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

March 2022

If you’ve been following these pages, you’re familiar with our generic MT comparative evaluations. Each month, we identify which MT engines are performing best for given language pairs and track engine improvements. In March, the performance of the different MT engines was flat. It’s a trend we’ve been noticing for some time already. As we commented last month, it may indicate that a new MT paradigm is needed.

While we share generic results, companies are increasingly pursuing custom MT comparative evaluations. Unlike the generic version, these evaluations take a company’s specific needs into consideration when determining the most advantageous MT engines.

When a company wants to start using MT or improve the way it currently uses MT, it is critical to identify which MT engines will work best. When we execute custom evaluations, we take a similar approach to the one demonstrated on this page, but we make recommendations based on a company’s content type and language pair requirements.

While custom MT comparative evaluations have been available for years, there’s greater demand for them. We attribute this trend to the important role MT plays in helping companies succeed in a digital marketplace.

—Rafa Moral, Lionbridge Vice President, Innovation

Click here to read previous expert commentaries.

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

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.

Contact Us