Remember how, in the past, translators would produce such absurd results that memes were written themselves? Like a product instruction from China advising to "iron the cat against the wool for activation." It was funny, but when you need to urgently translate an important document or explain to a foreign client why you missed a deadline — it's no longer a joke. Fortunately, times have changed. Modern neural translation networks are a completely different level. I decided to test seven popular services and see which ones truly handle complex tasks.

For the test, I prepared two tricky challenges. The first is a text filled with idioms and color codes (feeling blue, red tape, in the red). The second is a classic syntactic paradox about how time flies like an arrow, and fruit flies love bananas. This isn't just a test for a fool; these are real traps that even advanced algorithms fall into.

Let's start with BotHub. Under the hood, it runs Google’s Gemini 3 Pro. Google has indexed the entire internet for decades and knows more about languages than anyone. In the first task, Gemini handled it perfectly — correctly identified all idioms, translated 'feeling blue' as sadness, and 'red tape' as bureaucracy. In the second test, the model didn't fall into the trap with the word 'flies,' but lost the wordplay of the original. Technically a pass, but not impressive.

DeepL from Cologne, Germany — a specialized neural translation service that has built a good reputation since 2017. It has many features: works with documents, supports 35 languages, recently added Speech-to-Text. In the first task, DeepL chose a more conversational style, translating 'in the red' as 'in the minus' — very accurately capturing spoken language. But in the second task, it completely botched it — didn't recognize that 'fruit flies' is a fixed term for insects, and produced nonsense about flying fruits. It shows that it’s still a statistical translator that sometimes picks the most frequent option.

GigaChat from Sberbank runs on the NeONKA architecture and combines several neural networks at once. Rich functionality — you can feed it a document, table, presentation, even an hour-long audio file. In the first test, GigaChat was the closest to the truth — translated 'feeling blue' as 'depressed,' which better conveys the emotional nuance. In the second task, it also avoided the trap and correctly identified the flies. Impressive result for a universal model.

Microsoft Translator from Bing — a workhorse integrated everywhere: in Edge, Skype, Word. Knows 179 languages, including Klingon for Star Trek fans. Handled both tasks correctly — properly deciphered idioms and didn't fall for the syntactic trap. It’s clear that powerful algorithms back it.

MachineTranslation — an aggregator that sends your text immediately to Google, DeepL, Amazon, Microsoft, and ChatGPT, then shows all options side by side. Brilliant if you don’t know which is correct. Supports over 270 languages. Minus — in free mode, only 100 words at a time. In both tests, it produced safe, averaged options that anyone can understand.

Reverso works like a search engine for translations — it searches a database of subtitles, UN documents, and instructions. The interface is stuck in 2015, and the free version is stingy — only 2000 characters at a time. In the first task, it was a complete failure — translated 'red tape' as 'bureaucratic ribbon' (imagine a boss with scissors?). In the second, it made a dumber mistake than anyone — translated 'like' as a comparison, not as a verb. That’s the level of Google Translate from 2010.

Yandex Alice now runs on YandexGPT and has entered the generative model race. It’s everywhere — on Yandex’s main page, in browsers, in apps. Understands Russian nuances well because it was trained on Russian-language internet. In the first task, it produced one of the most literary options — adapted 'feeling blue' as 'in a depressed state,' and 'red tape' as 'getting rid of bureaucratic hurdles.' A real classic. In the second test, it also didn’t make mistakes and even translated 'like' as 'adore' — cool.

In conclusion, the best neural translation services are GigaChat, Alice, and Microsoft Translator. They truly understand context and don’t fall into syntactic traps. DeepL is good for documents but sometimes stumbles. Reverso is better not to use for serious tasks.

But remember the main point: neural networks are assistants, nothing more. They make mistakes, invent, and sometimes surprise you in the wrong way. Trust, but verify. Which neural translation service do you use? Share your experience in the comments!