Multilingual Benchmark

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.

Model scores 13 models tested

Reset
# 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

What now?

You've seen how models score on WMT24++ (en→xx) — here's what to do next.