Google Multimodal Embedding 001
We haven't written a description for this benchmark yet; see the card notes below for how each maker ran it.
- ModelScoreYour cost / mo
- 1voyage-multimodal-3.5 · Voyage AI30.6%
- 2voyage-multimodal-3.5 · Voyage AI4.7%
“Best value”: no other model here reports a higher score at a lower cost for your workload. Scores are self-reported by each maker with different settings, so treat close calls as ties.
How each maker reported it
- voyage-multimodal-3.5 launch post (NDCG@10; 18 multimodal + 38 text datasets)
Voyage's own numbers: average NDCG@10 margin of voyage-multimodal-3.5 over each model. Visual documents: ViDoRe, ViDoRe v2, MIRACL-VISION; video: MSR-VTT, YouCook2, DiDeMo; text: 38 datasets in six domains. Voyage also reports +4.56% over Cohere Embed v4 across 15 visual document datasets. Full results in Voyage's linked spreadsheet.
- voyage-multimodal-3.5 launch post (NDCG@10; 18 multimodal + 38 text datasets)
Voyage's own numbers: average NDCG@10 margin of voyage-multimodal-3.5 over each model. Visual documents: ViDoRe, ViDoRe v2, MIRACL-VISION; video: MSR-VTT, YouCook2, DiDeMo; text: 38 datasets in six domains. Voyage also reports +4.56% over Cohere Embed v4 across 15 visual document datasets. Full results in Voyage's linked spreadsheet.
Only each model's own maker's number is shown; scores a maker reports for rival models are left out.