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What are Large Language Models and how do they work
A Large Language Model (LLM) is a neural network trained on vast collections of texts from the internet, books, scientific articles, and other sources. The basic principle is surprisingly simple: the model predicts what word will come next in the text. By repeating this operation, it generates sentences, paragraphs, and even entire essays.
Modern LLMs are based on the so-called transformer architecture, introduced by Google researchers in 2017. A key element is the attention mechanism, which allows the model to relate words throughout the text to each other – regardless of distance. This enables understanding of context, nuances, and implicit meanings. Current top models contain hundreds of billions of parameters, and their training costs tens to hundreds of millions of dollars.
GPT-5.5: OpenAI Raises the Bar for Performance and Price
OpenAI, the company behind ChatGPT, introduced GPT-5.5 in April 2026 – the latest iteration of its model family. Compared to the previous GPT-5.4 version, the new release promises half the rate of hallucinations, better long-term memory, and smoother work with the Czech language. However, it also comes with a significant price increase: according to OpenRouter analysts, operating GPT-5.5 is approximately twice as expensive as its predecessor.
For regular users, the model is available as part of a ChatGPT Plus subscription for approximately 20 USD per month, or for free in a limited version. For businesses, OpenAI offers API access, which is used by many startups and corporations in the Czech Republic. GPT-5.5 Instant, the default model for most ChatGPT users since May 2026, emphasizes speed and accessibility.
Claude Opus 4.7: Safety as a Competitive Advantage
Anthropic, the second most significant American LLM producer, has opted for a different strategy than OpenAI. Its flagship model Claude Opus 4.7, introduced in April 2026, is positioned as a tool for demanding analytical and creative tasks. Anthropic places extraordinary emphasis on AI safety – Claude models are designed to reject potentially harmful requests and better explain their reasoning.
Claude excels in working with long documents (context window reaches millions of tokens) and in coding. The new version brought expanded possibilities for so-called agentic workflows – Claude can independently plan multi-step tasks, work with external tools, and integrate into corporate systems. For Czech developers and companies, it is available via API or through the Claude.ai platform, although official Czech localization still lags behind the competition.
Mistral: Europe Responds to American Giants
While OpenAI and Anthropic originate from the United States, Mistral AI is a French company that has become a symbol of European independence in generative AI. Mistral develops both open and commercial models that have surprised the professional community with their competitiveness. Their latest models – such as Mistral Large – rank among the top of the global LLM market.
Mistral holds special significance for the Czech Republic. In 2026, the Czech National Bank announced that it operates models from Mistral, OpenAI, and China's Alibaba on its computing clusters with Nvidia chips for financial supervision purposes. Mistral also actively cooperates with European institutions, and its models are designed with the EU AI Act in mind – an artificial intelligence regulation that significantly regulates the deployment of AI in Europe from 2025.
Practical Comparison: When to Use Which Model?
The choice between GPT-5.5, Claude Opus 4.7, and Mistral depends on the specific task. GPT-5.5 is the most versatile choice for regular users – it excels in conversation, idea generation, and quick text analysis. Claude Opus 4.7 is preferred by developers and lawyers who need precise outputs and the ability to process hundreds of pages of documents at once. Mistral is attractive for companies that need to run the model in their own infrastructure (on-premises) or emphasize compliance with European data protection regulations.
All three models in 2026 support Czech at a level enabling fluent communication, yet certain limitations must be considered. The Czech language, especially its declension and word order, still presents a challenge – the best results are achieved when you provide the models with clear instructions and context.
Impact on the Czech Market and Future
According to recent surveys, over 40% of medium and large companies in the Czech Republic already use generative AI. LLMs are gaining ground in customer support, marketing text creation, legal document analysis, and programming. However, with the advent of agentic AI – systems capable of independently planning and executing tasks – the rules of the game are changing. Models no longer just write texts but actively control software, search for information, and communicate with colleagues.
European regulation in the form of the AI Act sets clear rules: high-risk applications must undergo certification, providers must publish information about training data, and users have the right to know that they are communicating with artificial intelligence. For Czech companies, this means that choosing a transparent European supplier – such as Mistral – can be a strategic advantage.
What is the difference between an LLM and a classic chatbot?
Classic chatbots usually operate on the principle of pre-programmed rules or simple statistical models. LLMs, on the other hand, generate responses dynamically based on context and training data, which allows them to solve tasks for which they were not explicitly programmed.
Can I run a large language model locally on my computer?
Yes, but with significant compromises. Open models from Mistral or Llama from Meta can be run locally, but for the full performance of top-tier models, you need specialized hardware with large GPU memory. However, for common office tasks, there are scaled-down versions that even regular workstations can handle.
How does the EU AI Act affect the use of ChatGPT and Claude in the Czech Republic?
The EU AI Act classifies generative models as general-purpose systems and imposes obligations in the areas of transparency, copyright protection, and safety. For regular users, not much changes yet, but companies must ensure that the deployment of AI in key areas (healthcare, law, public administration) meets regulatory requirements.
