WMT24++ (en→xx)
WMT24++ (en→xx) - machine translation benchmark, English to other languages.
Category
Multilingual
Score type
percent
Scale
100%
higher is better
Models tested
13
What is WMT24++ (en→xx)?
WMT24++ (en→xx) is a machine-translation benchmark: English source texts must be translated into around 55 target languages, including many non-commercial ones. The score reflects translation quality measured against references.
What does it test?
WMT24++ (en→xx) measures performance in languages other than english. In detail, it checks:
- translation quality into a wide set of languages
- faithfulness to the source text
- quality for less common and lower-resource languages
- consistency across language pairs
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the WMT24++ (en→xx) benchmark?
WMT24++ (en→xx) is a machine-translation benchmark: English source texts must be translated into around 55 target languages, including many non-commercial ones. The score reflects translation quality measured against references.
What does WMT24++ (en→xx) test?
WMT24++ (en→xx) mainly measures performance in languages other than English. Concretely, it checks: translation quality into a wide set of languages; faithfulness to the source text; quality for less common and lower-resource languages.
How is the WMT24++ (en→xx) score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get WMT24++ (en→xx) data and scores?
The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/wmt24-plus-plus-en-xx/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 13 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
DeepSeek-V4-Pro
DeepSeek
|
85.9%
|
04.06.2026 | Report | Unverified |
| 2 |
DeepSeek-V4-Flash
DeepSeek
|
85.9%
|
04.06.2026 | Report | Unverified |
| 3 |
Kimi K2.6
Moonshot AI
|
84.5%
|
04.06.2026 | Report | Unverified |
| 4 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
83.7%
|
04.06.2026 | Report | Verified |
| 5 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
78.3%
|
01.02.2026 | Report | Verified |
| 6 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
77.6%
|
01.02.2026 | Report | Unverified |
| 7 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
76.3%
|
01.02.2026 | Report | Unverified |
| 8 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
75.8%
|
01.02.2026 | Report | Unverified |
| 9 |
Qwen3.5-4B
Qwen
|
66.6%
|
01.02.2026 | Report | Verified |
| 10 |
Gemma 3 27B PT
Google DeepMind
|
55.7%
|
27.02.2025 | Report | Verified |
| 11 |
Gemma 3 12B PT
Google DeepMind
|
53.9%
|
27.02.2025 | Report | Verified |
| 12 |
Gemma 3 4B PT
Google DeepMind
|
48.4%
|
27.02.2025 | Report | Verified |
| 13 |
Gemma 3 1B PT
Google DeepMind
|
36.7%
|
27.02.2025 | Report | Verified |