DeepL and ChatGPT will both give you a good translation of the same sentence, and the two results will not match. That is not a fault in either one. They are built on different ideas of what translating is.
DeepL moves your sentence into another language and leaves everything else alone. ChatGPT reads it, works out what you were going for, and writes what a person in that language might have written instead.
Which one you want depends on whether your source text is something to preserve or something to improve.
What DeepL is built to do
DeepL translates. That is the whole product, and the focus shows.
It covers 100+ languages, with a core of roughly 33 that get full feature support. On European pairs it is still the one to beat. It handles documents without wrecking the formatting, so a Word file comes back a Word file. Its glossaries hold your terms steady across a project, and they inflect properly instead of running search and replace, so your term survives the case system in Polish.
There is a formality toggle for ten languages, which decides whether a German reader gets du or Sie, a French one tu or vous.
The quiet feature is the discipline. DeepL will not add a flourish, smooth over a clumsy line, or decide your third paragraph was redundant. What you put in comes back, sentence for sentence. For a contract, a medical form, or anything where a helpful edit is a liability, that restraint is what you are paying for.
What ChatGPT does that DeepL doesn't
You can argue with ChatGPT.
That sounds small. It is the whole difference. You can tell it the reader is your boss's boss, that the joke in line two has to survive, that the last version sounded like a textbook. It will give you three versions and explain the trade in each. Ask why it chose a word and you get a reason instead of a shrug.
It also holds a whole document at once. A callback in paragraph nine to a phrase in paragraph one is something it can catch. A sentence-by-sentence engine cannot.
And it is better at explaining than at translating. Take the German doch. It contradicts a negative, it insists, it softens, it nudges, and English kept no single word for any of it. DeepL has to pick something and move on. ChatGPT can tell you what the speaker was doing, which is often what you actually needed.
Where each one breaks
DeepL fails quietly. It returns a confident sentence whether or not it had any business being confident, and it will flatten a culture-bound word into the nearest plain equivalent without noting the loss. Its formality switch has two settings. Real register has many more.
ChatGPT fails in the other direction. Because it is writing rather than converting, it can embellish, paraphrase, or quietly drop a clause it judged unimportant. Run the same paragraph twice and you get two different translations. In marketing copy that is a feature. In a clinical summary it is a defect.
The honest split: DeepL for European pairs and anything where the source text is the law, ChatGPT for tone you need to shape, for Asian languages where DeepL's depth thins out, and for the times you need the translation explained rather than just delivered.
The question neither one answers
Both hand you a sentence. Neither tells you where it lands.
A translation can be correct in the dictionary and wrong in the room.
Ask either tool for the Spanish for catching a bus and you may well get coger. In Madrid that is the ordinary, unremarkable verb. Across much of Latin America it is crude, and you have just said something you did not mean to a room that will not forget it. Both tools are technically right. Neither asks where your reader lives.
The gap runs the other way too. The Dutch gezellig comes back as "cozy", and cozy is a room. Gezellig can be a room, an evening, a visit, or a person, and what it describes is warmth between people rather than temperature. You get a word that fits the slot and misses the point.
What Linguin adds
Linguin works on the part both tools skip: whether the phrasing is real, and what the word actually carries.
Translation runs in two passes. The first drafts the sentence. The second takes that draft's own wording back out to the live language and looks for it in how people write today. Wording that turns up stays. Wording that does not gets rewritten before it reaches you. The check is on the phrasing, not on the grammar. Builders get the same pipeline through the API.
The dictionary side covers the rest. Look a word up and you get its weight: how formal it is, who says it, where it would be fine and where it would not, in your own language across 120+ languages. The coger problem is a line in that entry rather than something you learn from the faces at the table.
The room the sentence lands in
So: DeepL if the source text is sacred, ChatGPT if the tone is. Run anything important through both and keep whichever reads better.
Then answer the question neither of them touched. Not whether the sentence is correct, but whether anyone would say it. Look it up instead of hoping.