Vector embeddings for search, retrieval, and clustering, priced per million tokens.
BGE-EN-ICL is an English text embedding model from BAAI (Beijing Academy of Artificial Intelligence), built on a 7B-parameter Mistral architecture. It supports in-context learning: adding a task instruction and a few example query and answer pairs to a query helps it adapt to new retrieval tasks, and it also works zero-shot.
Multilingual E5 Large Instruct is a text embedding model from Microsoft researchers, initialized from XLM-RoBERTa-large with 24 layers and 1,024-dimensional embeddings. It covers about 100 languages and takes inputs up to 512 tokens, with each query paired with a one-sentence task instruction to guide retrieval and similarity tasks.
Llama Nemotron Embed VL 1B v2 is NVIDIA's multimodal embedding model for question-answering retrieval over documents. It pairs a Llama 3.2 1B language model with a SigLIP2 image encoder (about 1.7B parameters total) and embeds text queries and document pages given as images, text or both, including tables, charts and infographics, as 2,048-dimensional vectors.
Qwen3 Embedding 0.6B is the smallest text embedding model in Alibaba's Qwen team's Qwen3 Embedding series, with 28 layers and a 32K-token context. It supports more than 100 natural and programming languages, takes optional task instructions, and produces embeddings of up to 1,024 dimensions that can be set to a smaller size.
Qwen3 Embedding 8B is the largest text embedding model in Alibaba's Qwen team's Qwen3 Embedding series, with 36 layers and a 32K-token context. It supports more than 100 natural and programming languages, takes optional task instructions, and produces embeddings of up to 4,096 dimensions that can be set anywhere from 32 to 4,096.
text-embedding-3-small is OpenAI's smaller, more efficient text embedding model for search, clustering, recommendations and classification. It accepts up to 8,192 input tokens and returns 1,536-dimensional embeddings by default, which can be shortened with the dimensions parameter.
text-embedding-3-large is OpenAI's larger text embedding model for search, clustering, recommendations and classification. It accepts up to 8,192 input tokens and returns 3,072-dimensional embeddings by default, which can be shortened with the dimensions parameter to trade accuracy for storage.
BGE-M3 is a multilingual text embedding model from BAAI (Beijing Academy of Artificial Intelligence), built from XLM-RoBERTa-large. It supports more than 100 languages and inputs up to 8,192 tokens, and one model provides dense, sparse (lexical) and multi-vector retrieval, producing 1,024-dimensional dense embeddings.
Gemini Embedding 2 is Google's natively multimodal embedding model, in preview. It maps text, images, video, audio and PDF documents into a single embedding space across more than 100 languages, accepting up to 8,192 text tokens, with 3,072-dimensional output that can be shortened through Matryoshka Representation Learning.
