🧩 Embedding ModelsVector SearchRAG

Text Embedding Models 2026

Embedding models convert text into dense vector representations for semantic search, RAG pipelines, clustering, and recommendation systems. Unlike language models, they don't generate text — they produce numerical arrays that capture meaning, enabling similarity comparisons across millions of documents.

How to choose an embedding model

Dimensions

Higher dimensions = more nuance but slower vector search. 1024–3072 for accuracy; 384–768 for speed.

Max tokens

The max input length. Long-context embeddings (8k+) reduce chunking overhead in RAG pipelines.

Cost

Charged per input token — much cheaper than language models. At scale, open-source + self-hosted wins.

API Embedding Models

Model	Dimensions	Max tokens	$/1M tokens
text-embedding-3-large OpenAI flagship — best retrieval quality	3,072	8.191k	—
text-embedding-3-small OpenAI — best cost/quality for most RAG	1,536	8.191k	—
text-embedding-ada-002 OpenAI legacy — widely deployed	1,536	8.191k	—
voyage-3-large Voyage AI flagship — top MTEB score	1,024	32k	—
voyage-3-lite Voyage AI — fast, cheap alternative	512	32k	—
voyage-code-3 Voyage AI — optimized for code retrieval	1,024	32k	—
voyage-finance-2 Voyage AI — financial documents	1,024	32k	—
voyage-law-2 Voyage AI — legal documents	1,024	16k	—
text-embedding-004 Google Gecko — multilingual	768	2.048k	—
embedding-001	—	—	—
Embed English cohere Cohere — best for English RAG + reranking	1,024	512	—	Details →
embed-multilingual-v3.0 cohere Cohere — 100+ language support	1,024	512	—	Details →
mistral-embed-2312 mistralai Mistral AI — competitive open-weight pricing	1,024	8.192k	—	Details →

Open-Source Embedding Models

Self-hosted via Ollama, HuggingFace, or any inference server. Zero per-token cost at scale.

Model	Dimensions	Max tokens	Notes
Nomic-Embed-TextOPEN	768	8.192k	Open-source — run locally via Ollama
mxbai-embed-largeOPEN	1,024	512	Open-source — strong MTEB performance
BAAI/bge-large-en-v1.5OPEN	1,024	512	BAAI open-source — widely used in RAG

Which embedding model should I use?

General-purpose RAG

text-embedding-3-small

Best quality/cost for most retrieval workloads. 8k tokens handles most documents without chunking.

Maximum retrieval quality

voyage-3-large

Voyage 3 Large leads MTEB benchmarks. Anthropic recommends Voyage for Claude-based RAG.

Code search & retrieval

voyage-code-3

Trained specifically on code. Outperforms general models on function and snippet retrieval.

Multilingual / global

embed-multilingual-v3

Cohere Multilingual covers 100+ languages with strong cross-lingual retrieval quality.

High-volume / budget

text-embedding-3-small

At $0.02/1M tokens, the cheapest production-quality API embedding model available.

Self-hosted / offline

nomic-embed-text

Runs via Ollama with no API costs. Apache 2.0 license. Competitive MTEB score for its size.

Best models for RAG

Full LLMs ranked for retrieval-augmented generation

Compare two models

Side-by-side pricing and benchmark comparison