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Multimodal Embedding on MMEB V1

69.7Classification AccuracyThink-Then-Embed
38.546.654.762.8Nov 11, 2025Dec 16, 2025Jan 20, 2026Feb 25, 2026Apr 1, 2026May 6, 2026Jun 11, 2026

Evaluation results

MethodLinks
Think-Then-EmbedBackbone=Qwen2VL-2B, Method Category=Explicit Reasoning UME2026.0669.772.47490.680.56774.5
Think-When-NeededBackbone=Qwen3VL-4B, Method Category=Explicit Reasoning UME2026.0668.671.7688775.466.271.3
UNITE#Params=7B, Backbone=Qwen2-VL, Pre-training approach=Contrastive Learning2025.1168.365.171.684.873.666.370.3
Ops-MM-EmbedBackbone=Qwen2VL-2B, Method Category=Discriminative UME2026.0668.165.169.280.971.865.669
RIMEBackbone=Qwen2VL-2B, Method Category=Explicit Reasoning UME2026.0667.964.469.882.164.572.969.2
mmE5#Params=11B, Backbone=Llama-3.2-Vision, Pre-training approach=Contrastive Learning2025.1167.662.870.989.772.366.769.8
LaMEBackbone=Qwen2VL-2B, Method Category=Latent Reasoning UME2026.0667.666.270.581.273.164.469.3
CoMa#Params=7B, Backbone=Qwen2.5-VL, Pre-training approach=Additional pre-training2025.1167.470.672.487.675.267.672.2
PLUMEBackbone=Qwen2VL-2B, Method Category=Latent Reasoning UME2026.0666.159.567.679.969.662.266.3
MoCa#Params=7B, Backbone=Qwen2.5-VL, Pre-training approach=Additional pre-training2025.1165.864.77592.474.767.671.5
ReMatchBackbone=Qwen2VL-2B, Method Category=Discriminative UME2026.0665.865.970.183.372.864.769.2
CAFe#Params=7B, Backbone=LLaVA-OV, Pre-training approach=Contrastive Learning2025.1165.265.67091.275.862.469.8
UME-R1Backbone=Qwen2VL-2B, Method Category=Explicit Reasoning UME2026.0664.862.867.677.271.560.466.6
RGEBackbone=Qwen3VL-2B, Method Category=Explicit Reasoning UME2026.0664.467.870.290.175.363.770.1
UNITE#Params=2B, Backbone=Qwen2-VL, Pre-training approach=Contrastive Learning2025.1163.255.965.475.665.860.163.3
VLM2Vec-V2Backbone=Qwen2VL-2B, Method Category=Discriminative UME2026.0662.956.369.577.368.860.164.9
Embed-RLBackbone=Qwen2.5VL-3B, Method Category=Explicit Reasoning UME2026.0662.867.968.690.465.971.969.2
VLM2Vec#Params=7B, Backbone=Qwen2-VL, Pre-training approach=Contrastive Learning2025.1162.657.869.981.772.257.865.8
CoMa#Params=3B, Backbone=Qwen2.5-VL, Pre-training approach=Additional pre-training2025.1161.365.17082.771.361.667.5
UniME#Params=7B, Backbone=LLaVA-1.6, Pre-training approach=Additional pre-training2025.1160.652.967.985.168.457.966.6
MoCa#Params=3B, Backbone=Qwen2.5-VL, Pre-training approach=Additional pre-training2025.1159.862.970.688.672.361.567.5
VLM2VecBackbone=Qwen2VL-2B, Method Category=Discriminative UME2026.0658.749.36572.964.853.459.7
GME#Params=2B, Backbone=Qwen2-VL, Pre-training approach=Contrastive Learning2025.1156.941.267.853.4———
MMRet#Params=7B, Backbone=LLaVA-1.6, Pre-training approach=Contrastive Learning2025.115657.469.983.66859.165.8
VLM2Vec#Params=3B, Backbone=Qwen2.5-VL, Pre-training approach=Contrastive Learning2025.1155.357.362.773.2——60.3
CLIP#Params=0.4B, Backbone=ViT-L, Pre-training approach=Contrastive Learning2025.1155.219.753.262.247.642.845.4
UniME#Params=4.2B, Backbone=Phi3.5-V, Pre-training approach=Additional pre-training2025.1154.855.964.581.868.252.764.2
GMEBackbone=Qwen2VL-2B, Method Category=Discriminative UME2026.0654.429.966.955.549.255.251.9
CLIPBackbone=CLIP-L-0.4B, Method Category=Discriminative UME2026.0642.89.15351.837.138.737.8
OpenCLIP#Params=0.4B, Backbone=ViT-L, Pre-training approach=Contrastive Learning2025.1141.56.944.653.532.83636.6
E5-V#Params=7B, Backbone=LLaVA-1.6, Pre-training approach=Contrastive Learning2025.1139.710.839.460.234.233.933.9