Embedding Model Selection Guide

Model Selection Criteria

Choosing the right embedding model impacts retrieval quality, costs, and system performance.

Key Factors

1. Performance (MTEB Score):

• General benchmark performance
• Task-specific metrics
• Domain relevance

2. Cost:

• API pricing (if using hosted)
• Inference costs (if self-hosted)
• Model size

3. Latency:

• Model size affects speed
• Batch processing capability
• Hardware requirements

4. Context Length:

• How much text can be embedded
• 512 vs 8192 tokens

5. Dimensions:

• Storage implications
• Performance trade-offs

Model Categories

General Purpose:

• OpenAI text-embedding-3-small/large
• Cohere Embed v3/v4
• voyage-3
• all-MiniLM-L6-v2 (lightweight)
• BGE-base/large

Domain-Specific:

• Medical: PubMedBERT
• Legal: Legal-BERT
• Code: CodeBERT
• Scientific: SciBERT

Multilingual:

• voyage-multilingual-3
• multilingual-e5-large
• LaBSE
• paraphrase-multilingual

Long Context:

• jina-embeddings-v3 (8K tokens)
• Nomic Embed (8K tokens)

Top Performers (2026)

Best Overall: voyage-4, Cohere Embed v4 Best Open-Source: BGE-M3, jina-embeddings-v3 Best Budget: all-MiniLM-L6-v2, text-embedding-3-small Best Multimodal: voyage-multimodal-3.5

Selection by Use Case

General RAG:

• OpenAI text-embedding-3-small (cost/performance)
• voyage-3 (best quality)

Code Search:

• CodeBERT
• OpenAI text-embedding-3 (surprisingly good)

Multilingual:

• voyage-multilingual-3
• multilingual-e5

Long Documents:

• jina-embeddings-v3
• Nomic Embed

Budget-Conscious:

• all-MiniLM-L6-v2 (self-host)
• text-embedding-3-small (API)

Evaluation Methodology

1. Benchmark on MTEB: Standard comparison