In an era when models like GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro can translate entire chapters of text seconds after entering a prompt, it seems that professional translators are a thing of the past. The reality, however, is far more complex. As the case of Slovak analyst and author Róbert Ďurec shows, artificial intelligence is not a replacement for humans, but an extremely powerful tool that requires a precise conductor.
The Case of "King Who Lives Everything": The First AI Literature Experiment
Róbert Ďurec, a data analytics expert and author of the book "King Who Abounds in Everything", presented a unique project. It wasn't just about writing text with the help of AI, but about a complete case study on how this process is handled in translation. His work is fascinating because it combines creative writing under an open CC-BY license with a transparent description of how language intelligence models work with text.
An important insight from his experience is the division of labor. While LLMs (Large Language Models) excel in grammatical correctness and speed, they often fail to capture the deeper philosophical subtext or specific cultural metaphors that are key to fiction. This leads us to the question: Where exactly do the limits of today's models lie?
Comparison of Top Models in Translation
If you're looking for a translation tool today, you're not just choosing a "translator" — you're choosing between different architectures of thought. Here's a comparison of current market leaders (as of July 2026):
- Claude 3.5 Sonnet (Anthropic): Currently considered the top choice for creative and nuance-driven translations. It tends to write more naturally and less "robotically" than the competition. It handles literary style excellently, which is confirmed by the success of projects similar to Ďurec's.
- GPT-4o (OpenAI): A universal champion. It is extremely fast and great for technical texts where terminological accuracy matters. It is very well localized for the Czech environment.
- Gemini 1.5 Pro (Google): Thanks to its enormous context window, it is ideal for translating entire books or extensive documentation, where the model must maintain consistency of terms across hundreds of pages.
- DeepL: A specialized tool that, while not an LLM in the strict sense (though it uses neural networks), still remains the standard for quick, factual translations without the need for complex prompting.
For the average user, this means that the choice of tool depends on the goal. Need to translate an email to your boss? Use ChatGPT. Need to translate a book or an essay? Reach for Claude. Need a technical manual for a washing machine? DeepL will suffice.
Practical Impact: What Does This Mean for Czech Companies and Freelancers?
For the Czech market and the European scene, this "80/20 rule" has significant economic implications. Companies no longer have to pay for translating every word from scratch, but they must invest in the role of a post-editor.
1. Efficiency vs. Risk: Automated translation can reduce localization costs by 60–70%. However, if a company skips human review in an effort to save money, it risks contextual errors that can be fatal in marketing or legal documents. Under the EU AI Act (EU regulation on artificial intelligence), transparency in generated content must remain an increasingly important topic.
2. Availability and Price:
