Snowflake's Arctic Embed L Beats OpenAI and Google at Semantic Search
Snowflake's open-source Arctic Embed L delivers 55.98 NDCG@10 on MTEB Retrieval, matching closed APIs from OpenAI and Cohere at a fraction of the size.
- Snowflake Arctic Embed L is a 335M-param, Apache 2.0 English embedding model hitting 55.98 NDCG@10 on MTEB Retrieval.
- Beats OpenAI text-embedding-3-large (55.44) and Cohere embed-english-v3.0 (55.00) at roughly a quarter of the parameter count.
- Built on e5-large-unsupervised with two-stage contrastive training: 400M pair pretraining plus 1M hard-negative triplet fine-tune.
- 1024-dim CLS embeddings, 512-token context, query prefix required, works with sentence-transformers, ONNX, and Transformers.js.
- English only; for multilingual workloads use the newer arctic-embed-l-v2.0 successor.
- Over 800K downloads and 170+ community fine-tunes make it a solid base for domain-specific retrieval.
Snowflake introduced the Arctic Embed family in 2024, with Arctic Embed L as its largest original English model. The encoder converts text into 1,024-dimensional vectors for semantic search and retrieval-augmented generation, where a vector database finds relevant passages before a separate model generates an answer.
Arctic Embed L uses the Apache 2.0 license and has roughly 335 million parameters. Snowflake’s materials also cite 334 million total parameters and 303 million excluding token embeddings, reflecting different counting conventions. The model is available in Safetensors and ONNX formats, with support for Sentence Transformers, raw Transformers, and Transformers.js.
A 335M model among API leaders
Snowflake reported an average MTEB Retrieval score of 55.98 NDCG@10. NDCG@10 measures how effectively a system places relevant results within its first 10 responses, with MTEB presenting the result on a 0-to-100 scale. The model card compared Arctic Embed L with several hosted embedding systems:
| Model | Parameters | NDCG@10 |
|---|---|---|
| snowflake-arctic-embed-l | About 335M | 55.98 |
| Google gecko-text-embedding | Undisclosed | 55.70 |
| OpenAI text-embedding-3-large | Undisclosed | 55.44 |
| Cohere embed-english-v3.0 | Undisclosed | 55.00 |
| bge-large-en-v1.5 | About 335M | 54.29 |
The table captures a historical model-card snapshot, and leaderboard positions change as evaluations and models evolve. MTEB also cannot predict performance on a company’s own documents, queries, languages, or relevance criteria. Domain-specific evaluation remains necessary before replacing an existing embedding service.
A model of this size requires about 670 MB for FP16 weights or 1.34 GB for FP32 weights before runtime overhead. Self-hosting removes per-request embedding charges, while compute, deployment, monitoring, and index storage remain part of the operating cost.
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