Serious LLMs by Country
A curated country-by-country shortlist of serious public large language models with links to their Hugging Face model pages.
LLM choice is becoming a governance question, not only a technical preference. Teams increasingly need to know which serious model family they are using, who publishes it, what country or region the developer is associated with, and what policy should apply before the model touches company data.
The table below is a curated shortlist of serious public large language models with Hugging Face links. It favors models from major labs, national AI efforts, frontier-adjacent open releases, and models with meaningful scale or ecosystem adoption. It is not an exhaustive ranking, and it should not be treated as a safety approval list. Country is mapped to the model developer or publishing organization where that association is clear, not to every contributor, dataset source, cloud region, or downstream fine-tune.
Last reviewed: July 2, 2026.
| Country | LLM / family | Developer or publisher | Why it belongs here | Hugging Face link |
|---|---|---|---|---|
| United States 🇺🇸 | Llama 3.1 405B Instruct | Meta | Major open-weight frontier-scale LLM family with broad enterprise and developer adoption. | meta-llama/Llama-3.1-405B-Instruct |
| United States 🇺🇸 | Gemma 2 27B IT | Serious open model family from Google, designed for text generation and instruction-following use cases. | google/gemma-2-27b-it | |
| France 🇫🇷 | Mixtral 8x22B Instruct | Mistral AI | One of Europe's most important open-weight LLM families from a leading AI lab. | mistralai/Mixtral-8x22B-Instruct-v0.1 |
| China 🇨🇳 | DeepSeek-R1 | DeepSeek | High-profile reasoning model with large-scale open release and significant global adoption. | deepseek-ai/DeepSeek-R1 |
| China 🇨🇳 | Qwen2.5 72B Instruct | Alibaba Cloud / Qwen | Major Chinese open LLM family with strong multilingual, coding, and enterprise ecosystem adoption. | Qwen/Qwen2.5-72B-Instruct |
| Canada 🇨🇦 | Command R+ | Cohere Labs | Enterprise-focused LLM family known for retrieval, multilingual use, and tool-oriented workflows. | CohereLabs/c4ai-command-r-plus |
| United Arab Emirates 🇦🇪 | Falcon 180B Chat | Technology Innovation Institute | Large sovereign AI effort from Abu Dhabi's Technology Innovation Institute. | tiiuae/falcon-180B-chat |
| South Korea 🇰🇷 | EXAONE 3.5 32B Instruct | LG AI Research | Serious Korean LLM effort from a major industrial AI lab. | LGAI-EXAONE/EXAONE-3.5-32B-Instruct |
| Japan 🇯🇵 | gpt-oss-120b FishMath | Sakana AI | Serious research-oriented LLM fine-tune from Sakana AI, focused on advanced mathematical reasoning. | SakanaAI/gpt-oss-120b-sft-aimo3-fishmath |
| India 🇮🇳 | Sarvam 30B | Sarvam AI | India-focused large language model effort from a dedicated national AI startup. | sarvamai/sarvam-30b |
| Switzerland 🇨🇠| Apertus LLM | Swiss AI Initiative | Public, transparent sovereign LLM effort from Swiss research institutions. | swiss-ai/apertus-llm collection |
| Spain 🇪🇸 | Salamandra 7B Instruct | Barcelona Supercomputing Center | Public European language model effort from a national supercomputing center. | BSC-LT/salamandra-7b-instruct |
For governance teams, the useful takeaway is not that one country or LLM family is automatically acceptable or unacceptable. The useful takeaway is that model identity belongs in the approval record. A team should know the model publisher, license terms, intended use, data handling path, retention expectations, and whether the model is being used as a hosted API, private endpoint, local model, fine-tune, or embedded dependency.
This matters because LLM catalogs change quickly. A model that is appropriate for experimentation may not be appropriate for customer data. A model that works well for summarization may not be approved for agentic workflows. A model that is public on Hugging Face may still have license, attribution, acceptable use, or commercial restrictions that need review.
The practical control is to separate discovery from approval. Let teams discover candidate models, then route production use through an AI governance process that checks ownership, privacy guardrails, agent permissions, source access, auditability, and policy enforcement before sensitive workflows depend on the model.
InfoDump treats LLM choice as one part of the larger AI governance layer. Organizations should be able to use the right model for the job while keeping data ownership, controlled access, and accountable AI usage consistent across providers, countries, and deployment patterns.