# 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](https://app.agentset.ai/).

Last updated: February 15, 2026

| Model Name↕ | ELO↓ | nDCG@10↕ | Latency (ms)↕ | Price / 1M↕ | Dimensions↕ | License↕ | Compare |
| --- | --- | --- | --- | --- | --- | --- | --- |
| [Gemini Embedding 2](/content/embeddings/gemini-embedding-2/index.html) | 1605 | 0.628 | 435 | $0.000 | 3072 | Proprietary |  |
| [zembed-1](/content/embeddings/zembed-1/index.html) | 1590 | 0.619 | 250 | $0.050 | 2048 | CC BY-NC 4.0 |  |
| [Voyage 4](/content/embeddings/voyage-4/index.html) | 1586 | 0.624 | 339 | $0.060 | 1024 | Proprietary |  |
| [Jina Embeddings v5 Text Small](/content/embeddings/jina-embeddings-v5-text-small/index.html) | 1566 | 0.608 | 289 | $0.050 | 1024 | CC BY-NC 4.0 |  |
| [OpenAI text-embedding-3-large](/content/embeddings/openai-text-embedding-3-large/index.html) | 1563 | 0.709 | 18 | $0.130 | 3072 | Proprietary |  |
| [Voyage 3 Large](/content/embeddings/voyage-3-large/index.html) | 1534 | 0.501 | 272 | $0.180 | 1024 | Proprietary |  |
| [Cohere Embed Multilingual v3](/content/embeddings/cohere-embed-multilingual-v3/index.html) | 1512 | 0.701 | 7 | $0.100 | 512 | Proprietary |  |
| [Qwen3 Embedding 8B](/content/embeddings/qwen3-embedding-8b/index.html) | 1510 | 0.718 | 41 | $0.050 | 4096 | Apache 2.0 |  |
| [Voyage 3.5 Lite](/content/embeddings/voyage-35-lite/index.html) | 1490 | 0.703 | 19 | $0.020 | 512 | Proprietary |  |
| [Voyage 3.5](/content/embeddings/voyage-35/index.html) | 1489 | 0.703 | 18 | $0.060 | 1024 | Proprietary |  |

## Embedding Models Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no embeddings to manage.

## Overview

## Our Recommendation

We recommend [Gemini Embedding 2](/content/embeddings/gemini-embedding-2-preview/index.html) as the best overall embedding model for production use. See our [Gemini Embedding 2 benchmark](/content/blog/gemini-2-embedding/index.html) 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

Choose top-performing models like [Gemini Embedding 2](/content/embeddings/gemini-embedding-2-preview/index.html) or [Voyage 4](/content/embeddings/voyage-4/index.html). These models deliver the highest accuracy scores and are ideal for production applications where retrieval quality is paramount.

Best for:

- • High-stakes RAG applications
- • Customer-facing chatbots
- • Complex technical documentation

### For Self-Hosting

Open-source models like [BAAI/bge-m3](/content/embeddings/baaibge-m3/index.html) and [Jina Embeddings v3](/content/embeddings/jina-embeddings-v3/index.html) offer excellent performance with full control over deployment. These models can be hosted on your infrastructure, ensuring data privacy and cost control.

Best for:

- • Data privacy requirements
- • High-volume applications
- • Custom fine-tuning needs

### For Low Latency

[Gemini text-embedding-004](/content/embeddings/gemini-text-embedding-004/index.html) and [OpenAI text-embedding-3-small](/content/embeddings/openai-text-embedding-3-small/index.html) offer fast response times, making them ideal when processing speed is critical for your use case while maintaining good accuracy.

Best for:

- • Real-time applications
- • High-concurrency scenarios
- • Mobile applications

### For Multilingual Support

[Qwen3 Embedding 8B](/content/embeddings/qwen3-embedding-8b/index.html) and [BAAI/bge-m3](/content/embeddings/baaibge-m3/index.html) excel at multilingual tasks, supporting 100+ languages with strong cross-lingual retrieval capabilities. Perfect for international applications.

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.

```typescript
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.