voyage-3-large
Voyage AI has unveiled voyage-3-large, a cutting-edge general-purpose and multilingual embedding model that leads across eight evaluated domains, including law, finance, and code, outperforming OpenAI-v3-large and Cohere-v3-English by averages of 9.74% and 20.71%, respectively. Enabled by Matryoshka learning and quantization-aware training, it supports embeddings of 2048, 1024, 512, and 256 dimensions, along with multiple quantization options such as 32-bit floating point, signed and unsigned 8-bit integer, and binary precision, significantly reducing vector database costs with minimal impact on retrieval quality. Notably, voyage-3-large offers a 32K-token context length, surpassing OpenAI's 8K and Cohere's 512 tokens. Evaluations across 100 datasets in diverse domains demonstrate its superior performance, with flexible precision and dimensionality options enabling substantial storage savings without compromising quality.
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Cohere Embed
Cohere's Embed is a leading multimodal embedding platform designed to transform text, images, or a combination of both into high-quality vector representations. These embeddings are optimized for semantic search, retrieval-augmented generation, classification, clustering, and agentic AI applications. The latest model, embed-v4.0, supports mixed-modality inputs, allowing users to combine text and images into a single embedding. It offers Matryoshka embeddings with configurable dimensions of 256, 512, 1024, or 1536, enabling flexibility in balancing performance and resource usage. With a context length of up to 128,000 tokens, embed-v4.0 is well-suited for processing large documents and complex data structures. It also supports compressed embedding types, including float, int8, uint8, binary, and ubinary, facilitating efficient storage and faster retrieval in vector databases. Multilingual support spans over 100 languages, making it a versatile tool for global applications.
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voyage-code-3
Voyage AI introduces voyage-code-3, a next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 32 code retrieval datasets, respectively. It supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization options, including float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a 32 K-token context length, it surpasses OpenAI's 8K and CodeSage Large's 1K context lengths. Voyage-code-3 employs Matryoshka learning to create embeddings with a nested family of various lengths within a single vector. This allows users to vectorize documents into a 2048-dimensional vector and later use shorter versions (e.g., 256, 512, or 1024 dimensions) without re-invoking the embedding model.
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voyage-4-large
The Voyage 4 model family from Voyage AI is a new generation of text embedding models designed to produce high-quality semantic vectors with an industry-first shared embedding space that lets different models in the series generate compatible embeddings so developers can mix and match models for document and query embedding to optimize accuracy, latency, and cost trade-offs. It includes voyage-4-large (a flagship model using a mixture-of-experts architecture delivering state-of-the-art retrieval accuracy at about 40% lower serving cost than comparable dense models), voyage-4 (balancing quality and efficiency), voyage-4-lite (high-quality embeddings with fewer parameters and lower compute cost), and the open-weight voyage-4-nano (ideal for local development and prototyping with an Apache 2.0 license). All four models in the series operate in a single shared embedding space, so embeddings generated by different variants are interchangeable, enabling asymmetric retrieval strategies.
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