Benchmark Almanac

Flores

Gemma 3 27B 48.8 leads since Mar 2025 Dropped

Models translate 3,001 English Wikipedia sentences that professionals translated into 101 languages. Scored with BLEU or chrF against references.

FLORES-200 extended it to 200 languages in 2022, and the Open Language Data Initiative now maintains it.

In the authors’ words

One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.

Abstract of The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (arXiv:2106.03193, CC0)
Made by Meta AI Released Jun 2021 Translation All tasks public Last in a launch post Mar 2025 , dropped for a newer version Last model added May 2025
Level
University Undergraduates and entry-level professionals
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
8
Models scored in lab cards
5
Launch posts list it, from 3 labs
5
System cards report it, from 2 labs

Gemma 3 27B leads with 48.8 among models released by Mar 2025

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in Mar 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Gemma 3 27B 48.8
  2. 2 Gemma 3 12B 46.0
  3. 3 Gemma 3 4B 39.2
  4. 4 Qwen2.5-72B 39.0
  5. 5 Qwen2-72B 37.8
  6. 6 Aya Vision 32B 37.8
  7. 7 Qwen1.5-72B 35.6
  8. 8 Gemma 3 1B 29.5
#ModelScoreSettingDateSource
1 Gemma 3 27B 48.8 Mar 2025 Model card
2 Gemma 3 12B 46.0 Mar 2025 Model card
3 Gemma 3 4B 39.2 Mar 2025 Model card
4 Qwen2.5-72B 39.0 Sep 2024 Model card
5 Qwen2-72B 37.8 Jun 2024 Model card
6 Aya Vision 32B 37.8 May 2025 Model card
7 Qwen1.5-72B 35.6 Feb 2024 Model card
8 Gemma 3 1B 29.5 Mar 2025 Model card

4 models have held number one since 2023

Lab claims count from the day the card came out, independent runs from the model’s release.

Qwen Google DeepMind
Lab claims Not tested since May 2025

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
25.0 30.0 35.0 40.0 45.0 50.0 2024 2025 25.0 30.0 35.0 40.0 45.0 50.0 2024 2025

Labs moved on to FLORES-200

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Qwen
Mistral AI
Google DeepMind
DateLaunch postScoreAs the post writes it
12 Mar 2025 Gemma 3 27B 48.8 FloRes (PT)
17 Feb 2025 Mistral Saba Arabic-Centric FLORES-101 (chart, metric not stated)
19 Sep 2024 Qwen2.5-72B 39 Multi-Translation
7 Jun 2024 Qwen2-72B 37.8 Multi-Translation
5 Feb 2024 Qwen1.5-72B 35.6 Multilingual Translation (Flores, BLEU)

5 system cards report it, from 2 labs

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
13 May 2025 Aya Vision 32B technical report 37.8 FLORES, En to X, spBLEU, 23 languages (Table 5) Capabilities
25 Mar 2025 Gemma 3 4B technical report 39.2 FLoRes, chrF, PT, 1-shot (Table 13) Capabilities
25 Mar 2025 Gemma 3 27B technical report 48.8 FLoRes, chrF, PT, 1-shot (Table 13) Capabilities
25 Mar 2025 Gemma 3 1B technical report 29.5 FLoRes, chrF, PT, 1-shot (Table 13) Capabilities
25 Mar 2025 Gemma 3 12B technical report 46 FLoRes, chrF, PT, 1-shot (Table 13) Capabilities

Versions

More in Multilingual

All 115 in Multilingual
  • MMMLU In use MMLU professionally translated into 14 languages. Multiple-choice accuracy. Claude Mythos Preview 92.7%
  • WMT24++ In use English-to-55-language translation of WMT24 test data. Scored with automatic metrics such as MetricX or xCOMET. K-EXAONE 90.5%
  • MMLU-ProX In use MMLU-Pro translated into up to 29 languages with expert review. Multiple-choice accuracy per language. Qwen3.7-Max 87%
  • CLIcK In use Models answer 1,995 multiple-choice questions on Korean language and culture. Questions come from official Korean exams and textbooks in eleven categories. Solar Open 2 90.7%
  • Global MMLU Lite 3.0 In use Models answer 400 human-translated MMLU questions per language in 23 languages. Version 3.0 adds Czech, Hungarian, Oriya, Slovak and Tajik and updates Italian. Accuracy. Inkling 88.7%
  • MMMB In use Models answer multiple-choice questions about images in six languages across 15 categories. The set has about 12,000 questions. LFM2.5-VL-3B 83%