📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA, a €5.5 million European Portuguese language model, is live and outperforms many benchmarks. However, it faces three fundamental questions about its openness, native data, and objectives, reflecting broader issues in European sovereign-LLMs.
Portugal’s €5.5 million AMÁLIA large language model is now operational, marking a significant step in the country’s AI efforts. Developed by a consortium of 60 researchers across leading institutions, it outperforms many existing models on Portuguese benchmarks. However, critical questions about its openness, native-language data, and strategic goals remain unanswered, raising concerns about the broader European sovereign-LLM landscape.
The AMÁLIA project, announced in December 2024 and completed by September 2025, involves Portugal’s top research institutions and is publicly accessible through the FCT’s IAedu platform to 450,000 academic users. It is based on a continuation of the EuroLLM multilingual foundation, with a focus on Portuguese. The model has been shown to outperform previous open models on Portuguese benchmarks and surpasses Qwen 3-8B on most tests, though it still trails on certain specific tasks like ALBA.
Despite these achievements, questions persist about how open AMÁLIA truly is, with debates around the transparency of its training data and architecture. Additionally, the project’s strategic goals—whether it aims for broad openness, native-language dominance, or specific performance metrics—are not fully clarified. These issues echo broader concerns across European sovereign-LLM initiatives, which face similar structural questions.
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.
Portuguese language large language model
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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.
open-source AI language model
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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.
AI training data transparency tools
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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.
European sovereign LLM solutions
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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.
Implications for European Sovereign-LLM Strategies
The case of AMÁLIA highlights critical structural questions facing European national AI efforts: how open should models be, how much native-language data is sufficient, and what their primary objectives should be. These issues influence policy, transparency, and competitiveness, making them vital for understanding Europe’s position in the global AI landscape. The answers to these questions will shape future models and national AI policies across Europe.
European Sovereign-LLM Efforts and Portugal’s Role
European countries have launched multiple large language model initiatives, such as Italy’s Minerva, Germany’s Aleph Alpha, and France’s Mistral, often with public funding and strategic aims. Portugal’s AMÁLIA stands out as a publicly funded project with a clear national scope, involving extensive academic collaboration. The broader European movement faces common challenges: balancing openness with security, native-language data sufficiency, and defining clear strategic goals amid a rapidly evolving AI landscape.
Public discourse has often focused on individual model capabilities, but experts like Duarte O.Carmo emphasize the importance of understanding the structural choices and policy implications underlying these projects. The ongoing development of AMÁLIA and similar models will significantly influence Europe’s AI sovereignty and competitiveness in the coming years.
“The three questions—openness, native data, and strategic goals—are fundamental to evaluating the true value and direction of European LLM efforts.”
— Duarte O.Carmo
Unresolved Questions About Openness and Goals
It remains unclear how open AMÁLIA truly is, particularly regarding the transparency of its training data and architecture. The strategic objectives—whether prioritizing native-language dominance, openness, or specific benchmarks—are also not fully articulated. Additionally, the final version’s capabilities and how they will meet evolving needs are still under development, with some gaps likely to be addressed before June 2026.
Upcoming Milestones and Policy Discussions
The final version of AMÁLIA is scheduled for release in June 2026, which will provide a more comprehensive evaluation of its capabilities and transparency. Simultaneously, European policymakers and researchers will likely intensify discussions around openness, native data, and strategic goals, shaping future national and pan-European AI policies. Further technical assessments and public debates are expected in the coming months to clarify these critical issues.
Key Questions
What are the main technical features of AMÁLIA?
AMÁLIA is based on a continuation of the EuroLLM multilingual foundation, with a focus on Portuguese. It was trained on approximately 107 billion tokens, including 5.8 billion tokens from Portuguese web archives, and outperforms previous open models on Portuguese benchmarks.
Why are questions about openness and native data important?
Transparency about training data and architecture impacts trust, reproducibility, and strategic control. These factors influence how the model can be used, regulated, and integrated into national AI ecosystems.
What are the broader implications for European AI efforts?
Addressing these questions will determine Europe’s competitiveness, sovereignty, and ability to develop AI that aligns with national values and policies. The outcome will shape the future of European models and their role in global AI development.
When will the final version of AMÁLIA be available?
The final version is scheduled for release in June 2026, with ongoing evaluations and potential adjustments before then.
What challenges does AMÁLIA face compared to other models?
Compared to models trained from scratch like Italy’s Minerva, AMÁLIA’s approach of building on a multilingual foundation may limit native-language specialization but offers strategic advantages in integration and resource sharing. However, transparency and strategic clarity remain challenges.
Source: ThorstenMeyerAI.com
