Best Embedding Models for RAG
Find the best embedding models for RAG and semantic search. We benchmark OpenAI, Voyage, Cohere, Gemini, Jina, BAAI, Qwen, and open-source models on accuracy, latency, and cost—so you can pick the right one. If you want to compare the best embedding models for your data, try Agentset.
Last updated: February 15, 2026
| Compare | |||||||
|---|---|---|---|---|---|---|---|
1605 | 0.628 | 435 | $0.000 | 3072 | Proprietary | ||
1590 | 0.619 | 250 | $0.050 | 2048 | CC BY-NC 4.0 | ||
1586 | 0.624 | 339 | $0.060 | 1024 | Proprietary | ||
1566 | 0.608 | 289 | $0.050 | 1024 | CC BY-NC 4.0 | ||
1563 | 0.709 | 18 | $0.130 | 3072 | Proprietary | ||
1534 | 0.501 | 272 | $0.180 | 1024 | Proprietary | ||
1512 | 0.701 | 7 | $0.100 | 512 | Proprietary | ||
1510 | 0.718 | 41 | $0.050 | 4096 | Apache 2.0 | ||
1490 | 0.703 | 19 | $0.020 | 512 | Proprietary | ||
1489 | 0.703 | 18 | $0.060 | 1024 | Proprietary |
Embedding Models Are Just One Piece of RAG
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Overview
Our Recommendation
We recommend Gemini Embedding 2 as the best overall embedding model for production use. See our Gemini Embedding 2 benchmark for detailed results.
Highest Win Rate
Gemini Embedding 2 leads with 1605 ELO, winning more head-to-head matchups than any other model.
Strong Competition
zembed-1 and Voyage 4 follow closely, with all top 3 models within 20 ELO points.
Strong Accuracy
Top models deliver high nDCG and Recall scores across diverse datasets with consistent retrieval.
Understanding Embeddings
What are embeddings?
Vector Representations of Text
Embeddings are numerical vector representations of text that capture semantic meaning. They transform words, sentences, or documents into high-dimensional vectors where similar content has similar vector representations. This enables machines to understand context, relationships, and nuances in natural language.
Why Embeddings Matter
Embeddings are the foundation of modern semantic search and RAG systems. Unlike keyword-based search, embeddings understand meaning and context, enabling systems to find relevant information even when exact words don't match. They power vector databases, enable similarity search, and are essential for building intelligent AI applications.
When to Use Different Embedding Models
The choice of embedding model affects retrieval quality, latency, and cost. High-dimensional models (1024–3072 dimensions) offer better accuracy but require more storage and compute. Smaller models are faster and more cost-effective for high-volume applications. Consider your accuracy requirements, infrastructure constraints, and language support needs when selecting a model.
Selection Guide
Choosing the right embedding model
For Maximum Accuracy
Best for:
- • High-stakes RAG applications
- • Customer-facing chatbots
- • Complex technical documentation
For Self-Hosting
Best for:
- • Data privacy requirements
- • High-volume applications
- • Custom fine-tuning needs
For Low Latency
Best for:
- • Real-time applications
- • High-concurrency scenarios
- • Mobile applications
For Multilingual Support
Best for:
- • International applications
- • Multilingual documentation
- • Cross-language search
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Methodology
How We Evaluate Embeddings
The Embedding Model Leaderboard tests models on multiple datasets — financial queries, scientific claims, business reports, and more — to see how well they capture semantic meaning across different domains.
Testing Process
Each embedding model is tested on the same query-document pairs. We measure both retrieval quality and latency, capturing the real-world balance between accuracy and speed that matters for production RAG systems.
ELO Score
For each query, GPT-5 compares two retrieved result sets and picks the more relevant one. Wins and losses feed into an ELO rating — higher scores mean more consistent wins across diverse queries.
Evaluation Metrics
We measure nDCG@5/10 for ranking precision and Recall@5/10 for coverage. Together, they show how well an embedding model surfaces relevant results at the top of search results.
Common questions
Embedding Model FAQ
- What is an embedding model?
- An embedding model converts text into numerical vectors that capture semantic meaning. These vectors enable similarity search and form the foundation of modern retrieval systems. Similar content produces similar vectors, allowing machines to understand context and relationships.
- Why are embeddings important for RAG?
- Embeddings enable semantic search in RAG systems. They help find relevant documents based on meaning rather than just keywords, leading to better context retrieval and more accurate LLM responses. High-quality embeddings are essential for effective RAG.
- How much do better embeddings improve retrieval?
- Top embedding models can improve retrieval accuracy by 10–30 % compared to older or smaller models. This translates to better context for your LLM, fewer irrelevant results, and more reliable RAG performance overall.
- Why use ELO scoring for ranking?
- ELO scoring measures how often one model outperforms another in direct comparisons. It reflects real-world consistency better than isolated metrics — a higher ELO means the model wins more head-to-head matchups across diverse queries and datasets.
- Which datasets are used for evaluation?
- We benchmark embeddings on multiple datasets including FiQA (finance), SciFact (science), MSMARCO (web search), DBPedia (knowledge base), PG (long-form content), and business reports. This diversity ensures models are tested across different domains and query types.
- Should I use an open-source or proprietary embedding model?
- Open-source models like BAAI/bge-m3 and Jina Embeddings v3 offer great performance and full control for self-hosting. Proprietary options like OpenAI and Cohere provide slightly better accuracy and managed infrastructure. Choose based on your accuracy requirements, data privacy needs, and deployment preferences.