“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 SMT that people joked about needing to decipher the text after it.
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 over SMT:
- Words are not translated in isolation — the context of the entire sentence is considered
- Idioms and fixed expressions are handled more accurately
- The naturalness of the text is significantly higher
- Tone and style are better preserved
Google Translate switched to NMT in 2016. DeepL was initially 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 phrases — and LLM will take them into account.
Where LLM is redundant: technical specifications, standard descriptions, repetitive content — there NMT provides sufficient quality faster and cheaper.
Practical Value for a 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 NMT, and they are quite sufficient for basic content. If the tool description simply states “AI translation” without 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.
