Text and multimodal embedding models for semantic search and retrieval.
OpenAI's most capable embedding model with 3072 dimensions
Industry standard for production embeddings. Matryoshka support lets you trade dimensions for cost. Best MTEB scores among API models. Drop-in for any RAG pipeline.
Multilingual embedding model optimised for search and RAG
Best multilingual embeddings for search. 100+ languages with compression options. Search and classification input types improve relevance. Strong for global enterprise RAG.
Specialised embeddings for code, legal, finance, and multilingual
Domain-specific variants (code, law, finance) consistently outperform general models. Best code embedding for codebase search. Acquired by Anthropic — expect deep Claude integration.
BAAI multi-granularity multilingual embedding with dense + sparse
Unique hybrid model supporting dense, sparse, and ColBERT retrieval in one model. 100+ languages, 8K context. Best open-source choice for multilingual RAG systems.
Large LLM-based embedding model for maximum retrieval quality
LLM-scale embedding model — 7B params delivers top MTEB scores. Task-specific prompting improves quality. Needs GPU but excels where embedding quality is critical.
8K context multilingual embedding with task-specific LoRAs
Long context embeddings up to 8192 tokens — ideal for document-level retrieval. Task-specific LoRA adapters for retrieval, classification, and similarity. Good API and open weights.
Fully open-source embedding model with Matryoshka support
Truly open — fully auditable training data and code. Competitive MTEB scores at 137M params. Runs on CPU. Best for teams requiring full transparency and reproducibility.
Classic lightweight embedding model — fast and CPU-friendly
The embedding model that started the vector search revolution. 22M params, runs anywhere. Quality surpassed by newer models but still the default for quick prototypes and edge deployment.
Alibaba's embedding model with strong CJK language support
Excellent for Chinese, Japanese, Korean retrieval scenarios. Multiple size variants from 1.5B to 7B. Good balance of multilingual quality and inference cost.
Emerging high-quality embedding model from Berlin-based lab
Strong newcomer with competitive MTEB scores. Binary quantization support for efficient storage. Good API with self-host options. Watch this space — rapidly improving.
