Best AI for translation

As of 2026-10-13, Google: Gemini 3.5 Flash Lite is the strongest model for this task in our daily benchmark, where over 350 AI models are evaluated and the best are presented.

Translation is where the gap between models is widest for Scandinavian languages. A model can write excellent English and still produce stiff, English-flavoured Norwegian with the wrong word order and obvious anglicisms.

For translation we rank by how well the model actually lands in the target language. Ranked by language score, from today’s benchmark.

Runner-up
IBM: Granite 4.0 Micro
ibm-granite
Score: 7.3/10
Language: 9.7/10
Speed: 326 t/s
$0.02
Third choice
Cohere: Command R+ (08-2024)
cohere
Score: 8.3/10
Language: 9.3/10
Speed: 276 t/s
$2.50

Today’s ranking

ModelProviderScoreLanguageSpeedInput price/1M
Google: Gemini 3.5 Flash Litegoogle8.7/109.7/10178 t/s$0.30
IBM: Granite 4.0 Microibm-granite7.3/109.7/10326 t/s$0.02
Cohere: Command R+ (08-2024)cohere8.3/109.3/10276 t/s$2.50
Cohere: Command R7B (12-2024)cohere6.9/109.3/10405 t/s$0.04
Anthropic: Claude Opus 5.5anthropic8.3/109.0/10266 t/s$4.00
Anthropic: Claude Sonnet 5.5anthropic8.0/109.0/10200 t/s$2.00
DeepSeek: DeepSeek V4 Flash 0731deepseek8.1/108.7/10452 t/s$0.02
Meta: Llama 4 Maverickmeta-llama7.5/108.7/10111 t/s$0.19

What separates a good translator from a bad one?

Weak models translate word by word. The result is grammatically correct but instantly recognisable as machine output: English sentence structure, wrong prepositions and phrases no native speaker would use. Strong models rewrite the sentence the way a native writer would, and hit the right register — formal in a letter, conversational in a chat.

When should you not use AI for translation?

Legal agreements, medical information and marketing copy intended for publication should be reviewed by a human. AI is excellent for first drafts, internal communication and understanding text in a language you do not read — but it can be confidently wrong, and the error is hard to spot precisely because the text reads well.

hvilkenAI is independent. We have no affiliate agreements, sponsored placements or commercial ties to the AI providers we test. Every recommendation rests on test results alone.

Frequently asked questions

Which AI is best at translating into Norwegian?
It changes from month to month. The ranking on this page comes from today’s benchmark, where we evaluate over 350 models and present the ones that perform strongest on the language.
Is AI better than Google Translate?
For running text the best language models are clearly better, because they account for context, tone and register. Google Translate remains fast and practical for single words and short sentences.
Can I translate sensitive documents with AI?
Not without checking the provider’s data processing terms. Personal data and trade secrets should only go to services with an approved data processing agreement.
Why are Scandinavian languages harder than English for AI?
Models are trained on far more English text. Norwegian, Swedish and Danish are small languages in the training data, which is exactly why the differences between models show up so clearly here.

Other tasks

Doing something else? See which model is best for it.

→ Other tasks

Read also

← Back to home

How to get more value from this recommendation

Use this recommendation as a practical decision aid, not as an advertising list. HvilkenAI is built to make AI selection easier when the market changes quickly and model names alone say little about real quality.

Start with the task you actually need to solve. A model that is good at short chat is not automatically best for long documents, code, customer-facing text, research or work involving sensitive information. Consider language quality, price, speed, stability and how much editing the answer requires before choosing a subscription or API.

We evaluate over 350 AI models and present the most useful signals in a format that works for everyday users, students, businesses and developers. The exact calculation is proprietary, but the assessment is based on standardised tasks, updated benchmark data and editorial review of whether the recommendations make sense in practical use.

For important decisions, run a small test of your own: use the same prompt, language and expected format in two or three relevant models. Look at precision, source handling, tone, speed and how easy the answer is to verify. That makes the benchmark a time-saving starting point, not a final answer you must follow blindly.

Use the links on the page as well. One page rarely gives the whole picture: the benchmark shows today’s numbers, the AI test translates your need into concrete recommendations, and the privacy/terms pages explain which data and limitations you should consider. Used together, these sources make the choice more robust than following a model name from social media.

If the result will be used at work, document why the tool was chosen: which task it solves, which alternatives were considered, which data should not be shared and how the answer will be quality-checked. That makes it easier to switch model later if price, quality or provider terms change.

When a page is included in the sitemap, it should also stand on its own as a useful explanation. That is why we emphasise practical choices, concrete control questions and clear limits for how data can be used.

The goal is that the reader can move on with a safer choice after a few minutes, without already knowing every model or provider.

Pay particular attention to whether the recommendation fits the language, price and risk in your own situation.

In short: choose what you can verify.

When free is enough

Choose free or low-cost first when the task is simple, risk is low and you can verify the answer yourself.

When paid is worth it

Paid models can be worth the cost when documents are long, precision matters or you save real working time.

When to wait

If the need is unclear, start with the test and benchmark before committing to a provider.