📊 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 is now active, outperforming some benchmarks. However, fundamental questions about its openness, native data use, and objectives are still unresolved, raising concerns about the broader European sovereign-LLM effort.
Portugal’s €5.5 million AI project, AMÁLIA, is now operational, with the model publicly accessible through academic platforms. However, questions about its openness, native-language data, and strategic goals remain unanswered, highlighting broader issues in the European sovereign-LLM movement.
AMÁLIA, a consortium project involving approximately 60 researchers from Portugal’s top research institutions, was officially released in September 2025. The model is based on a continuation of the EuroLLM multilingual foundation, with the Portuguese component comprising about 5.8 billion tokens from the Portuguese web archive and 17-18% of supervised fine-tuning data being Portuguese. It outperforms previous open models on European Portuguese benchmarks and surpasses Qwen 3-8B on most Portuguese-specific tests, although it still trails Qwen on the primary ALBA benchmark.
Despite these technical achievements, Duarte O.Carmo’s analysis emphasizes that the project raises three critical questions: How open is the model truly? How much native-language data is enough? And what are the strategic objectives guiding the development? These questions are central to evaluating the model’s broader impact and are currently left largely unanswered by the project’s public disclosures.
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.
Mastering LM Studio to Create AI Agents Locally: Master the Art of Local AI Development with LM Studio: A Comprehensive Guide to Building, Optimizing, and Integrating AI Agents
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
European Portuguese NLP tools
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
Large Language Models: The Hard Parts: Open Source AI Solutions for Common Pitfalls
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
Cloud Native Go: Building Reliable Services in Unreliable Environments
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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-Language AI Development
The AMÁLIA project exemplifies the challenges faced by European nations in developing autonomous, native-language AI models. Its partial openness, reliance on a multilingual foundation, and unclear strategic aims reflect broader structural issues in the European sovereign-LLM movement. These unresolved questions influence national AI policies, research priorities, and international competitiveness, making their answers critical for future developments.
European Sovereign-LLM Efforts Face Fundamental Questions
Across Europe, countries like Italy, Germany, France, and Norway are investing in native-language large language models, often with public funding. These efforts share a common structural challenge: balancing openness, native data use, and strategic goals. Portugal’s AMÁLIA is the latest example, with a significant investment and public deployment, but it highlights a recurring pattern where the core questions about model openness, native data sufficiency, and purpose remain largely unaddressed publicly.
Historically, models like Italy’s Minerva and France’s Mistral have trained from scratch or used different approaches, but all face similar issues of transparency and strategic clarity. The European initiative as a whole appears to be operating in a phase of experimentation, with structural questions yet to be fully answered.
“The AMÁLIA project raises three fundamental questions that European sovereign-LLMs must answer publicly: How open is ‘fully open’? How much native data is enough? And what are we optimizing for?”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Openness and Goals
It remains unclear how open the AMÁLIA model will be in its final form, especially regarding access to training data and source code. Additionally, the strategic objectives guiding its development are not publicly articulated, leaving questions about its intended use, governance, and long-term vision still unaddressed. The final version is expected in June 2026, but details on these aspects are still emerging.
Next Steps in Evaluating AMÁLIA and European Models
In the coming months, the research team is expected to release further details on AMÁLIA’s capabilities, openness, and strategic goals. The final version due in June 2026 will likely provide more clarity on native-language data use and model accessibility. Meanwhile, policymakers and researchers will scrutinize how these models align with national AI strategies and European sovereignty ambitions.
Key Questions
What is the current status of AMÁLIA?
The base version is publicly available since October 2025, with ongoing development towards a final version expected in June 2026.
How does AMÁLIA compare to other European models?
It outperforms previous open models on European Portuguese benchmarks and beats Qwen 3-8B on most Portuguese-specific tests, though it still trails Qwen on the primary ALBA benchmark.
What are the main concerns about AMÁLIA?
The main concerns involve the model’s openness, the sufficiency of native-language data, and the clarity of its strategic objectives.
Why are these questions important?
They determine the transparency, usability, and strategic value of the model, impacting Portugal’s and Europe’s AI sovereignty and competitiveness.
What will happen next in this project?
The final version will be released by June 2026, with further disclosures expected on data, openness, and strategic aims, shaping future policy and research directions.
Source: ThorstenMeyerAI.com
