Best AI if English is not your first language
Five jobs you do in a second language, and a two-minute test that shows you which AI model actually handles your own.
The difficulty with answering "which AI is best in my language" is finding a test for your language, at your register, on the kind of thing you actually write. Public benchmarks lean heavily on English. The ones that reach beyond it mostly test translation of clean sentences, which is a different job from making your cover letter sound like a person wrote it.
So this guide runs the other way round. Five jobs, in roughly the order they come up. Each one gets the same four things: what goes wrong, the move that fixes it, a test you can run on your own text in about two minutes, and a verdict. MultiChats carries 60 models from 18 providers, 21 of them open on a free account, which is what makes the testing easy. One paragraph, three models, one sitting.
Test a model in your own language before you trust it
Take a paragraph you already wrote, in your language, that you cared about when you wrote it. Real text. Textbook sentences expose nothing, because everything handles textbook sentences.
Give three different models the same instruction: rewrite this so a native reader would not notice a non-native wrote it, then list every change and the reason for it.
Read the list before you read the rewrite. That is where the difference shows. A model with a real grip on the language gives reasons that are about the language: this preposition after that verb, this word is regional, that phrasing sits a register too high for a message to a colleague. A model that is faking it hands back a clean rewrite with no reasons, or reasons that are English grammar wearing your language's clothes.
Do all three in one conversation. You can change the model part way through a thread and put the same request to the next one without retyping it.
Writing something professional in a language you did not grow up in
You know what you mean. What comes out lands one notch too formal or one notch too casual, and the notch is exactly what you cannot hear. AI makes this worse before it makes it better, because the default register you get back tends to be formal, polite and a little stiff. Ask for a quick note to a colleague and you can get back something that reads like a letter from a bank.
The move. Name the register and name the reader inside the prompt. "A message to a colleague I have worked with for two years, friendly, no formal greeting" produces different text from "make this professional". Then add the constraint that matters: keep my sentence structure, change only what a native speaker would flag.
Two-minute test. Send the same paragraph twice, once with the reader named and once without. If the two replies come back nearly identical, that model is not acting on your register instruction, and rephrasing the prompt will not change it. Try the next model.
Verdict. State the reader and how close you are to them. A model guesses register badly, and it never tells you it was guessing.
Does my draft sound natural, or does it sound translated?
Translationese is hard to self-diagnose. The sentences are grammatical, individually fine, and collectively off in a way you can hear in someone else's writing and not your own. Worse, a model asked "does this sound natural?" will usually say yes, because that question has an easy answer and agreeing is the easy answer.
The move. Take the judgement call away from it. Ask for evidence instead: mark every phrase a native speaker would not have chosen, quote it, and give the version they would have used. If it finds nothing in a real second-language draft, push once with "find at least five" before you believe the nothing.
Two-minute test. Paste a paragraph written by a native speaker, lifted from a newspaper in your language, and ask the same question. If the model still finds errors in it, its judgement on your own draft is worth nothing either.
Verdict. Make it quote and replace. Yes-or-no questions about naturalness get yes-or-no answers with nothing underneath them.
When you read a language slower than you speak it
A contract, a letter from a landlord, a form from a school. You could decode it given an hour, and the hour is what you do not have. The real risk sits in how a summary reads. It reads as complete whether or not it quietly dropped the one clause that costs money.
The move. Ask for structure before meaning: sections and headings first, then a short summary of each in your stronger language, then the specific question you actually came with. Attach the file rather than pasting text. 25 of the 60 models take a PDF directly and 47 accept an image, and the app will not let you attach a PDF to a model that cannot take one. A photograph of paper carries no text layer for anything to extract, so results there vary more.
Two-minute test. Ask it to quote, verbatim and in the original language, the sentence its summary rests on. Then find that sentence in the document yourself. If you cannot locate it, treat the summary as decoration.
Verdict. A summary becomes usable at the moment it is anchored to quoted lines you can find in the original.
Translate it, then check the translation
One model, one pass, no check. Fluent output reads as correct output, and that is the whole trap: a mistranslation arrives in confident, well-formed sentences with no marker on the spot where it went wrong.
The move. Translate on one model. Then open the same conversation on a different model, give it only the translation, and ask for it back in your original language. Compare that against what you wrote. Whatever survives the round trip is safe. Whatever shifted is where to look. Give the model the context too, who is reading and what the text is for, because a form of address or a job title can be right in the dictionary and wrong in the letter.
Two-minute test. Round-trip one sentence you already know is difficult, an idiom or a legal term. If it returns as a literal word-for-word rendering, that model is moving words rather than meaning, and you have learned something about the rest of the document.
Verdict. Two models and a round trip catch the confident errors that one model and one pass will never show you.
One conversation, two languages
You write in your language and the reply comes back in English. Or you drop in one English technical term and the model quietly switches for good. Most people patch this by retyping "in French, please" every few messages for the rest of their lives.
The move. Put the rule somewhere permanent. In the web app, Settings, then Account, the box labelled "Personal preferences" holds up to 5,000 characters and applies to every new chat. Something like: "Reply in French unless I write to you in English. Correct my French mistakes at the end of your reply, not inside it." On mobile the same box sits in the personalization sheet under "Anything else the AI should know". This one needs no paid plan. Memories, which are a separate feature, do sit on Pro.
Two-minute test. Save the instruction, open a brand new chat, and write one line in your language. If the answer arrives in English, the instruction is not reaching that model, and it is worth trying the same chat on another one.
Verdict. A standing instruction takes a minute once and deletes the sentence you have retyped a hundred times.
Where it still falls down
Register flattens. Left alone, output drifts formal. Rephrasing the request more carefully does not remove it, which is why naming the reader every time is in the list above rather than mentioned in passing.
Idiom goes two ways, both bad. Either a literal rendering that means nothing in the target language, or a substitution with the blandest available equivalent, which is safe and dead.
English is where the polish went. Training data and public evaluation both concentrate there, which is a fact about the industry rather than a measurement of your language. Nobody has published the gradient, so treat structural distance from English, in case marking, word order, or politeness encoded in the grammar itself, as a reason to check harder rather than as a prediction about what you will find.
It will not flag its own doubt. There is no wobble in the output when it is unsure. The uncertain sentence and the certain one arrive in the same confident voice, which is precisely why every entry above ends in something you can verify rather than something you can feel.
Questions people ask
Is MultiChats itself available in my language?
The interface is in English. The models are not limited to it: write in your language and the answers come back in your language, and the standing instruction above makes that the default rather than something you request each time.
Which single model should I pick for my language?
No honest single answer exists. Nobody has published a per-language ranking covering the 60 models we carry, and an invented one would be worse than no answer at all. The lab's home language is a reasonable opening guess: Mistral is a French lab and six of its models are here, while Qwen, GLM, Kimi, and the models from DeepSeek, MiniMax, Tencent and StepFun come from labs based in China. Treat that as a guess and settle it with the paragraph exercise at the top. Running it takes less time than reading this answer twice.
Do I need a paid plan for any of this?
A free account reaches 21 of the 60 models with 50 messages every 24 hours, which covers rewriting, checking and round-trip translation. The reasoning effort control, worth having on a long document or a fine register question, sits on Pro, as do memories. On the web it appears once Intent Detection is off in Settings, under Tool Preferences; on by default, that setting picks the effort for you. Pricing is $20.99 a month or $199.99 a year for Pro, listed in full on the pricing section.
Is it safe to paste a private letter into a translation?
We do not train on your conversations. Some providers might, depending on which model you pick, and that varies by model rather than applying across the board. Models where we know of a difference carry a privacy notice on their info card. An empty card does not establish what the provider does with your conversations.