---
date:
  created: 2024-10-24
categories:
  - Vector Search
  - Vector Databases
tags:
  - Embedding Model
authors:
  - myriel
---

# Picking the right 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 is crucial.

## Can't go to warp without a good vectorization engine!

![Choosing Embedding Models](/img/choosing-embedding-models/embedding-model.gif)

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Qdrant supports dense, sparse, and 𝗺𝘂𝗹𝘁𝗶𝘃𝗲𝗰𝘁𝗼𝗿 embeddings. However, the vectors' performance largely depends on the embedding model and its ability to capture meaning.

## What's the best model for you?

1️⃣ Look at benchmarks like 𝗠𝗧𝗘𝗕 or 𝗕𝗘𝗜𝗥. But remember - the top model might not perform the same in your setup. You need to experiment!

2️⃣ Ask the community & experts. On 𝗤𝗱𝗿𝗮𝗻𝘁'𝘀 𝗗𝗶𝘀𝗰𝗼𝗿𝗱, I am seeing 𝗻𝗼𝗺𝗶𝗰-𝗲𝗺𝗯𝗲𝗱-𝘁𝗲𝘅𝘁 perform better than OpenAI’s 𝘁𝗲𝘅𝘁-𝘀𝗺𝗮𝗹𝗹-𝟯 for text retrieval. Try to learn from the experiences of others.

💬 What’s your go-to embedding model? Are you using 𝘦5, 𝘯𝘰𝘮𝘪𝘤, 𝘖𝘱𝘦𝘯𝘈𝘐’𝘴 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴, 𝘰𝘳 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘦𝘭𝘴𝘦? Let me know in the comments!

P.S. 𝗖𝗼𝗵𝗲𝗿𝗲 𝗘𝗺𝗯𝗲𝗱 𝟯 just dropped—a multimodal model that handles complex docs and catalogs better than CLIP. Worth a try?