Google Translate vs. DeepL: Which is the best translation tool for your business?

Have you ever found yourself abroad, struggling to communicate in a different language and turning to Google Translate for help? Chances are you have. With over 500 million users globally, Google Translate has been the gold standard for casual users, thanks to its accessibility and ease of use.

It wasn’t always this way. When Google Translate first launched, it used a basic word-for-word translation method. While straightforward, this approach often produced awkward results because it didn’t account for grammar or context. Over time, Google improved its system by introducing more advanced technologies like neural networks, which process entire sentences rather than individual words.

In 2016, Google made significant advancements with its Neural Machine Translation (GNMT) system. This AI-powered technology uses multiple layers to analyse the meaning and structure of sentences, making translations more accurate and natural. The system also includes an attention mechanism that helps focus on keywords to maintain context and flow.

Today, Google Translate is a highly advanced tool used widely by businesses for quick, cost-free translations. But this reliance comes with trade-offs. Like any AI-powered tool, Google Translate can sometimes miss nuances, produce inaccuracies, or mis-interpret tone which can affect the clarity and precision of communication.

Even with these drawbacks, Google Translate remains a widely used option due to its ease of use and accessibility. However, there is a new kid on the ‘machine-generated translation’ block set to challenge its position.

How DeepL Came to Challenge Google Translate

DeepL, launched in 2017, has become known for its greater accuracy and more natural fluency, especially when handling complex texts. Its focus on delivering contextually accurate and polished translations has made it a standout in machine translation.

While Google Translate has made significant improvements, it can still produce translations that miss linguistic or cultural nuances. In contrast, DeepL uses advanced neural network technology to deliver results that many users and linguists find “closer to those of a human translator”.

Unlike Google Translate, which supports over 130 languages and caters to a wide range of needs, DeepL focuses on a smaller set of languages, with particular strength in European ones. This more targeted approach prioritises translation quality, though its performance may be less reliable for languages outside its core focus.

That said, like any AI-powered tool, DeepL is not without its flaws. It can still make mistakes, including mistranslations and even “AI hallucinations,” where the system inserts words or meanings that aren’t present in the original text. These hallucinations can alter the meaning of the text, highlighting the importance of human review, especially for important or sensitive content.

Google Translate’s Push for Inclusivity with 110 New Languages

However, Google Translate is not resting on its laurels. With the integration of its PaLM 2 large language model, Google Translate is undergoing its most extensive expansion yet, rolling out 110 new languages to its platform.

 This expansion reflects Google’s effort to make translation more accessible, even for lesser-known languages. Some of the highlights include:

• Afar: A tonal language spoken in Djibouti, Eritrea, and Ethiopia, showing Google’s focus on underrepresented communities.
• Cantonese: One of the most requested languages, now available to serve millions of speakers worldwide.
• NKo: A standardised form of West African Manding languages, helping unify various dialects.
• Manx: The Celtic language of the Isle of Man, whose last native speaker passed in 1974, highlighting efforts to preserve endangered languages.
• Tok Pisin: An English-based creole and lingua franca of Papua New Guinea, broadening Google’s reach in the Pacific region.

This update demonstrates Google Translate’s ambition to remain the most accessible translation tool available. Its sheer breadth of language support and user-friendly approach make it a powerful choice for casual users and businesses looking for rapid solutions. So if the question was, Google Translate vs. DeepL, which is the most accessible? There wouldn’t be much competition. But as Google Translate focuses on reach and inclusivity, DeepL’s focus is elsewhere.

Google Translate vs DeepL: Which comes out on top?

Depth vs. Breadth

While Google Translate strives to support as many languages as possible, now including many low-resource community languages, DeepL remains focused on refining the quality of its translations. Its neural network technology aims to reflect not just the meaning but also the linguistic nuances of the target language. 

Speed vs. Precision

Google Translate’s edge lies in its speed and versatility—users can instantly translate text, webpages, and even entire documents in seconds. Meanwhile, DeepL’s strength lies in precision. This makes it a better option for content that requires greater attention to detail or a more polished result.

Convenience vs. Quality

Google Translate’s free and accessible platform makes it a popular choice for everyday translation needs. DeepL also offers a free service, as well as a paid premium service with higher-quality translations and features like customisable terminology and tone, making it suitable for more specific or professional requirements.

The Limitations of AI in Translation

So, the question arises: will Google Translate’s extensive language support and accessibility keep it ahead of the curve, or will DeepL’s focus on translation quality and contextual accuracy appeal more to professionals and businesses seeking polished results? It’s important to remember that both these tools, being AI-powered, have their limitations and may not always provide perfect translations.

This question of Google Translate vs. DeepL ultimately comes down to user priorities. For casual users, Google Translate’s broad language coverage and straightforward interface make it a great option. It’s ideal for quick translations, whether for travel or general business communication, where speed and convenience are important. The addition of 110 new languages, including low-resource community languages,  further strengthens Google’s reputation as an inclusive and widely accessible tool. However, this focus on covering more languages can sometimes result in less precise translations, which may not suit industries where accuracy is critical.

DeepL, on the other hand, prioritises quality over quantity. Its translations are often more accurate and better at capturing the nuances of language. Features such as customisable tone and terminology make it particularly appealing for professional use. However, its limited language options might not work for everyone, especially those needing translations in community languages. In the UK, this can include public services and charities. 

That said, no machine translation tool is flawless. Both Google Translate and DeepL rely on AI, which can lead to occasional errors, especially with complex or nuanced content. These tools can struggle with idiomatic expressions, cultural references, or highly technical language. Therefore, it’s crucial to review their output carefully and consider the value of human expertise in translation.

Why Human Expertise Matters in the Google Translation vs. DeepL Debate

At AST Language Services, we value the role technology plays in translation. We’ve long leveraged tools like translation memory and CAT software to help ensure consistency and efficiency. Now, we can also use AI to assist us. However, we also believe that human translators are essential for achieving the accuracy, cultural understanding, and appropriate tone, elements that AI alone cannot provide. By combining advanced technology with human expertise at the post-editing stage, we can deliver reliable, cost-effective results while avoiding the potential pitfalls of relying solely on machine translation. 

In the end, who comes out on top of Google Translate vs. DeepL will depend on the user’s specific needs. However, for businesses and professionals looking for dependable, cost-effective translations, combining AI-technology with the expertise of human translators is often the best way forward. Whilst businesses seeking translations tailored to their brand’s tone, target audience and terminology, should opt for human-generated translation.