📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA, a €5.5M European Portuguese LLM, is now operational and outperforms some benchmarks. However, fundamental questions about openness, native data sufficiency, and optimization goals remain unanswered, highlighting broader issues in European sovereign-LLM efforts.
Portugal’s €5.5 million investment in the AMÁLIA large language model has resulted in a functioning, publicly accessible system that outperforms some benchmarks on European Portuguese tasks. However, key structural questions about the model’s openness, native-language data sufficiency, and strategic goals remain unanswered, raising concerns about the broader European sovereign-LLM movement.
AMÁLIA is a consortium project involving approximately 60 researchers from Portugal’s leading institutions, including NOVA, IST, and IT. The model, announced in December 2024 and completed by September 2025, is based on a continuation of the EuroLLM multilingual foundation, with a focus on Portuguese. It is accessible through the FCT’s IAedu platform to 450,000 academic users, with knowledge cut-off at the end of 2023. Technical benchmarks show AMÁLIA surpasses previous open models on Portuguese-specific tasks and beats Qwen 3-8B on most benchmarks, though it still trails on some, such as ALBA.
Despite these achievements, questions about the model’s openness, the adequacy of native-language data, and the strategic objectives guiding its development remain largely unaddressed. The technical approach involves extending a multilingual foundation rather than training from scratch, contrasting with Italy’s Minerva, which trained from scratch on Italian and English data. The ongoing development and upcoming final version in June 2026 are expected to clarify some of these issues, but the current state highlights systemic challenges in the European sovereign-LLM landscape.
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.
AI model openness and 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.
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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.
Broader Implications for European Sovereign-LLMs
The case of AMÁLIA exemplifies critical structural issues facing European national LLM initiatives. The questions around how open these models truly are, whether native-language data is sufficient, and what their primary goals should be are not just technical concerns but have significant policy and strategic implications. Addressing these questions openly is essential for ensuring transparency, fostering trust, and guiding future investments in sovereign AI capabilities. The ongoing discourse influences not only Portugal but also the broader European effort to develop independent, language-specific AI models that can compete globally.
Structural Challenges in European Sovereign-LLM Development
Across Europe, countries like Italy, Germany, France, and Norway are investing in sovereign LLMs, often with public funds, to foster national AI independence. These efforts are characterized by different technical approaches—some training models from scratch, others building on multilingual foundations—yet all face shared questions about openness, data sufficiency, and strategic direction. The case of AMÁLIA highlights how these projects are often evaluated as individual launches rather than as part of a broader, systemic pattern. The €5.5 million Portuguese investment and the model’s public deployment make it a prominent example of these structural issues.
Public discourse has thus far focused on benchmarks and technical performance, but experts like Duarte O.Carmo have called for more scrutiny of the foundational questions that will determine the long-term success and trustworthiness of these models. The upcoming final version of AMÁLIA will be a critical milestone in this ongoing evaluation.
“While AMÁLIA demonstrates impressive performance, the fundamental questions about openness and native data remain largely unaddressed.”
— Duarte O.Carmo
Unanswered Questions About Model Openness and Strategy
It is still unclear how open AMÁLIA truly is, especially regarding access to training data, model weights, and fine-tuning procedures. The strategic goals guiding its development—whether it aims for broad accessibility, commercial deployment, or governmental use—are not explicitly defined. Additionally, the sufficiency of native Portuguese data remains a debated point, with some experts questioning if the current dataset adequately captures the language’s diversity and complexity. The final version scheduled for June 2026 may provide clarity, but current details are limited.
Next Milestones and Evaluation Points for AMÁLIA
The immediate next step is the release of the final version of AMÁLIA in June 2026, which is expected to address some of the current gaps. Researchers and policymakers will scrutinize its openness, native data integration, and alignment with strategic goals. Additionally, ongoing benchmarking and independent evaluations will determine whether the model meets the expectations set by its initial performance. Broader discussions about transparency and governance of sovereign LLMs across Europe are likely to intensify as these developments unfold.
Key Questions
What makes AMÁLIA different from other European language models?
AMÁLIA is based on a continuation of a multilingual foundation rather than training from scratch, which contrasts with models like Italy’s Minerva. It is publicly funded and publicly accessible within Portugal, making its development and performance more transparent.
Why are the questions about openness and native data important?
Openness determines how accessible and transparent the model is, affecting trust and collaboration. Native data sufficiency impacts the model’s ability to accurately represent and serve the Portuguese language and culture, which is vital for national AI sovereignty.
What are the risks of not addressing these structural questions?
Ignoring these questions could lead to models that are less trustworthy, less effective for native speakers, and less aligned with national or European strategic interests, potentially undermining efforts for AI independence.
When will the final version of AMÁLIA be available?
The final version is scheduled for release in June 2026, which will be a key moment for evaluating whether it has addressed the current gaps.
How does this impact Portugal’s AI strategy?
It highlights the importance of transparency, data quality, and clear strategic objectives in national AI investments, shaping future policies and funding priorities.
Source: ThorstenMeyerAI.com
