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Comparison of Intfloat: Multilingual-E5-Large, Nemotron-3-Embed-1B-BF16
2 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×nvidiaNemotron-3-Embed-1B-BF16Remove ×
What’s differentThe multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized…Nemotron-3-Embed-1B-BF16 is a compact multilingual text embedding model from NVIDIA, pruned and distilled from Ministral-3 to ~1B parameters, that maps text into 2048-dimensional dense vectors for retrieval…
DateJun 4, 2024ListedJul 15, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotes
Provider endpoints1 endpoint →0 endpoints →
FreshStack · MTEB 2.14.3 ↗MTEB 2.14.3; test/default; dataset 7a20df1aNot reported0.4173 ↗Revision f8801746; test/defaultContributor reported
Context window51232.8K
Max output460—
Parameters—1B
Open weightsSupportedNot reported

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