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Video Understanding on EgoSchema (test)

77.9AccuracyQwen2-VL-72B
24.75638.55352.3566.147Sep 18, 2024Dec 7, 2024Feb 26, 2025May 18, 2025Aug 6, 2025Oct 26, 2025Jan 15, 2026

Evaluation results

MethodLinks
Qwen2-VL-72BParameters=72B2024.0977.9—
GPT-5Access Type=API call only2026.0175.6—
Claude Sonnet 4.5Access Type=API call only2026.0173.1—
GPT-4o2024.0972.2—
Gemini 2.5 ProAccess Type=API call only2026.0172.2—
Eagle2.5-8BAccess Type=Open weights only2026.0172.2—
Gemini-1.5-ProInput Res.=768×768, Num Frames=322025.0372.2—
GPT-5 miniAccess Type=API call only2026.0170.9—
Gemini 2.5 FlashAccess Type=API call only2026.0170.2—
Qwen3-VL-8BAccess Type=Open weights only2026.0169.8—
Gemini 3 ProAccess Type=API call only2026.0168.9—
PLM-8BAccess Type=Open models2026.0168.8—
Qwen3-VL-4BAccess Type=Open weights only2026.0168.4—
PLM-3BAccess Type=Open models2026.0166.9—
Qwen2-VL-7BParameters=7B2024.0966.7—
Qwen2-VL-7B-Ins.Backbone=7B-Instruct2024.1266.7—
Gemini-1.5-FlashInput Res.=768×768, Num Frames=322025.0365.7—
VanillaBackbone=Qwen2.5-VL 7B, Token reduction ratio=0%2024.1265100
iLLaVABackbone=Qwen2.5-VL 7B, Token reduction ratio=90%2024.1263.397.4
Gemini 1.5-Pro2024.0963.2—
iLLaVABackbone=Qwen2.5-VL 7B, Token reduction ratio=95%2024.1262.796.4
GLM-4.1V-9BAccess Type=Open weights only2026.0162.6—
VisionZipBackbone=Qwen2.5-VL 7B, Token reduction ratio=90%2024.1262.596.2
FasterVLMBackbone=Qwen2.5-VL 7B, Token reduction ratio=90%2024.1262.395.9
PyramidDropBackbone=Qwen2.5-VL 7B, Token reduction ratio=90%2024.1262.295.8
Previous SoTA2024.0962—
LLaVA-OV-72BBackbone=72B2024.1262—
Molmo2-8BAccess Type=Molmo2 family2026.0162—
FasterVLMBackbone=Qwen2.5-VL 7B, Token reduction ratio=95%2024.1261.895
VisionZipBackbone=Qwen2.5-VL 7B, Token reduction ratio=95%2024.1261.794.9
PyramidDropBackbone=Qwen2.5-VL 7B, Token reduction ratio=95%2024.1261.594.6
Molmo2-4BAccess Type=Molmo2 family2026.0161.2—
SparseVLMBackbone=Qwen2.5-VL 7B, Token reduction ratio=90%2024.1261.194
SparseVLMBackbone=Qwen2.5-VL 7B, Token reduction ratio=95%2024.1260.593
LLaVA-OV-7BBackbone=7B2024.1260.1—
InternVL3.5-4BAccess Type=Open weights only2026.0158.9—
LLaVA-OV-72Bvariant=Single-Image (SI), Backbone=72B2024.1258.6—
InternVL3.5-8BAccess Type=Open weights only2026.0158.6—
MAmmoTH-VL-8BBackbone=8B2024.1258.5—
LLaVA-Video-7BAccess Type=Open models2026.0157.3—
Molmo2-O-7BAccess Type=Molmo2 family2026.0156.8—
Keye-VL-1.5-8BAccess Type=Open weights only2026.0156.3—
Qwen2-VL-2BParameters=2B2024.0954.9—
VideoChat2-7BTotal Params=7.3B, Input Res.=224×224, Num Frames=16, Throughput=11.42025.0354.4—
InternVL-2-8BBackbone=8B2024.1254.2—
LLaVA-OV-7Bvariant=Single-Image (SI), Backbone=7B2024.1252.9—
VideoChat-Flash-7BAccess Type=Open models2026.0151.3—
LLaVA-Mini-8BTotal Params=8.4B, Input Res.=336×336, Num Frames=1fps, Throughput=4.62025.0351.2—
MiniCPM-V-4.5-8BAccess Type=Open weights only2026.0149.6—
Video-LLaVA-7BTotal Params=7.3B, Input Res.=224×224, Num Frames=8, Throughput=12.52025.0345.3—
LLaMA-VID-7BTotal Params=7.3B, Input Res.=336×336, Num Frames=1fps, Throughput=6.02025.0338.5—
Mobile-VideoGPT-1.5BTotal Params=1.6B, Input Res.=224×224, Num Frames=16, Throughput=41.02025.0336.7—
Video-ChatGPT-3.8BTotal Params=4.2B, Input Res.=224×224, Num Frames=100, Throughput=14.12025.0336.2—
Mobile-VideoGPT-0.5BTotal Params=0.6B, Input Res.=224×224, Num Frames=16, Throughput=45.92025.0331.4—
LLaVA-OneVision-0.5BTotal Params=1.0B, Input Res.=384×384, Num Frames=32, Throughput=22.72025.0326.8—