Zen Embedding: #1 Multilingual Retrieval

Zen Embedding is our first purpose-built retrieval model, and it ships at the top of the MTEB multilingual leaderboard with a score of 70.58. It supports 100+ languages, instruction-tuned retrieval, and Matryoshka representation learning (MRL) for flexible embedding dimensions.

Benchmark Results

MTEB (Massive Text Embedding Benchmark) is the standard leaderboard for embedding model quality across retrieval, classification, clustering, and semantic similarity tasks.

The 70.58 MTEB multilingual score represents a meaningful gap over the prior generation of multilingual embedding models. For languages outside of English, the improvement is especially pronounced -- Zen Embedding was trained with explicit multilingual objectives rather than treating non-English as an afterthought.

Matryoshka Representation Learning

Zen Embedding implements MRL, which means a single model produces embeddings that are useful at multiple dimension sizes:

With MRL, you embed once at full 4096 dimensions and truncate at query time. No re-embedding required when you change dimension targets. This is particularly useful for tiered retrieval systems: coarse 256-dimensional first-pass, then re-rank with full 4096 dimensions.

Language Coverage

100+ languages with strong coverage across:

• European: English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Polish, and 20+ more
• CJK: Chinese (Simplified and Traditional), Japanese, Korean
• Arabic and right-to-left scripts
• South and Southeast Asian: Hindi, Bengali, Thai, Vietnamese, Indonesian, and more
• Low-resource languages with explicit training coverage

Instruction-tuning means the model understands retrieval intent. You can specify Represent this document for retrieval: or Represent this query for searching: prefixes, and the model adjusts its embedding accordingly.

Architecture

7B parameters, 8192 token context window. Built on a transformer encoder architecture with bidirectional attention -- standard for embedding models, distinct from the decoder-only architecture used in generative Zen models.

The instruction-tuned training objective uses contrastive learning with hard negatives mined from 100+ language corpora. MRL training adds nested loss at each dimension checkpoint.

Integration

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("zenlm/zen-embedding")

# Full 4096-dimensional embeddings
embeddings = model.encode(documents, normalize_embeddings=True)

# Truncate to 256 for fast retrieval
embeddings_256 = embeddings[:, :256]

Direct integration with LlamaIndex, LangChain, and any vector database that accepts sentence-transformers compatible models. Hanzo Cloud exposes Zen Embedding via the /v1/embeddings API endpoint.

Get Zen Embedding

• HuggingFace: huggingface.co/zenlm/zen-embedding
• Hanzo Cloud API: Available at api.hanzo.ai/v1/embeddings
• Zen LM: zenlm.org -- integration guides and benchmark details

Zach Kelling is the founder of Hanzo AI, Techstars '17.