📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s €5.5 million AMÁLIA large language model is now operational, outperforming some benchmarks. However, fundamental questions about its openness, native-language data, and objectives remain unresolved, highlighting broader issues in European sovereign-LLM efforts.
Portugal’s €5.5 million state-funded large language model, AMÁLIA, is now operational and publicly accessible, with initial benchmarks showing it outperforming some European models on Portuguese tasks. However, key structural questions about the model’s openness, native-language data, and primary objectives remain unanswered, raising concerns about the broader European sovereign-LLM landscape.
AMÁLIA was developed by a consortium of approximately 60 researchers across Portugal’s top institutions, including NOVA, IST, and IT, and was announced by the government in December 2024. The model’s base version was completed in September 2025 and is available through the FCT’s IAedu platform to 450,000 academic users. It is trained as a continuation of the EuroLLM multilingual foundation, with a focus on European Portuguese, and has demonstrated superior performance on Portuguese benchmarks compared to previous open models, and on most benchmarks compared to Qwen 3-8B, though it still lags on some specific tasks.
Despite these achievements, the project faces scrutiny over three core questions: How open is ‘fully open’ in practice? How much native-language data is sufficient? And what should the model be optimized for? These questions are central to evaluating the strategic and technical success of sovereign-language models, yet they remain largely unaddressed publicly, according to recent analysis by Duarte O.Carmo.
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.
large language model LLM development kit
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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.
Portuguese language AI training data
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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 model openness evaluation 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 Portuguese NLP software
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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 development and deployment of AMÁLIA exemplify a broader trend among European countries investing in national LLMs, often with public funds and institutional backing. The unresolved questions about openness, native data sufficiency, and goal setting are critical for understanding whether these models can truly serve national interests, promote data sovereignty, and foster innovation within Europe. The lack of transparent answers risks undermining public trust and strategic coherence in these efforts, especially as other nations pursue similar projects.
European Sovereign LLM Initiatives and Structural Challenges
Across Europe, multiple countries have launched or announced sovereign-language LLM projects, including Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and others within the OpenEuroLLM consortium. These efforts share a common structural challenge: balancing openness with strategic control, determining native-language data requirements, and defining primary objectives—whether for general-purpose AI, national security, or linguistic preservation. The case of AMÁLIA highlights how these questions are often addressed superficially or remain unresolved, complicating the assessment of their long-term viability and strategic coherence.
“The three questions about openness, native data, and objectives are the structural core that all European sovereign LLM efforts must confront openly.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Openness and Goals
It remains unclear how open AMÁLIA truly is in practice, especially regarding access, licensing, and transparency. The extent to which native Portuguese data suffices for future improvements is also still under debate, as is the ultimate goal of the model—whether it is primarily for academic use, strategic autonomy, or commercial deployment. The final version, due in June 2026, may address some of these gaps, but current details are limited.
Next Milestones for AMÁLIA and European Sovereign Models
The next 12-24 months will be critical as the final version of AMÁLIA is released and evaluated. Researchers and policymakers will scrutinize its openness, native-language data strategies, and practical applications. Additionally, other European countries will likely reveal their plans, making it essential to observe how these models evolve and whether they address the structural questions openly. Transparency and strategic clarity will be key indicators of success or ongoing challenges.
Key Questions
What are the main concerns about AMÁLIA’s openness?
It is still unclear how accessible the model will be to external researchers, whether the data and training processes will be fully transparent, and if the model will be open-source or restricted.
How much native Portuguese data was used in training AMÁLIA?
Approximately 5.8 billion tokens from the Portuguese web archive Arquivo.pt were used, representing about 5.5% of the extended pre-training mixture, but whether this is sufficient for future development remains debated.
What are the strategic goals of AMÁLIA?
The primary objectives are still not explicitly defined, but likely include fostering academic research, national AI sovereignty, and linguistic preservation, though these are not formally clarified.
Will the final version address these unresolved issues?
It is expected that the final version in June 2026 may clarify some of these questions, but current details are limited, and the issues remain open for debate.
Source: ThorstenMeyerAI.com
