NVIDIA
NV-Embed v2
Open weightopen-weightNVIDIA's state-of-the-art text embedding model ranked #1 on MTEB leaderboard for retrieval and semantic similarity tasks.
Capability profile
Radar view of the model's practical strengths. This chart is backed by textual summaries below for crawlability.
Benchmark summary
Leading embedding model for RAG, semantic search, and text similarity tasks.
No benchmark series is attached to this model yet. Source links and product metadata are available below.
Strengths
- • MTEB #1 ranking
- • Strong retrieval quality
- • Long context support
Trade-offs
- • Embedding only, no generation
Crawlable benchmark analysis
NV-Embed v2 is positioned as an embedding model with published scores that emphasize its practical fit for buyers evaluating the entry.
Published scores highlight reasoning 20/100, coding 10/100, enterprise readiness 80/100, vision 5/100, speed 90/100, and safety 70/100.
Pricing is not fully published for this entry. With a context window of 32,768 tokens, it supports large-document analysis and retrieval workflows.
Benchmark coverage is still limited for this entry, so this section focuses on published metadata and deployment fit.
Sources
Provider and distribution links used to verify this model record.
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