FLEURS
Phi-4-multimodal 4.00% leads since Mar 2025 Fading Models transcribe read speech in 102 languages built from FLoRes-101 sentences. Model cards usually report word error rate, so lower scores are better.
FLEURS also supports speech translation, retrieval, and language identification, so check which task a score uses.
In the authors’ words
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Short answer A number, word or short string matched to a key.
Phi-4-multimodal leads with 4.00% among models released by Mar 2026
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 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 Phi-4-multimodal 4.00%
- 2
Qwen3-Omni-30B-A3B 5.33% - 3 Qwen3.5-Omni-Plus 6.55%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Phi-4-multimodal | 4.00% | Mar 2025 | Model card | |
| 2 | Qwen3-Omni-30B-A3B | 5.33% | Sep 2025 | Model card | |
| 3 | Qwen3.5-Omni-Plus | 6.55% | Apr 2026 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs headline it less often now, last in Sep 2026
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.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 18 Sep 2026 | Qwen3.8-Omni-Flash | 9.3 | FLEURS-ASR Multilingual ASR (WER, 60 languages); FLEURS-S2TT BLEU 31.8 |
| 28 Apr 2026 |  Seed2.0 Lite (0428) | 74.7 | Fleurs (15 langs) (zh/en<->xx) speech translation |
| 2 Apr 2026 |  Gemma 4 31B IT | FLEURS (lower is better) (31B: no score) | |
| 30 Mar 2026 | Qwen3.5-Omni-Plus | 6.55 | Fleurs(top60) ASR WER (S2TT xx to zh/en BLEU 32.8) |
| 31 Oct 2025 |  LongCat-Flash-Omni | 3.99 | Fleurs (zh | en) 3.99 | 5.02 |
| 22 Sep 2025 | Qwen3-Omni-30B-A3B | 5.33 | Fleurs-avg (19 lang) ASR WER; Fleurs-en 2.72, Fleurs-zh 2.20 |
| 15 Jul 2025 |  Voxtral Small | FLEURS transcription, macro-average WER (chart) | |
| 25 Apr 2025 | Kimi-Audio-7B-Instruct | 2.69 | Fleurs (zh | en) 2.69 | 4.44 |
| 27 Mar 2025 | Qwen2.5-Omni-7B | 3.55 | Mean of Fleurs zh 3.0 / en 4.1 |
12 system cards report it, from 8 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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 23 Oct 2026 |  Nemotron 3.5 Bolt E4B technical report | 10.2 | FLEUR (WER/CER), lower is better, reasoning, 10.43 non-reasoning (Table 8) | Capabilities |
| 22 Sep 2026 | Qwen3.8-Omni-Flash technical report | 9.3 | FLEURS-ASR WER over 60 languages, lower is better (Table 4). FLEURS-S2TT BLEU 31.8 | Capabilities |
| 17 Apr 2026 | Qwen3.5-Omni-Plus technical report | 6.55 | Fleurs (top60) ASR WER, lower is better (Table 5). S2TT top59 BLEU: xx↔zh 30.2, xx↔en 35.4, xx↔zh/en 32.8 (Table 5). Per-language detail in Tables 13 to 15 | Capabilities |
| 4 Feb 2026 |  ERNIE 5.0 technical report | Fleurs-en 3.14, Fleurs-zh 0.83, WER, lower is better, no overall score (Table 7) | Capabilities | |
| 31 Oct 2025 | LongCat-Flash-Omni technical report | Fleurs zh 3.99, en 5.02, error rate, lower is better, no overall score (Table 9) | Capabilities | |
| 22 Sep 2025 | Qwen3-Omni-30B-A3B technical report | 5.33 | Fleurs-avg (19 lang) ASR WER, lower is better, Instruct (Table 6). Fleurs-en 2.72, Fleurs-zh 2.20. S2TT BLEU en2xx 37.50, xx2en 31.08, zh2xx 25.17, xx2zh 33.13 | Capabilities |
| 17 Jul 2025 | Voxtral Small technical report | FLEURS ASR WER per language with no printed macro-average, lower is better: ar 13.44, nl 4.94, en 3.35, fr 4.03, de 3.38, hi 7.69, it 2.62, pt 3.79, es 2.72 (Table 4, Figure 3 chart). FLEURS speech translation BLEU: en to de 47.0, es 39.9, fr 57.3, it 39.9, de to en 56.6, es to en 46.3, fr to en 54.2, it to en 46.8 (Table 7) | Capabilities | |
| 7 Jul 2025 | Gemini 2.5 Pro technical report | 6.66 | FLEURS, 53 languages, WER (lower is better) (Table 5) | Capabilities |
| 7 Jul 2025 | Gemini 2.5 Flash technical report | 9.95 | FLEURS, 53 languages, WER (lower is better) (Table 5) | Capabilities |
| 25 Apr 2025 | Kimi-Audio-7B-Instruct technical report | Fleurs zh 2.69, en 4.44, WER, lower is better, no overall score (Table 4) | Capabilities | |
| 26 Mar 2025 | Qwen2.5-Omni-7B technical report | Fleurs zh 3.0, en 4.1, WER, lower is better, no overall score (Table 2) | Capabilities | |
| 3 Mar 2025 | Phi-4-multimodal technical report | 4 | FLEURS ASR, WER average over 8 languages, lower is better (Tables 3 and 4). FLEURS AST BLEU X-EN 29.86 (CoT 32.35), EN-X 32.15 (CoT 33.56) (Table 5) | Capabilities |
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