Model
EuroLLM
Publisher
EuroLLM consortium (Unbabel, Instituto Superior Técnico / Instituto de Telecomunicações, University of Edinburgh, and partners) - EU-funded (EU)
Family
EuroLLM-9B / EuroLLM-1.7B
Openness
open_weights_recipe
Licence
Apache License 2.0
EuroLLM trades frontier scale for a genuinely European, Apache-2.0, all-EU-language model with the cleanest possible EU AI Act posture - an open-weight model designed in and for the EU, well under any systemic-risk threshold, whose natural home is multilingual EU-facing workloads rather than beating the largest labs on English reasoning.
Do you really own it?
Substantial
none·limited·partial·substantial·full
Analytical input: AOI C · 69.2/100
Floor-weighted, not averaged. The weakest factor caps the level, because ownership is a conjunction. No factor is weak and both use-and-modify and data-control are strong, so the level is substantial; a moderate elsewhere keeps it short of full.
You fully own the use of these Apache-2.0 weights - clean across 9B, 1.7B and 22B-Instruct-2512, with unconditional commercial use and no field-of-use limits - and, self-hosted and EU-domiciled, you keep your data entirely. It stays short of full because data openness is partial, not full: the SFT set (EuroBlocks-SFT-2512) is released but the ~4T-token pre-training corpus is only described, so you can inspect the recipe but not reproduce it from a released corpus as with a fully-open model; the 9B size, 4k context and uneven multilingual safety also cap reliability. Adopt it for multilingual EU-facing work that fits inside 4k, behind your own guardrails; look elsewhere for a long-context or frontier engine.
1
Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?
StrongOSI-approved Apache-2.0 across base and Instruct weights with unconditional commercial use and no field-of-use limits - run, fine-tune, redistribute and commercialise it freely.
How this scores (AOI sub-dimensions)
Openness4/5how much is released - weights, data, code, licence - and how freelyGenuinely open-weight-plus-recipe: Apache-2.0 base and Instruct weights, a detailed technical report (arXiv 2506.04079) documenting tokenizer, architecture, the data mixture and training procedure, plus the released EuroFilter classifier and the EuroBlocks-Synthetic post-training dataset.
Legal4/5how permissive and clean the licence is for real commercial useOSI-approved Apache-2.0 with unconditional commercial use and no field-of-use limits, across base and Instruct weights; EU-domiciled and well under the systemic-risk compute threshold, giving a clean Article 53 open-source-exemption posture.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
ModerateOpen-weights-plus-recipe, but only partial data openness: the technical report documents the tokenizer, architecture, data mixture and training procedure, and the released SFT dataset (EuroBlocks-SFT-2512) plus the EuroFilter artifact make the recipe inspectable - yet the ~4T-token pre-training corpus is described, not published as one dataset, so this is stronger than weights-only but weaker than a fully-open model (cf. OLMo) and not reproducible from a released corpus.
How this scores (AOI sub-dimensions)
Provenance3/5how well we can trace and verify what went into the modelDistributed from the verified utter-project org on Hugging Face in safetensors with per-file checksums (checklist ~5/8); the technical report makes the pipeline well-documented and the data sources are named.
Governance3/5how accountable and well-documented the publisher isAn EU-funded consortium with named academic and industrial partners, detailed technical reports and a clear release cadence (1.7B -> 9B -> 22B).
3
Doesn't fail youIs it reliable and good enough for the job?
ModerateBest-in-class multilingual for its size (on par with Gemma-2-9B, ahead on WMT24++ translation), but a 9B model with a short 4k context and light, uneven per-language safety coverage - a compact backbone, not a frontier or long-context engine.
How this scores (AOI sub-dimensions)
Performance3/5how capable it is relative to its classBest-in-class for its niche: independent and publisher benchmarks put EuroLLM-9B on par with Gemma-2-9B on multilingual EU-language tasks, ahead on WMT24++ translation, and matching Mistral-7B on English.
Operational4/5how practical it is to run, serve and maintain in productionStandard safetensors with a conventional architecture load on all mainstream stacks (vLLM, llama.cpp, Ollama, TGI, transformers); community GGUF / MLX / GPTQ quants and a hosted NVIDIA NIM endpoint exist.
Safety3/5whether misuse risks are evaluated and guardrails are providedThe Instruct variants are instruction-tuned on EuroBlocks and behave as ordinary safety-tuned assistants, but published safety/alignment coverage is light, there is no companion guard model, and multilingual coverage means safety behaviour varies by language and is not uniformly evaluated.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted and EU-domiciled, the Apache weights run on your own infrastructure and nothing leaves - your data and derived knowledge stay yours.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
How the AOI score is computed
The seven dimensions above, each scored 0 to 5, weighted and summed to the 0 to 100 headline. The score is the analytical input behind the ownership verdict, not the verdict itself.
DimensionScoreWeightPoints
Openness4/50.1814.4
Provenance3/50.169.6
Legal4/50.1612.8
Safety3/50.169.6
Performance3/50.148.4
Operational4/50.129.6
Governance3/50.084.8
HeadlineC · 69.2/100
Sources
Every rating traces to a primary document. Read means the text was verified; unverified means it is known to exist but has not yet been read.
DocumentWhat it grounds
Vendor announcementunverified2026-07-25
EuroLLM is presented as an open-source European multilingual LLM suite covering all 24 official EU languages plus additional strategic languages, developed by an EU-funded consortium.
Model cardread2026-07-25
EuroLLM-9B-Instruct model card, read verbatim: hosted on the verified utter-project org on Hugging Face in safetensors under Apache-2.0, trained on 4T tokens, instruction-tuned on EuroBlocks, a 4k context and a documented chat template (BOS id 1 / EOS id 4); noted as not preference-aligned (may hallucinate or produce harmful content); comparable to Gemma-2-9B on EU languages and Mistral-7B on English.
Model cardread2026-07-25
EuroLLM-1.7B-Instruct model card, read verbatim: 1.7B parameters, a 4,096-token context, coverage of 35 languages, Apache-2.0, and comparable to Gemma-7B on machine-translation benchmarks.
Technical_reportread2026-07-25
EuroLLM-9B: Technical Report (arXiv 2506.04079), read verbatim: EuroLLM is trained from scratch to cover all 24 official EU languages plus 11 more, and documents the tokenizer, architecture, ~4T-token data mixture, EuroFilter pipeline, EuroBlocks-Synthetic post-training set and Megatron-LM training; the report frames its "Open release" as the "Public availability of models, filters, and datasets."
Documentationread2026-07-25
EuroLLM-22B-Instruct-2512 model card, read verbatim: openness is PARTIAL, not fully-open - the SFT dataset EuroBlocks-SFT-2512 IS released (utter-project/EuroBlocks-SFT-2512), but the ~4T-token pretraining corpus (web, parallel, Wikipedia, Arxiv, books, math, code, Apollo) is only described, not published as one downloadable dataset.
Vendor announcementunverified2026-07-25
The EuroLLM team's release blog describes EuroLLM-9B, its EU consortium and EU funding (EuroHPC / Horizon Europe), multilingual coverage, Apache-2.0 licensing and the release of the models, filter and synthetic post-training data.
Licenceread2026-07-25
EuroLLM base and Instruct weights - 9B, 1.7B and 22B-Instruct-2512 - are distributed under OSI-approved Apache-2.0 with unconditional commercial use and no field-of-use limits (the licence is not a preference-alignment/safety grant, and the cards warn the models may hallucinate).
Third-party analysisunverified2026-07-25
The European Commission's Open Source Observatory profiles EuroLLM as EU-funded pioneering European open-source AI released under an open licence.
Third-party analysisunverified2026-07-25
An independent review of the EuroLLM-9B technical report reports it on par with Gemma-2-9B on multilingual benchmarks, ahead on WMT24++ translation, and matching Mistral-7B on English.
Third-party analysisunverified2026-07-25
Independent coverage ranks EuroLLM among the leading European-developed open models for multilingual EU-language performance.
Third-party analysisunverified2026-07-25
EuroLLM safetensors checkpoints load on mainstream serving stacks (vLLM, llama.cpp, Ollama, TGI, transformers), with community GGUF/MLX/GPTQ quants and a hosted NVIDIA NIM endpoint available.