AI Models for Your GenAI Apps

Building GenAI applications with the right embedding and reranking models that suit your business.

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Choosing the Right Embedding Model for Your Data

Learn how to choose the right embedding model and where to find it based on your data type, language, specialty domain, and many other factors.

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Popular AI Models

Learn and compare top embedding and reranking models available in the market.

text-embedding-3-small
Embedding
Text
Proprietary
Milvus & Zilliz Cloud Integrated
OpenAI's most cost-effective text embedding model with improved accuracy and efficiency compared to text-embedding-ada-002 and text-embedding-3-large.
text-embedding-ada-002
Embedding
Text
Proprietary
Milvus Integrated
OpenAI's legacy text embedding model; with an average level of price and performance when compared with text-embedding-3-large and text-embedding-3-small.
text-embedding-3-large
Embedding
Text
Proprietary
Milvus & Zilliz Cloud Integrated
OpenAI's best-performing text embedding model; Tailored for English text.
clip-vit-base-patch32
Embedding
Multimodal
Apache 2.0
Zilliz Cloud Integrated
A CLIP multimodal model variant that utilizes a ViT-B/32 Transformer architecture for image encoding and a masked self-attention Transformer for text encoding.
jina-embeddings-v2-small-en
Embedding
Text
Apache 2.0
Milvus Integrated
An English monolingual embedding model for a sequence length of up to 8192 tokens; the smallest variant in the Jina Embeddings v2 family.
jina-embeddings-v2-base-en
Embedding
Text
Apache 2.0
Milvus Integrated
Specialized embedding model for English text and long documents; support sequences of up to 8192 tokens
voyage-code-2
Embedding
Text
Proprietary
Milvus Integrated
Voyage AI's text embedding model optimized for code retrieval (17% better than alternatives).
voyage-2
Embedding
Text
Proprietary
Milvus Integrated
Voyage AI's general-purpose text embedding model optimized for balancing cost, latency, and retrieval quality.
voyage-large-2
Embedding
Text
Proprietary
Milvus Integrated
Voyage AI’s general-purpose text embedding model; optimized for retrieval quality (e.g., better than OpenAI V3 Large); ideal for tasks like summarization, clustering, and classification.
embed-english-light-v3.0
Embedding
Text
Proprietary
Milvus Integrated
A smaller, faster version of embed-english-v3.0. Almost as capable but a lot faster. English only.
embed-english-v3.0
Embedding
Text
Proprietary
Milvus Integrated
Tailored for English text; MTEB score: 64.5; BEIR score: 55.9
embed-multilingual-v3.0
Embedding
Text
Proprietary
Milvus Integrated
Tailored for multilingual text; supports 100+ languages and can be used to search within a language and across languages; MTEB score: 64; BEIR score: 54.6
all-MiniLM-L12-v2
Embedding
Text
Apache 2.0
Milvus Integrated
A sentence and short paragraph encoder ideal for semantic search and RAG; based on the pre-trained Microsoft/MiniLM-L12-H384-uncased model and was fine-tuned on a large dataset of over 1 billion sentence pairs.
bge-reranker-base
Reranking
Text
Apache 2.0
Milvus Integrated
A cross-encoder model optimized for reranking tasks built on the foundation of BERT architecture.
all-mpnet-base-v2
Embedding
Text
Apache 2.0
Milvus Integrated
A sentence and short paragraph encoder; generates 768-dimensional vectors.
instructor-base
Embedding
Text
Apache 2.0
Milvus Integrated
The smallest variant of the Instructor model family, designed to balance efficiency and performance.
instructor-large
Embedding
Text
Apache 2.0
An instruction-finetuned model tailored for text embeddings; better performance than `instructor-base`, but worse than `instructor-xl`.
jina-embeddings-v3
Embedding
Text
CC BY-NC 4.0
Milvus Integrated
A multilingual text embedding model; supports 89 languages in total; can process up to 8192 tokens per sequence.
mistral-embed
Embedding
Text
Proprietary
A specialized embedding model for text data with a context window of 8,000 tokens. Optimized for similarity retrieval and RAG applications. MTEB retrieval score: 55.26.
rerank-english-v2.0
Reranking
Text
Proprietary
Milvus Integrated
A reranking model for English language documents with a context length of 512 tokens.

AI Models for Your GenAI Apps

Choosing the Right Embedding Model for Your Data

Popular AI Models

text-embedding-3-small

text-embedding-ada-002

text-embedding-3-large

clip-vit-base-patch32

jina-embeddings-v2-small-en

jina-embeddings-v2-base-en

voyage-code-2

voyage-2

voyage-large-2

embed-english-light-v3.0

embed-english-v3.0

embed-multilingual-v3.0

all-MiniLM-L12-v2

bge-reranker-base

all-mpnet-base-v2

instructor-base

instructor-large

jina-embeddings-v3

mistral-embed

rerank-english-v2.0