📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA language model, funded with €5.5 million, is now operational, outperforming many models in Portuguese benchmarks. However, key questions about its openness, native data, and goals remain unresolved, reflecting broader issues in European sovereign LLM development.
Portugal’s €5.5 million investment in the AI project AMÁLIA has resulted in a functioning European Portuguese language model, publicly accessible since October 2025, but fundamental questions about its openness, data, and objectives remain unresolved.
AMÁLIA is a consortium project involving approximately 60 researchers from Portugal’s leading research institutions, including NOVA, IST, and IT. The project was announced in December 2024, with the model’s base version completed by September 30, 2025, and publicly launched on October 1, 2025. It is accessible to 450,000 academic users via the FCT IAedu platform, with knowledge cut off at the end of 2023. The model is based on a continuation of the EuroLLM multilingual foundation, not trained from scratch, contrasting with Italy’s Minerva, which was trained from scratch on Italian and English data. The technical approach involves extended pre-training on 107 billion tokens, with a small proportion (around 5.8 billion tokens) from Portuguese web archive Arquivo.pt, representing roughly 5.5% of the training data. Despite outperforming previous open models and beating Qwen 3-8B on most Portuguese benchmarks, AMÁLIA still trails Qwen on ALBA, its primary benchmark, raising questions about its native-language performance. The final version is scheduled for release in June 2026, with ongoing assessments of its capabilities and limitations.
AMÁLIA
The three hard
questions.
Portugal spent €5.5M to build a European Portuguese LLM. The base version is operational, the benchmarks beat Qwen 3-8B on most pt-PT tasks. So why are the most important questions still unanswered?
Last month, Duarte O.Carmo published the sharpest public analysis of AMÁLIA — Portugal’s state-funded European Portuguese large language model. He prefaces his critique with the necessary diplomatic apparatus before doing what almost nobody else in the European-sovereign-LLM discourse has been willing to do publicly: asking hard questions about whether the work, as released, actually does what it set out to do. This piece is a structural extension of his analysis. The AMÁLIA case study exposes three hard questions every national LLM effort needs to answer publicly — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
Three questions every national LLM effort needs to answer publicly.
Duarte O.Carmo’s framing maps cleanly onto the structural argument. Each question lands specifically in AMÁLIA — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
The three questions form a structural feedback loop. Q3 (optimization target) determines Q2 (data volume needed) which conditions Q1 (openness sufficient for community contribution). The European sovereign-LLM movement collectively benefits from these questions becoming standard methodology disclosure, not exceptional critique.
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107 billion tokens. 5.8 billion clearly pt-PT.
The structurally tractable question with a structurally surprising answer. For a model whose entire stated purpose is European Portuguese prioritization, the native-language share of extended pre-training is 5.5%. The implications cascade into every other question.
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The Olmo standard. AMÁLIA’s current state.
Allen Institute for AI’s Olmo project defines what “fully open” operationally requires. Olmo doesn’t lead frontier benchmarks. That’s not the point. The point is to be the structural reference for openness. AMÁLIA’s “fully open source” claim should track to the operational standard.
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Four strategic positions. AMÁLIA between two and three.
Approximately €100M+ in publicly disclosed European sovereign-LLM funding across the major initiatives. The structural question every project faces: what is the actual competitive position you’re staking? Four options — none mutually exclusive — but each requiring different commitments.
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Three standards. For AMÁLIA and the movement.
The structural critique generalizes beyond AMÁLIA. Italy, France, Germany, Switzerland, the OpenEuroLLM consortium, and every subsequent national project benefit from public discourse holding national LLM efforts to operational standards on openness, data accounting, and strategic positioning.
The European sovereign-AI agenda is a serious strategic project that deserves serious public discourse. O.Carmo’s analysis is what serious public discourse looks like. Appropriately diplomatic. Structurally rigorous. Willing to ask the hard questions in public when the public investment justifies it. More of this is needed — across every European sovereign-LLM project, not just AMÁLIA.
Structural Challenges in European Sovereign LLMs
The development of AMÁLIA exemplifies broader issues facing European efforts to create sovereign language models. Despite significant investment, key questions about how open these models truly are, whether native-language data is sufficient, and what their primary objectives should be remain largely unaddressed publicly. These questions influence policy, strategic direction, and the future of regional AI sovereignty, making them critical for understanding the continent’s AI landscape.
European Sovereign LLM Initiatives and Common Questions
Across Europe, countries like Italy, Germany, France, and Norway are developing their own large language models, often with public funding and national strategic goals. These efforts are characterized by similar structural questions: How open is the model? How much native-language data is enough? What should the models optimize for? While each project varies in scale and approach, they collectively face these fundamental issues, which influence their design choices, transparency, and strategic value. The European sovereign-LLM movement is still in a formative stage, with many projects in progress and final outcomes uncertain.
“The three questions—openness, native data sufficiency, and objectives—are central to evaluating the true progress and strategic value of European LLMs.”
— Duarte O.Carmo
Unresolved Questions About AMÁLIA’s Openness and Goals
It remains unclear how open AMÁLIA truly is, particularly regarding access to underlying data and model weights. The extent to which native Portuguese data influences the model’s core capabilities is also still being evaluated. Additionally, the strategic objectives—whether the model is primarily for academic, governmental, or commercial use—are not yet fully clarified, and final performance assessments are ongoing as the model approaches its June 2026 release.
Next Steps for AMÁLIA and European Language Models
The final version of AMÁLIA is scheduled for release in June 2026, with ongoing benchmarking and transparency assessments. The project team is expected to publish more detailed data about model openness, native-language performance, and strategic objectives. Simultaneously, broader European efforts will continue to grapple with the same structural questions, shaping policy debates and future investments in sovereign AI models across the continent.
Key Questions
What is the current status of AMÁLIA?
AMÁLIA’s base version is operational, publicly accessible since October 2025, with a final version expected in June 2026.
How does AMÁLIA compare to other European models?
It outperforms many open models on Portuguese benchmarks and beats Qwen 3-8B on most tests, but still trails Qwen on its primary Portuguese benchmark, ALBA.
What are the main concerns about AMÁLIA’s development?
Key concerns include the model’s openness, the sufficiency of native Portuguese data, and clarity about its strategic objectives.
Why are these questions important for European AI?
Addressing these questions determines the transparency, strategic value, and sovereignty of European language models, impacting policy and future AI development across the continent.
Source: ThorstenMeyerAI.com
