The European Union has 24 official languages and dozens more unofficial ones spoken across the continent. If you add in the European countries outside the union, then that brings at least a dozen more into the mix. Add dialects, endangered languages, and languages brought by migrants to Europe, and you end up with hundreds of languages.

One thing many of us in technology could agree on is that the US dominates — and that extends to online languages. There are many reasons for this, mostly due to American institutions, standards bodies, and companies defining how computers, their operating systems, and the software they run work in their nascent days. This is changing, but for the short term at least, it remains the norm. This has also led to the majority of the web being in English. An astounding 50% of websites are in English, despite it being the native tongue of only about 6% of the world’s population, with Spanish, German, and Japanese next, but a long way behind, each only between 5-6% of the web.

As we delve deeper into the new wave of AI-powered applications and services, many are driven by data in large language models (LLMs). As much of the data in these LLMs is scraped (controversially in many cases) from the web, LLMs predominantly understand and respond in English. As we find ourselves at the start of or in the midst of a shift in technological paradigm caused by the rapid growth of AI tools, this is a problem, and we’re bringing that problem into a new age.

Europe already boasts several high-profile AI companies and projects, such as Mistral and Hugging Face. Google DeepMind also originated as a European company. The continent has research projects that develop language models to enhance how AI tools comprehend less commonly spoken languages.

This article explores some of these initiatives, questions their effectiveness, and asks whether their efforts are worthwhile or if many users default to using English versions of tools. As Europe seeks to build its independence in AI and ML, does the continent have the companies and skills necessary to achieve its goals?

Terminology and technology primer

To make sense of what follows, you don’t need to understand how models are created, trained, or function. But it’s helpful to understand a couple of basics about models and their human language support.

Unless model documentation explicitly mentions it is multilingual or cross-lingual, prompting it or requesting a response in an unsupported language may cause it to translate back and forth or respond in a language it does understand. Both strategies can produce unreliable and inconsistent results — especially in low-resource languages.

While high-resource languages, such as English, benefit from abundant training data. Low-resource languages, such as Gaelic or Galician, have far less, which often leads to inferior performance

The harder concept to explain regarding models is “open,” which is unusual, as software in general has had a fairly clear definition of “open source” for a while. I don’t want to delve too deeply into this topic as the exact definition is still in flux and controversial. The summary is that even when a model might call itself “open” and is referenced as “open,” the meaning of “open” isn’t always the same.

Here are two other useful terms to know:

Training teaches a model to make predictions or decisions based on input data.

Parameters are variables learned during model training that define how the model maps inputs to outputs. In other words, how it understands and responds to your questions. The larger the number of parameters, the more complex the model is.

With that brief explanation done, how are European AI companies and projects working to enhance these processes to improve European language support?

Hugging Face

When someone wants to share code, they typically provide a link to their GitHub repository. When someone wants to share a model, they typically provide a Hugging Face link. Founded in 2016 by French entrepreneurs in New York City, the company is an active participant in creating communities and a strong proponent of open models. In 2024, it started an AI accelerator for European startups and partnered with Meta to develop translation tools based on Meta’s “No Language Left Behind” model. They are also one of the driving forces behind the BLOOM model, a groundbreaking multilingual model that set new standards for international collaboration, openness, and training methodologies.

Hugging Face is a useful tool for getting a rough idea of the language support in models. At the time of writing, Hugging Face lists 1,743,136 models and 298,927 datasets. Look at its leaderboard for monolingual models and datasets, and you see the following ranking for models and datasets that developers tag (add metadata) as supporting European languages at the time of writing:

You can already see some issues here. These aren’t tags set in stone. The community can add values freely. While you can see that they follow them for the most part, there is some duplication.

As you can see, the models are dominated by English. A similar issue applies to the datasets on Hugging Face, which lack non-English data.

What does this mean?

Lucie-Aimée Kaffee, EU Policy Lead at Hugging Face, said that the tags indicate that a model has been trained to understand and process this language or that the dataset contains materials in that language. She added that the confusion between language support often comes during training.“When training a large model, it’s common for other languages to accidentally get caught in training because there were some artefacts of it in that dataset,” she said. “The language a model is tagged with is usually what the developers intended the model to understand.”

As one of the main and busiest destinations for model developers and researchers, Hugging Face not only hosts much of their work, but also lets them create outward-facing communities to tell people how to use them.

Mistral AI

Perhaps the best-known Europe-based AI company is France’s Mistral AI, which unfortunately declined an interview. Its multilingual challenges partly inspired this article. At the FOSDEM developer conference in February 2024, linguistics researcher Julie Hunter asked one of Mistral’s models for a recipe in French — but it responded in English. However, 16 months is an eternity in AI development, and neither the company’s “Le Chat” chat interface nor running its 7B model locally reproduced the same error in recent tests. But interestingly, 7B did produce a spelling error in the opening line: “boueef” — and more may follow.

While Mistral sells several commercial models, tools, and services, its free-to-use models are popular, and I personally tend to use Mistral 7B for running tasks through local models.

Until recently, the company wasn’t explicit about its models having multilingual support, but its announcement of the Magistral model at London Tech Week in June 2025 confirmed support for several European languages.

EuroLLM

EuroLLM was created as a partnership between Portuguese AI platform Unbabel and several European universities to understand and generate text in all official European Union languages. The model also includes non-European languages widely spoken by immigrant communities and major trading partners, such as Hindi, Chinese, and Turkish.

Like some of the other open model projects in this article, its work was partly funded by the EU’s High Performance Computing Joint Undertaking program (EuroHPC JU). Many of them share similar names and aims, making it confusing to separate them all. EuroLLM was one of the first, and as Ricardo Rei, Senior Research Scientist at Unbabel, told me, the team has learned a lot from the projects that have come since.

As Unbabel’s prime business is language translation, and translation is a key task for many multilingual models, the work on EuroLLM made sense to the Portuguese platform. Before EuroLLM, Unbabel had already been refining existing models to make its own and found them all too English-centric.

One of the team’s biggest challenges was finding sufficient training data for low-resource languages. Ultimately, the availability of training material reflects the number of people who speak the language. One of the common data sources used to train European language models is Europarl, which contains transcripts of the European Parliament’s activities translated into all official EU languages. It’s also available as a Hugging Face dataset, thanks to ETH Zürich.

Currently, the project has a 1.7B parameter model and a 9B parameter model, and is working on a 22B parameter model. In all cases, the models can translate, but are also general-purpose, meaning you can chat with them in a similar way to ChatGPT, mixing and matching languages as you do.

OpenLLM Europe

OpenLLM Europe isn’t building anything directly, but it’s fostering a Europe-wide community of LLM projects, specifically medium and low-resource languages. Don’t let the one-page GitHub repository fool you: the Discord server is lively and active.

OpenEuroLLM, Lumi, and Silo

A joint project between several European universities and companies, OpenEuroLLM is one of the newer and larger entrants to the list of projects funded by EuroHPC. This means that it has no public models as of yet, but it involves many of the institutions and individuals behind the Lumi family of models that focus on Scandinavian and Nordic languages. It aims to create a multilingual model, provide more datasets for other models and conform to the EU AI Act.

I spoke with Peter Sarlin of AMD Silo, one of the companies involved in the project and a key figure in Finnish and European AI development, about the plans. He explained that Finland, especially, has several institutes with significant AI research programs, including Lumi, one of the supercomputers part of EuroHPC. Silo, through its SiloGen product, offers open source models to customers, with a strong focus on supporting European languages. Sarlin pointed out that while sovereignty is an important motivation to him and Silo for creating and maintaining models that support European languages, the better reason is expanding the business and helping companies build solutions for small markets such as Estonia.

“Open models are great building blocks, but they aren’t as performant as closed ones, and many businesses in the Nordics and Scandinavia don’t have the resources to build tools based on open models,” he said. “So Silo and our models can step in to fill the gaps.”

The Lumi models use a “cross-lingual training” technique in which the model shares its parameters between high-resource and low-resource languages.

All this prior work led to the OpenEuroLLM project, which Sarlin describes as “Europe’s largest open source AI initiative ever, including pretty much all AI developers in Europe apart from Mistral.”

While many efforts are underway and performing well, the training data issue for low-resource languages remains the biggest challenge, especially amid the move towards more nuanced reasoning models. Translations and cross-lingual training are options, but can create responses that sound unnatural to native speakers. As Sarlin said, “We don’t want a model that sounds like an American speaking Finnish.”

OpenLLM France

France is one of the more active countries in AI development, with Mistral and Hugging Face leading the way. From a community perspective, the country also has OpenLLM France. The project (unsurprisingly) focuses on French language models, with several models of different parameters and datasets, which help other projects train and improve their models that support French. The datasets include a mix of political discourse, meeting recordings, theatre shows, and casual conversations. The project also maintains a leaderboard of French models on Hugging Face, one of the few (active) European language model benchmark pages.

Do Europeans care about multilingual AI?

Europe is full of people and projects working on multilingual language models. But do consumers care? Unfortunately, getting language usage rates for proprietary tools such as ChatGPT or Mistral is almost impossible. I created a poll on LinkedIn asking if people use AI tools in their native language, English, or a mixture of both. The results were a 50/50 split between English and a mixture of languages. This could indicate that the number of people using AI tools in a non-English language is higher than you think.

Typically, people use AI tools in English for work and in their own language for personal tasks.

Kaffee, a German and English speaker, said: “I use them mostly in English because I speak English at work and with my partner at home. But then, for personal tasks…, I use German.”

Kaffee mentioned that Hugging Face was working on a soon-to-be-published research project that fully analysed the usage of multilingual models on the platform. She also noted anecdotally that their usage is on the rise. 

“Users have a conception that models are now more multilingual. And with the accessibility through large models like Llama, for example, being multilingual, I think that made a big impact on the research world regarding multilingual models and the number of people wanting to now use them in their own language.”

The internet was always supposed to be global and for everyone, but the damning statistic that 50% of sites are in English shows it never really worked out that way. We’re entering a new phase in how we access information and who controls it. Maybe this time, the (AI) revolution will be international.