"Machine translation" is a term that describes very different technologies. What Google Translate did in 2010 and what a modern LLM does are fundamentally different things. Understanding the difference is important: the quality of website translation directly affects conversion and search rankings.
Generation 1: Statistical Machine Translation (SMT)
Statistical Machine Translation worked on parallel text corpora: the system analyzed translated documents and selected the statistically most probable translation for each word or phrase.
Result: translations were mechanical, lost context, and produced characteristic "machine-like" phrases. It was about SMT that people joked that the text after it needed to be deciphered.
SMT has not been used in commercial systems since 2016-2017.
Generation 2: Neural Machine Translation (NMT)
Neural Machine Translation processes text differently: not word by word, but the entire text as a whole, taking context into account.
Key improvements compared to SMT:
- A word is not translated in isolation — the context of the entire sentence is taken into account
- Idioms and fixed expressions are handled more correctly.
- The naturalness of the text is significantly higher.
- Tone and style are better preserved.
Google Translate switched to NMT in 2016. DeepL was originally built as an NMT engine and held the lead in quality for a long time.
For most texts, NMT provides an acceptable result — technical descriptions, product cards, standard content.
Generation 3: LLM Translation
Large Language Models (GPT-4, Claude, Gemini) are not specialized translators, but their transformer architecture provides a qualitatively different result for complex texts.
What LLMs do better:
- Marketing texts. “Try a free demo” in German is not a literal translation, but a phrasing that sounds like a call to action for a native speaker. LLM understands the task, not just translates words.
- Cultural adaptation. Address, tone, level of formality — different languages have different norms. LLM adapts to the target culture.
- SEO text. Keywords in different languages are not a literal translation. LLM can organically embed the necessary queries.
- Brand context. You can convey tone of voice, terminology, forbidden formulations — and LLM will take them into account.
Where LLM is redundant: technical specifications, standard descriptions, repetitive content — NMT provides sufficient quality faster and cheaper there.
Practical significance for the website
- Product cards, technical descriptions: NMT is sufficient
- Marketing texts, headlines, CTAs: LLM or mandatory proofreading by a native speaker
- Legal texts, privacy policy: professional translation only
- SEO Content: LLM considering search queries and structure
- Blog and Articles: LLM + editorial refinement
Why This Is Important When Choosing a Tool
Many automatic website translation tools use Google Translate API or DeepL — these are NMTs, and they are quite sufficient for basic content. If the tool description simply states "AI translation" without further clarification, it's usually the same NMT.
The difference appears where the result matters: marketing texts, CTAs, unique descriptions. This is where the LLM approach provides a tangible advantage.