audio benchmark
VocalSound Leaderboard
VocalSound is a dataset of over 21,000 crowdsourced recordings of human non-speech vocalizations from 3,365 unique subjects.
Updated Sep 5, 2026
Models1
Model coverage1
MetricScore
EvidenceB
VocalSound Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score93.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
VocalSound Highlights
The leading models and scores on this benchmark.
The Top AI Models for VocalSound
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis vocalsound AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACAlibaba Cloud / Qwen Team
- Score
- 93.90%
- Price
- $0.10 input / $0.40 output per 1M tokens
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures VocalSound, not total model capability
Selection summary
Best AI Models for VocalSound
Qwen2.5-Omni-7B currently leads VocalSound with 93.90%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is VocalSound?
What VocalSound measures and how its scores work.
VocalSound is an audio dataset containing recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs for audio event classification and recognition.
It measures audio event classification and recognition of human non-speech vocalizations using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VocalSound
- Modality
- audio
- Primary category
- audio
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- overall
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VocalSound.
Which model scores highest on VocalSound?
Qwen2.5-Omni-7B is currently ranked first with 93.90%.
What are the top three models on VocalSound?
The current leaders are Qwen2.5-Omni-7B (93.90%).
Which VocalSound model has the lowest official input price?
Qwen2.5-Omni-7B has the lowest matched official input price at $0.10 input / $0.40 output per 1M tokens.
Which models are fastest among VocalSound results?
No matched runtime record is currently available.
Does the highest score result prove overall model quality?
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.