When building Retrieval-Augmented Generation (RAG) and semantic search applications, your choice of embedding model is arguably your most critical architectural decision. The embeddings dictate how well your application "understands" user queries and retrieves relevant context.
In this post, we compare the top embedding models for RAG and semantic search across retrieval quality (measured in ELO and nDCG@10), latency, and cost. Our leaderboard includes proprietary giants like OpenAI, Voyage, Cohere, and Gemini, alongside powerful open-source alternatives like Jina, BAAI, and Qwen.
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Below is the definitive ranking of top embedding models, factoring in their ELO rating, retrieval performance, speed, and cost per 1M tokens.
| Model Name | ELO | nDCG@10 | Latency (ms) | Price / 1M | Dimensions | License |
|---|---|---|---|---|---|---|
| Voyage 4 | 1564 | 0.859 | 17 | $0.060 | 1024 | Proprietary |
| Jina Embeddings v5 Text Small | 1558 | 0.710 | 289 | $0.050 | 1024 | CC BY-NC 4.0 |
| OpenAI text-embedding-3-large | 1539 | 0.811 | 10 | $0.130 | 3072 | Proprietary |
| Voyage 3 Large | 1528 | 0.837 | 113 | $0.180 | 1024 | Proprietary |
| Qwen3 Embedding 8B | 1516 | 0.818 | 56 | $0.050 | 4096 | Apache 2.0 |
| Voyage 3.5 | 1515 | 0.816 | 13 | $0.060 | 1024 | Proprietary |
| OpenAI text-embedding-3-small | 1503 | 0.762 | 9 | $0.020 | 1536 | Proprietary |
| Voyage 3.5 Lite | 1503 | 0.803 | 11 | $0.020 | 512 | Proprietary |
| Cohere Embed Multilingual v3 | 1501 | 0.781 | 7 | $0.100 | 512 | Proprietary |
| Qwen3 Embedding 4B | 1496 | 0.802 | 28 | $0.020 | 2560 | Apache 2.0 |
| Jina Embeddings v3 | 1491 | 0.766 | 93 | $0.045 | 1024 | Apache 2.0 |
| BAAI/bge-m3 | 1491 | 0.753 | 29 | $0.010 | 1024 | MIT |
| Cohere Embed v3 | 1488 | 0.686 | 7 | $0.100 | 1024 | Proprietary |
| Qwen3 Embedding 0.6B | 1478 | 0.751 | 23 | $0.010 | 1024 | Apache 2.0 |
| Gemini text-embedding-004 | 1447 | 0.585 | 13 | $0.020 | 768 | Proprietary |
1. Voyage and OpenAI Lead Proprietary Performance: Voyage 4 currently sits at the top of the ELO rankings with an impressive nDCG@10 of 0.859, closely followed by OpenAI's text-embedding-3-large model. These are your go-to options if maximum retrieval quality is an absolute necessity.
2. The Open Source Revolution is Here: Jina Embeddings v5 Text Small punches far above its weight class, securing the number two spot on our leaderboard under a CC BY-NC 4.0 license. Qwen3's family of models also offers formidable Apache 2.0-licensed alternatives that rival proprietary APIs.
3. Speed vs. Dimension Trade-offs: Cohere's latest multilingual model is blazingly fast at just 7ms latency, while still packing serious understanding into 512 dimensions. Conversely, Qwen3's 8B model offers massive 4096-dimensional embeddings at the cost of slightly higher latency (56ms).
The great news is that you no longer have to compromise. Whether you're building a lightweight semantic search application on a budget, or a massively scaled enterprise RAG system that demands state-of-the-art accuracy, there's an embedding model designed specifically for your constraints.
For most businesses, balancing cost and performance means looking closely at models like Voyage 3.5 Lite or OpenAI's text-embedding-3-small, which offer incredible value at just $0.020 per million tokens. If data privacy is a primary concern, the open-source offerings from Jina, Qwen, and BAAI provide tier-one performance completely within your control.
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