Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | nvidiaNemotron-3-Embed-1B-BF16Remove × | googleembeddinggemma-2Remove × | BAAIbge-m3-multi-8kRemove × |
|---|---|---|---|
| What’s different | 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… | EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,… | BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval),… |
| Date | Jul 15, 2026Listed | Oct 8, 2026Listed | Sep 3, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | See provider quotes | See provider quotes |
| Provider endpoints | 0 endpoints → | 0 endpoints → | 0 endpoints → |
Benchmarks Each result retains its evaluation configuration | |||
| FreshStack · MTEB 2.14.3 ↗MTEB 2.14.3; test/default; dataset 7a20df1a | 0.4173 ↗Revision f8801746; test/defaultContributor reported | Not reported | Not reported |
Specifications | |||
| Context window | 32.8K | 8.2K | 8.2K |
| Max output | — | — | — |
| Parameters | 1B | — | — |
Add another model
Search all models →QwenQwen3-Embedding-8BliquidLiquidAI: LFM2.5-Embedding-350M (free)voyageaiVoyageAI by MongoDB: voyage-code-4voyageaiVoyageAI by MongoDB: voyage-multimodal-3.5voyageaiVoyageAI by MongoDB: voyage-4-litevoyageaiVoyageAI by MongoDB: voyage-4voyageaiVoyageAI by MongoDB: voyage-4-largenvidiaNemotron-3-Embed-8B