VideoFDB — Leaderboard

Evaluating full-duplex vision-speech capabilities in conversational agents.

Best and second-best non-human entries are in bold and underlined. Timing reports TOR-Alignment percentage above median latency below. Full per-dynamic breakdowns are in the paper appendix.

Model Fluency ↑ Conv. Flow ↑ Vis. Ground. ↑ Overall ↑ Timing ↑
Human reference4.164.204.244.2090% / 1400 ms
Closed-Source Full-Duplex AV2AV models
Tavus Griffin Lite3.603.673.923.7373.8% / 2232 ms
Closed-source full-duplex speech-vision (AV2A)
Gemini 2.5 Flash Native3.332.813.373.1772% / 3160 ms
Gemini 3.1 Flash Live3.152.203.162.8466% / 1720 ms
OpenAI gpt-realtime-mini2.912.372.902.7366% / 5320 ms
OpenAI gpt-realtime2.722.503.022.7572% / 5400 ms
Open-source full-duplex speech-vision (AV2A)
MiniCPM-o 4.53.033.543.633.4073% / 720 ms
MiniOmni20.651.371.541.1964% / 3080 ms
VITA-1.51.191.572.531.7658% / 400 ms
Audio-only baselines (A2A; same agents, video withheld)
Gemini 2.5 Flash Native3.352.983.173.1773% / 2760 ms
Gemini 3.1 Flash Live3.402.643.033.0369% / 1240 ms
OpenAI gpt-realtime-mini3.052.483.122.8869% / 5000 ms
OpenAI gpt-realtime2.932.373.592.9767% / 4440 ms
MiniCPM-o 4.53.453.763.103.4472% / 920 ms
MiniOmni21.481.702.151.7269% / 2760 ms
VITA-1.51.621.373.022.0061% / 800 ms
Table 1. Performance breakdown across Perception rubrics. AV2A and A2A runs are paired on the same clips to isolate the visual contribution.
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