If you've used Google Translate or DeepL in the past few years, you've been using Neural Machine Translation (NMT), a technology that replaced the older statistical and rule-based systems around 2016-2017. NMT was a massive leap forward. Translations became more fluent, more natural, and far more usable.
But a new shift is happening. Large Language Models (LLMs), the same technology behind ChatGPT and Claude, are now being applied to translation. And they're producing results that NMT engines simply can't match in many scenarios.
This isn't hype. It's a fundamental architectural difference. Let's break down why LLMs translate better, where NMT still has advantages, and what this means for developers and businesses that rely on translation APIs.
How NMT Works
Neural Machine Translation uses an encoder-decoder architecture. The encoder reads the source sentence and compresses it into a fixed-size vector representation. The decoder then generates the target sentence from that vector, one token at a time.
Modern NMT systems (Google Translate, DeepL, Amazon Translate) use the Transformer architecture, the same foundation that LLMs are built on. But there's a critical difference: NMT models are trained exclusively on parallel sentence pairs.
This means an NMT model sees millions of examples like:
English: "The meeting has been rescheduled to next Thursday."
Dutch: "De vergadering is verplaatst naar aanstaande donderdag."
It learns to map patterns between languages at the sentence level. This works well for straightforward text, but it has fundamental limitations.
How LLM Translation Works
LLMs take a completely different approach. Instead of being trained only on parallel text, they're trained on trillions of tokens across hundreds of languages: books, articles, code, conversations, documentation, legal texts, creative writing.
When an LLM translates, it doesn't just map sentence patterns. It understands the meaning of the text, the context it appears in, and the conventions of the target language. Translation is framed as a task within a much broader capability.
Think of it this way: an NMT model is a specialist translator who has only ever read bilingual dictionaries. An LLM is a multilingual person who has read widely in both languages and understands culture, idiom, register, and context.
Where LLMs Beat NMT: Real Examples
1. Register and Formality
One of the most common complaints about NMT is that it gets the register wrong. Dutch, German, French, and many other languages distinguish between formal and informal address (u/jij, Sie/du, vous/tu). NMT engines often pick the wrong one because they lack context.
| Source (EN) | Google Translate (NMT) | Langbly |
|---|---|---|
| "Click here to update your preferences" | "Klik hier om uw voorkeuren bij te werken" | "Klik hier om je voorkeuren bij te werken" |
The NMT version uses "uw" (formal), which sounds stiff for a website UI. The Langbly version uses "je" (informal), which is standard for modern web interfaces in Dutch. Advanced AI translation understands that UI copy calls for informal register.
2. Idioms and Natural Phrasing
NMT tends to translate literally when it encounters phrases that don't have a direct word-for-word equivalent. LLMs generate translations that sound like a native speaker wrote them.
| Source (EN) | Google Translate (NMT) | Langbly |
|---|---|---|
| "We've got you covered" | "Wij hebben u gedekt" | "Wij staan voor je klaar" |
| "It's a no-brainer" | "Het is een no-brainer" | "Het is een makkelijke keuze" |
"Wij hebben u gedekt" is a literal translation that makes no sense in Dutch. "Wij staan voor je klaar" is what a native speaker would actually say. Similarly, "no-brainer" isn't a Dutch expression, so an LLM recognizes this and translates the meaning, not the words.
3. Technical Context
When translating technical documentation, the same English word can have very different translations depending on context. "Table" in a database context vs. a furniture context. "Commit" in Git vs. everyday language. "Cell" in biology vs. spreadsheets.
NMT models have limited context windows and no way to receive domain hints. LLMs can be prompted with context: "This is a software documentation page about version control", and they'll consistently choose the right technical terms.
4. Marketing Copy and Tone
Marketing copy is where the difference is most dramatic. NMT produces translations that are technically correct but feel lifeless. LLMs maintain the energy, persuasiveness, and tone of the original.
| Source (EN) | NMT Output | LLM Output |
|---|---|---|
| "Start your free trial, no credit card required" | "Start uw gratis proefperiode, geen creditcard vereist" | "Probeer het gratis, geen creditcard nodig" |
The LLM version is more concise, uses informal register appropriate for a CTA, and reads naturally. The NMT version is stilted and overly formal.
5. Locale-Specific Formatting
LLMs can be instructed to apply locale-specific formatting rules automatically. For Dutch, this means converting decimal points to commas (1,200.50 → 1.200,50), using 24-hour time, and applying local date formats. NMT engines never do this; they preserve the source formatting regardless of the target locale.
Where NMT Still Wins
It wouldn't be fair to pretend LLMs are better in every dimension. NMT has real advantages:
- Speed: NMT models translate in 50-200ms. LLM translation typically takes 500ms-3s depending on text length. For real-time applications like chat translation, NMT is faster.
- Cost at extreme scale: NMT inference is cheaper per character at very high volumes (billions of characters/month). For most businesses, this difference is negligible, but for companies processing Wikipedia-scale text, it matters.
- Predictability: NMT output is deterministic. Given the same input, you always get the same output. LLMs have a small degree of variability (though this can be minimized with temperature=0).
- Rare language pairs: For some low-resource language pairs, NMT models trained on specialized parallel corpora may still outperform general-purpose LLMs that have less training data in those languages.
The Quality Gap is Widening
The most important trend to understand: LLM translation quality is improving faster than NMT quality.
NMT models have been largely plateauing. Google Translate's quality in 2026 isn't dramatically different from 2023. The architecture is mature, and improvements are incremental.
LLMs, on the other hand, are on a steep improvement curve. Each new model generation (GPT-4 → GPT-4o → GPT-o1, Claude 3 → Claude 3.5 → Claude 4) brings noticeable quality improvements across all tasks, including translation. The best LLMs in 2026 produce translations that were impossible just two years ago.
This means the quality advantage of LLM-based translation is growing, not shrinking.
What This Means for Developers
If you're building a product that uses machine translation, whether it's localizing your UI, translating user content, or building a multilingual feature, you have a choice to make:
- Stick with NMT (Google Translate, DeepL) for maximum speed and lowest cost at extreme volumes.
- Switch to LLM-based translation for higher quality output, especially in customer-facing text, marketing, and complex content.
For most SaaS companies, e-commerce platforms, and content businesses, the quality improvement of LLM translation is worth the trade-off. Your users notice bad translations, and they erode trust.
How Langbly Approaches This
Langbly is an advanced, context-aware translation API that's drop-in compatible with the Google Translate v2 API. It leverages the latest advances in AI translation technology. If you're currently using Google Translate, you can switch by changing one line (the base URL) and immediately get higher-quality translations. See the setup guide to get started in minutes.
We've optimized specifically for translation quality: language-specific prompt engineering, locale-aware formatting, placeholder preservation, and HTML tag handling. Our eval system tests 160+ cases across 38 categories to catch regressions before they reach production.
The result: translations that read like they were written by a native speaker, at a price point that's 81-90% lower than Google Translate.
Related Articles
- How to Cut Your Translation API Costs by 90% Without Sacrificing Quality. A detailed pricing breakdown comparing Langbly, Google Translate, and DeepL with real monthly cost scenarios.
- Google Translate API for Developers: A Complete Guide (2026). Setup instructions, code examples, and when to consider alternatives to Google Translate.