Mistral turns focus toward regional LLMs with Saba release

French AI startup Mistral is turning its focus toward providing large language models (LLMs) that understand regional languages and their parlance as a result of rising demand among its enterprise customers.

“Making AI ubiquitous requires addressing every culture and language. As AI proliferates globally, many of our customers worldwide have expressed a strong desire for models that are not just fluent but native to regional parlance,” the company wrote in a blog post.

Explaining further, it said that while larger LLMs are more general purpose and often proficient in several languages, they often fail to understand the usage of words in a certain language or lack understanding of the cultural background, which leads to failure of servicing use cases in local languages.

Some examples of these use cases could be conversational support, domain-specific expertise, and cultural content creation.

Mistral believes that LLMs that are custom-trained in regional languages can help service these use cases as the custom training would help an LLM “grasp the unique intricacies and insights for delivering precision and authenticity.”

Mistral’s first custom-trained regional language LLM

Mistral has released its first custom-trained regional language-focused model named Saba, which is a 24-billion parameter model. According to Mistral, the LLM has been trained on “meticulously curated datasets” from across the Middle East and South Asia.

This means that Saba can support use cases in Arabic and many Indian-origin languages, particularly South Indian-origin languages, such as Tamil the company said, adding that Saba’s support for multiple languages could increase its adoption.

Mistral claims that Saba is similar to its Mistral Small 3 model in size and this means that it is relatively cheaper to use than most LLMs.

Saba is lightweight and can be deployed on single-GPU systems, making it “more adaptable” for a variety of use cases, the company said, adding that the LLM can serve as a strong base to train highly specific regional adaptations.

The LLM’s deployment options include an API and local deployment on-premises. Mistral said the local deployment option could help more regulated industries, such as finance, banking, and healthcare, adopt the model.

In benchmark tests, such as Arabic MMLU, Arabic TyDiQAGoldP, Arabic Alghafa, and Arabic Hellaswag, Saba outperforms Mistral Small 3, Qwen 2.5 32B, Llama 3.1 70B, and G42’s Jais 70B.

Saba also outperforms LLama 3.3 70B Instruct, Cohere Command-r-08-2024 32B, Jais 70B Chat, and GPT-4o-mini in benchmarking tests, such as Arabic MMLU Instruct, Arabic MT-Bench Dev, and Arabic-Centric FLORES-101.

Why is Mistral turning its focus toward regional language LLMs?

Mistral’s focus on releasing regional language LLMs could help the company expand its overall revenue, analysts say.

“There’s a growing market for regional LLMs like Saba, especially for enterprises needing culturally and linguistically tailored solutions. The market could be significant, driven by demand for localized AI in sectors like finance, healthcare, and government, potentially reaching billions as businesses seek to enhance customer engagement and operational efficiency,” said Charlie Dai, principal analyst at Forrester.

“LLMs finetuned towards regional markets address specific linguistic, cultural, and regulatory needs, making AI solutions more relevant and effective for local enterprises. This differentiation can drive adoption and unlock revenue growth in underserved markets,” Dai explained.

In addition to regional language LLMs, Mistral said it has started training models for strategic customers who can provide deep and proprietary enterprise context.

“These (custom) models stay exclusive and private to the respective customers,” the company wrote in the blog post.

However, analysts warned that Mistral is not the only model provider trying to use the regional language model playbook for expansion.

BAAI from China open-sourced their Arabic Language Model (ALM) back in 2022. This was followed by DAMO of Alibaba Cloud open-sourcing its PolyLM in 2023 covering eleven languages including Arabic, Spanish, German, and others.

“We have been observing that language-specific LLMs have been growing in the Middle East. We saw some regional LLM launches by start-ups such as G42, which launched one of the first Arabic LLMs,” said Suseel Menon, practice director at Everest Group.

Alongside pointing out that regional public sector organizations in the Middle East have been attempting to create Arabic LLMs, such as the Saudi Data and AI Authority (SDAIA) that launched its LLM named ALLaM on IBM Cloud last year, Menon said that Saba’s presence is likely to drive more competition among model providers in the region.

Mistral also faces competition in South Asia, specifically in India where several startups have used Llama 2 to create regional language models, such as OpenHathi-Hi-v0.1 for Hindi, Tamil Llama, Telegu Llama, odia_llama2_7B_v1, and VinaLLaMA for Vietnamese.

But Dai believes that the announcement of the models is just the first step. “Model providers who offer high-quality, localized solutions will only gain loyalty and market share in underserved areas,” Dai explained, adding that regional business operations around the models are another key to success.