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Lionbridge Machine Translation Tracker

Introducing our assessment tool to help you choose the best Machine Translation engine for your needs.


Comparing Four Machine Translation Deployment Strategies

Machine Translation (MT) has been around for decades. In recent years, it has evolved at an exponential rate. As companies produce ever-growing amounts of content in different languages, they have come to see MT as an opportunity to extend their reach in an increasingly globalized world. 

Companies seeking to deploy MT can explore the following four basic strategies. 

Public MT

This strategy includes services like Google Translator or Bing Translate. Such services are readily available at no cost. However, the engines are completely unsecure and not trained for specific domains or particular use cases. 

On-Premise MT

This strategy requires a company to deploy an MT server in its own IT environment. While this is the most secure option, it comes at a significant cost, is complex to deploy and manage, and requires ongoing maintenance. Importantly, this strategy often produces suboptimal MT output across multiple language pairs or content types.

Cloud MT

This strategy works like Public MT, is also hosted in the cloud, but creates a company-dedicated instance. Any data shared with the service is tightly secured and not shared with third parties. It provides additional capabilities around terminology customization and has other benefits. However, it can result in vendor lock-in and less-than-optimal MT quality across multiple language pairs.

Best of Breed MT

This is a single platform that allows companies to leverage multiple MT engines. It provides a single layer of terminology customization, an easy-to-manage interface and the ability to choose the best engines for different language pairs, industries or domains, and types of content. 

No matter which strategy you are contemplating, selecting the right engine may be challenging without the proper data and MT experience. Lionbridge is an expert in MT. In addition to having more than two decades of MT experience, we have gathered a large volume of linguistic and quality data about MT technology that will help you make the right choice. This webpage provides basic information about the performance of popular MT engines for the most common language pairs to help you select the best option based on your content. 


Which Machine Translation (MT) engine is best? There’s no simple answer.

When choosing among the many available MT systems, it’s important to note some engines address a specific function or domain. If your needs don’t align with that purpose, the engine may perform sub-optimally no matter how advanced it is. To determine the best option, first, identify why you are using MT.

If you want an MT engine for general use, it may be appropriate to use Google Translate or Bing Translator. If you seek MT services for a specific language or domain, you may achieve better results by turning to Amazon Translate or DeepL Translator.

Lionbridge’s Machine Translation Tracker analyzes engine performance monthly to help you figure out the best MT engine depending on the language pairs you use. The next time you ask which MT engine is best, reframe the question to, “Which MT engine is best for me?” And count on Lionbridge for guidance.

Want to learn more about the different types of MT technologies? Check out our blog Machine Translation in Translation.

Lionbridge Expert's Commentary

November 2022

We’ve seen a nice overall improvement in Microsoft’s Machine Translation (MT) results during October 11-November 1. With this recent quality increase by Bing Translator, the main MT engines are producing very similar results. As such, they face a tight battle for the top leadership position.

The major MT engines have not shown interesting improvements for months. Let’s hope this development from Microsoft breaks that trend and is the start of forthcoming progress by these engines. 

We went beyond our usual measure of single-reference translations and confirmed the Microsoft improvement results with a second tracking that encompassed multiple references. In this MT evaluation, we used 10 reference translations completed by humans — the gold standard — instead of just one translation to get a more precise Edit Distance metric that considers multiple possible correct translations in the final results.

As we reach the end of the year, we note that 2022 has had very flat MT results. We observed little change; this Microsoft Bing MT development may be the most notable advancement of the whole year. As commented on earlier in the year, the current MT paradigm may be reaching a plateau. We look forward to seeing what 2023 holds for Machine Translation.

—Rafa Moral, Lionbridge Vice President, Innovation

Click here to read previous expert commentaries.

Evaluating Overall MT Performance
Per Language Pair Quality
Choose between German, Spanish, Russian, or Chinese in the drop-down menu
Per Domain Performance
Select Domain/Subject Matter in the drop-down menu

For more insights and future trends about Machine Translation, read our Future of Language Tech blog post – Future of Machine Translation.

Lionbridge Machine Translation Tracker Methodology

Lionbridge uses inverse edit distance as a scoring method. The edit distance measures the number of characters (for Asian languages) or words (for Western languages) that need to be changed by a human post-editor before it achieves the quality level produced by a human translator. The higher the metric, the better the quality.

Out of the four MT engines we assessed, Google and Bing NMT performed best across different language pairs and for general content. However, specialized engines performed best for certain language combinations. For instance, DeepL had the strongest performance in German, and Amazon translated Chinese best.


  1. Machine Translation engines in this report are assessed monthly by Lionbridge.
  2. The data provided is for illustration purposes and each case should be treated and assessed individually.
  3. This report is generated based on source data preselected by Lionbridge Machine Translation teams. The same source data is submitted to every Machine Translation engine and language pair each time, making comparisons between translation engines possible.
  4. No customer data has been used in the generation of the report.

Get Smaⁱrter™ About MT

Smaⁱrt MT™: Machine Translation for the Digital Age

Find out how to leverage MT to offer digital experiences in local languages, achieve better customer satisfaction ratings, and excel in global markets.

The Future of Language Technology: The Future of Machine Translation

Machine translation will continue to evolve and become increasingly important for translation productivity if it is deployed properly.

Machine Translation vs. Machine Translation Plus Post-Editing

When is it best to rely on Machine Translation? When should you consider a hybrid model that incorporates traditional, human translation? We go through the scenarios. 

Neural Machine Translation: How Artificial Intelligence Works When Translating Language

Delve into what Neural Machine Translation is and why it is considered a game changer for the language industry. 

Machine Translation in Translation

This handy cheat sheet will bring you up to speed on the most important terms associated with Machine Translation. 

Click the image below to view key definitions for understanding Machine Translation 

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