--- title: "A guide to the open models used most in business: Llama, Mistral, Qwen, DeepSeek" description: "Llama, Mistral, Qwen, DeepSeek, Gemma: the open model families that matter today, and the criteria that weigh more than a benchmark score." date: 2026-01-20 tags: [Open models, Guide] url: "https://ai.malagoli.me/en/blog/guida-modelli-open-llama-mistral-qwen-deepseek" locale: en_US ---
# A guide to the open models used most in business: Llama, Mistral, Qwen, DeepSeek
<!-- 2026-01-20 · Open models · Guide -->
"Open model" isn't a single category: behind the term are very different families. Here's how to find your way without letting benchmarks alone guide you.
When a company decides to build on open models, the first surprise is discovering how many there are. There is no single "open" model: there are different families, with different goals, strengths, and licenses. Choosing the right one matters as much — sometimes more — than the choice between open and closed.
## The main families, in brief
- Llama (Meta): the most widely used, a huge ecosystem of tools and integrations, a solid generalist starting point.
- Mistral: efficient models, developed in Europe, often a natural choice where European regulatory and linguistic proximity matters.
- Qwen (Alibaba): very solid multilingual support, excellent at coding tasks, rapidly growing in enterprise adoption.
- DeepSeek: low training costs, strong on reasoning tasks, very fast development between versions.
- Gemma (Google): designed to run on smaller hardware, suited to cases where efficiency matters more than maximum capability.
## The criteria that matter more than the benchmark score
Public leaderboards compare models on standardized tasks that rarely match your actual use case. Before choosing, check: quality in your company's working language (many benchmarks are English-only and say little about performance in other languages), the context window size relative to your documents, the license and what it actually permits, the tooling ecosystem for fine-tuning and serving, and the real hardware requirements for the volume you expect.
## A practical four-question framework
- Is the use case generalist or specialized (code, mathematics, a vertical domain)? Some families are clearly stronger in specific areas.
- How much does performance in your working language matter relative to English in your context?
- What hardware do you already have, or are willing to acquire? Some models perform much better quantized, others lose quality.
- Does the license permit commercial use in your specific scenario, including the expected number of users?
The right answer differs from company to company, and often even from one project to another within the same company. That's why, before committing to a model family, it's always worth testing on a real use case with your own data — not relying on a leaderboard alone.
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