average can conceal a weak language, while benchmark results alone do not establish that a model is ready for real users. A sound comparison combines complementary tests, a reproducible protocol, native-language review, and measurements of token and runtime costs.
Start with the languages and tasks you actually need
Write down the exact languages and varieties your product will serve, then define the work users will ask the model to do. Examples include question answering, summarization, information extraction, translation, and instruction following. For German, identify relevant register, domain terminology, and locale conventions rather than treating all German-language use as identical.
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Test cross-language workflows separately. A German question about an English document, for example, is not the same task as answering a German question from German source material. Do not assume a monolingual benchmark predicts performance on either workflow.
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Build a benchmark suite, not a single score
Use established tests to provide comparable reference points, then add an application-specific set. Public benchmarks differ in what they measure and how their examples were created, so a strong result on one is not a complete evaluation.
#1 Best Overall
EU MMLU for selected EU-relevant knowledge
The European Commission Directorate-General for Translation announced EU MMLU on 22 July 2026. At that time, it covered 16 EU official languages, including German, with more languages expected to follow. It focuses on seven subject areas selected for EU relevance from the source MMLU’s 57 subjects. The Commission says more than 1,000 questions were translated and revised with contributions from nearly 250 students at 21 universities. Check the Commission announcement for the language list and current availability.
Belebele for reading comprehension
Belebele is a multilingual reading-comprehension dataset with documented zero-shot and few-shot setups. Its documentation specifies accuracy and says to use the test set only, not for training or validation. Keep the instruction language and example language consistent across candidates, or compare those choices as distinct conditions.
EuroEval for broader European-language coverage
EuroEval says it supports encoder, decoder, and encoder-decoder models, including base and instruction-tuned models, across more than 30 European languages. Consult its live documentation to confirm the tasks and model coverage for the run you intend to make.
Translated suites as breadth, not proof of local quality
A 2024 Fraunhofer paper describes EU20 versions of MMLU, HellaSwag, ARC, TruthfulQA, and GSM8K for 20 European languages, and reports evaluating 40 models. These translated tests can extend coverage, but translation artifacts mean important findings should be checked against human-reviewed and native-language tasks. See the EU20 paper.
Add a held-out set based on your users’ work
Create realistic examples for the actual application, with expert-written expected answers or explicit scoring rules. Keep some examples held out for final comparison. If you tune a model using public benchmark examples, do not report performance on those same examples as an independent test result.
Make every comparison reproducible
Run candidates under the same conditions and record enough detail for someone else to reproduce the result. Differences in prompting, decoding, or scoring can make nominally similar benchmark numbers incomparable.
Rank #3
- Model: exact name and checkpoint or revision, quantization, and whether it is base or instruction-tuned.
- Runtime: inference software and version, hardware, context length, and system prompt.
- Prompting: prompt template, instruction language, number and source of demonstrations, and whether examples are translated.
- Generation: decoding settings, stopping rules, and whether tools or retrieval are allowed.
- Test and scoring: dataset version and split, language code, metric, sample count, and scoring method. For generated answers, state whether you use exact match, a rubric, or human judgment, and how acceptable answer variants are handled.
These details matter in practice: Belebele documents zero-shot and few-shot settings and choices of instruction and example languages, while Meta’s Llama 3.1 model card reports results with named benchmarks, shot counts, and metrics. Apply one declared protocol to every candidate, changing a condition only when you are deliberately testing it as a separate experiment.
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Present a language-by-task matrix with raw results for each target language. Add a macro average and a measure of spread, such as standard deviation or the gap between the best- and worst-performing target language. State which languages are missing and why; do not silently omit a difficult language.
Do not weight languages by dataset size or web-data availability unless that weighting reflects the intended user population. The Commission warns that English-built evaluation can miss underperformance in other languages and recommends balanced representation. Fraunhofer’s Teuken discussion also reports language-level outliers in comparisons across translated European languages. Averages are useful summaries, but they should not replace the underlying per-language results.
Check naturalness, culture, and local conventions
Include examples written or reviewed by competent speakers. Check more than grammatical correctness: idioms, compound words, register, domain terminology, humour, cultural references, date and number formats, tone, and politeness can all affect whether an answer works for its audience.
The Commission’s EU MMLU announcement argues that an EU-ready benchmark should address EU values and cultural contexts and recommends balanced inclusion of all 24 EU official languages. That recommendation is broader than EU MMLU’s announced coverage of 16 languages as of 22 July 2026. Treat translated English tests as one source of evidence, not a substitute for locally authored or carefully reviewed examples.
Measure tokenization and inference costs in each language
Run representative German and other-language inputs through each candidate’s actual tokenizer. Record tokens per word or character, then measure latency, memory use, throughput, and energy or cost where available. Compare quality at the same inference budget as well as at each model’s practical best settings; otherwise a quality difference may simply reflect a difference in compute spent.
Tokenizer fertility—the number of tokens used to represent a word—affects compute. Fraunhofer’s Teuken project reports that German text with its tokenizer incurs 22% additional compute compared with its English counterpart using Llama 3. This is a result for that project’s specific comparison, not a general estimate for German models. See the Fraunhofer Teuken project page.
Use model cards to shortlist, not to declare a winner
Model cards help identify a candidate’s claimed benchmark setup and can narrow a shortlist. Meta’s Llama 3.1 card, for example, reports German MMLU 5-shot macro accuracy of 60.59 for 8B Instruct, 79.27 for 70B Instruct, and 84.36 for 405B Instruct; it also includes Portuguese, Spanish, Italian, and French rows. These are vendor-reported figures under that card’s setup. Do not rank them directly against another publisher’s scores unless the dataset, prompts, and metrics align. The figures and configuration are in Meta’s Llama 3.1 model card.
Training composition and language focus provide useful context, but they do not replace task-level evaluation. Fraunhofer describes Teuken as trained multilingually across the 24 EU languages and reports comparisons against similarly sized models on selected translated benchmarks, alongside strengths and areas for further development. No single source establishes a best open-weight model for every European language, task, and deployment.
Make the decision against the deployment’s real constraints
Compare candidates across the same decision axes, with results broken out by language:
- Quality and robustness on representative tasks, including the weakest required language.
- Language coverage and benchmark provenance: human-authored or revised, translated, or application-specific.
- Token efficiency, latency, memory, throughput, and infrastructure cost.
- Checkpoint reproducibility and the exact evaluation protocol.
- Current license terms and deployment constraints, checked separately for each model.
A high overall average may be unacceptable if German terminology or a required smaller language fails. Conversely, a modest score difference may matter less than a large tokenization or runtime difference in a high-volume deployment. Set acceptance criteria before reviewing results, based on the risk and needs of the application; there is no universal score threshold established for production readiness.
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