Translation productivity 2026: human translation vs MTPE

Machine translation with post-editing (MTPE) has become the dominant production method globally, with adoption surging from 26% in 2022 to 46% in 2024.¹ Yet fundamental questions persist: is post-editing genuinely more productive than human translation? Under what conditions do efficiency gains materialise, and how do they affect overall translation productivity?

Recent research reveals a nuanced picture. At SwissGlobal, we combine machine translation (MT) with expert post-editing to deliver measurable gains  while maintaining the quality and security our clients expect.

The data: what translation productivity gains look like

The most comprehensive study comes from Silvia Terribile at the University of Manchester, analysing 90 million words from 879 linguists across 11 language pairs.² Key findings include that post-editing is 66% faster than human translation on average, but with massive variation:

This variability means that it’s misleading to generalise average speed values. This is why we selectively deploy MTPE only for language pairs and content types where it delivers clear productivity gains, and choose human-only processes where they’re more efficient.

A 2025 Finnish study of 908 segments found post-editing GenAI output was 14% faster than translating from scratch, with individual variation ranging from -2% to +102%.³ again underscoring the need for project-specific decisions.

Translation productivity benchmarks

Translation productivity varies significantly based on the type of translation and the depth of post-editing required.

Full MTPE involves comprehensive editing of machine-translated content to achieve publishable quality, with thorough review of terminology, style, and accuracy for demanding texts.

Light MTPE applies to high-quality machine output that needs only minimal corrections for basic readability, typically used for internal documentation or high-volume repetitive content where perfect fluency is less critical.

The EU Translation Centre establishes clear daily output expectations for contracted linguists: 10 pages for human translation, 15 pages for full MTPE, and 20 pages for light MTPE (based on 1,500 characters per page).⁴

Industry practitioners report higher outputs, with experienced post-editors achieving 700 words/hour for full MTPE and 1,000 words/hour for light MTPE, translating to approximately 5,600–8,000 words daily.⁴

This difference in editing intensity directly determines daily output:

The economics of collaborative MTPE

When implemented with fair compensation models, MTPE creates measurable value for both clients and translation professionals. The European Commission’s DG TRAD exemplifies this balance, maintaining 90.3% satisfaction rates while processing record volumes through strategic MTPE deployment.⁵

Leading platforms have developed equitable frameworks where translation productivity gains are shared. Translated’s “Fair Rate” policy ensures that as MT quality improves and editing time decreases, translators process more words per hour and increase earnings, with the platform recognised by Fairwork for meeting fair pay and conditions standards.⁶,⁷

The key lies in moving beyond simplistic discount models: successful MTPE pricing typically sets post-editing rates at 70–85% of translation rates rather than 60%, with hourly rate models (averaging CHF 90/hour for direct client work) increasingly preferred to align compensation with actual cognitive effort.⁸

Cognitive challenges

Post-editing changes the nature of translation work, shifting effort from drafting to correction. Research shows post-editing reduces translation time by 63%, keystrokes by 59%, and pauses by 63% compared to human translation from scratch.⁹

However, modern MT introduces human-like errors, including fluent, plausible output with subtle inaccuracies. A 2025 study found GPT-3.5 required fewer edits but took longer to post-edit than GPT-4 and a fine-tuned Mistral model because corrections were more complex.¹⁰

This “fluency trap” once again highlights how important it is to combine strong MT with experienced post-editors who can navigate these nuances efficiently, rather than relying on surface fluency alone.

When human translation wins

 MTPE and human translation are complementary tools in the same toolbox. In some scenarios, a human-only workflow simply delivers the best overall value. Alongside MTPE, there are specific contexts where a human‑only workflow remains the most appropriate choice:

• Legal documentation: jurisdictional nuances require precision¹¹
• Creative works: human translation outperforms in creativity and idiom handling
• Brand-critical content: cultural adaptation beyond literal accuracy

For certain language pairs (such as English-Swedish) or when MT quality doesn’t yet meet our internal threshold, a human-only workflow is currently more efficient.² So such decisions are made at project level, taking into account all relevant aspects, including the quality of MT for a given language pair.

Certified translation quality and security with SwissGlobal

SwissGlobal delivers specialised translation and MTPE services backed by industry-leading certifications and secure infrastructure. Our approach integrates human expertise with machine efficiency to deliver optimal outcomes for every project:

We hold four key ISO certifications that guarantee standardised, verifiable quality:

• ISO 17100:2015 for translation services, ensuring qualified linguists and the four-eyes principle
• ISO 18587:2017 for post-editing of machine translation, guaranteeing qualified post-editors and equivalent content quality
• ISO 9001:2015 for quality management, a continuous process improvement and client satisfaction
• ISO 27001:2017 for information security, providing encrypted data processing on Swiss servers via CSF

Learn more about our quality and security standards.

By combining certified quality assurance with intelligent workflow design, we provide measurable translation productivity gains without compromising the precision and cultural nuance your global content demands.

Optimising your translation productivity strategy

The evidence is clear: MTPE can deliver substantial efficiency gains, but these gains vary widely depending on language pair, content type, and machine translation quality. The 66% average speed advantage is realistic yet masks significant variation, from 130% faster for some pairs to 7% slower for others.

Success lies not in choosing between human translation and MTPE, but in determining which approach suits each project. This decision requires robust quality estimation, strategic content stratification, and sustainable pricing that supports experienced linguists.

For ISO-certified human translation and MTPE workflows that maximise your translation productivity, contact SwissGlobal.

References

1. Nimdzi Insights (2025). “The State of the Language Industry: 2025 Edition.”
2. Terribile, S. (2023). “Is post-editing really faster than human translation?”
3. Martikainen, M., et al. (2025). “Evaluation of Generative Artificial Intelligence Implementation Impacts in Social and Health Care Language Translation” JMIR Formative Research
4. Albarino, S. (2023). “How Fast Should You Post-edit Machine Translation? Here’s What the EU Translation Centre Thinks.” Slator.
5. European Parliament. (2025, April 4). “Annual Activity Report 2024 | DG TRAD.”
6. Translated. (n.d.). “Fair Rate Policy.”
7. Fairwork. (2022). “The Fairwork Cloudwork Ratings 2022: The translation industry.”
8. Girletti, S., & Lefer, M.-A. (2025). “Lost in compensation: pricing methods, rates, and income satisfaction among freelance translators in Belgium and Switzerland.” Perspectives: Studies in Translatology, 1–23.
9. Ahsan, A., Mujadia, V., & Sharma, D. M. (2021). “Assessing Post-editing Effort in the English-Hindi Direction.” Proceedings of the 18th International Conference on Natural Language Processing.
10. Castaldo, A., Castilho, S., Moorkens, J., & Monti, J. (2025). “Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing.” Proceedings of Machine Translation Summit XX: Volume 1, 506–515.
11. SwissGlobal. (2025, April 16). “Legal translation beyond reasonable doubt.”