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Tech riderRev. 22 Sept 2026

OPUS-MT

Ch 616.3of 154 Translation Software
  1. 1Runs onAPI, Linux, self-hosted, Web
  2. 2CostsNot stated by the maker
  3. 3API accessYes
2 lines stated Written from the maker's own pages: github.com
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The OPUS-MT homepage

Overview

OPUS-MT is ranked #61 of 154 in translation software on Specifiction. It runs on API, Linux, Self-hosted, Web.

Compared on translation software

API access
Yesgithub.com

Facts

Purpose
OPUS-MT provides open neural machine translation models and tools for translation services.github.com · 5 Oct 2026
Model foundation
The models are based on Marian-NMT and trained on OPUS data using OPUS-MT-train.github.com · 5 Oct 2026
Model license
The repository says its downloadable pre-trained translation models are licensed CC-BY 4.0.github.com · 5 Oct 2026
Web interface and API
One included setup is a Tornado-based web application with a web UI and API for multiple language pairs.github.com · 5 Oct 2026
Deployment
The web application can be installed manually or run with Docker, including a documented CUDA GPU Docker option.github.com · 5 Oct 2026
Linux service
The project documents installing a WebSocket translation service on Ubuntu.github.com · 5 Oct 2026
Translation output
The WebSocket example returns translated text along with alignment and source and target segments.github.com · 5 Oct 2026
Language pair configuration
The web server uses a JSON configuration to specify translation language pairs and model decoder configurations or remote hosts.github.com · 5 Oct 2026
Translation workflow integration
The project points to OPUS-CAT as an NMT plugin for Trados Studio that can run OPUS-MT models.github.com · 5 Oct 2026
Quality limitation
The maintainers caution that many automatic evaluation scores use simple, short Tatoeba sentences and may be too optimistic for realistic data.github.com · 5 Oct 2026
Training limitation
The maintainers say the model training scripts are heavily customized for the University of Helsinki and CSC computing environment.github.com · 5 Oct 2026
Model limitations
The maintainers state that current models lack filtering, data augmentation, domain adaptation, and quality control beyond automatic evaluation on selected test sets.github.com · 5 Oct 2026

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Sources