LLM comparison: Mistral, GPT, LLaMA

Comparison of major LLMs for business: Mistral, GPT-4, LLaMA 3. Performance, costs, sovereignty and enterprise use cases. The LLM landscape is now broad enough that SMEs need selection criteria, not hype. GPT-4-class models still lead on versatility and ecosystem maturity, but they can be expensive at scale and often imply sending data to US-based services. Mistral stands out for European companies because it combines strong performance with better sovereignty options, including on-premise or controlled deployment patterns. LLaMA remains highly valuable when customisation and fine-tuning matter, but it usually requires a more technical team and a clearer operating framework. The real comparison should not stop at benchmark scores. It must cover confidentiality, deployment model, operating cost, integration effort and governance. For many SMEs, Mistral 7B or similar local-capable models offer the best balance for internal assistants, document search or controlled automation. GPT-style APIs remain useful for exploration and occasional high-complexity tasks. The key rule is simple: choose the model according to business context and data sensitivity, not according to brand visibility alone.