https://store-images.s-microsoft.com/image/apps.19458.1a77be20-d064-469f-8031-458b2f763a86.957dc48a-6be6-42de-bbbf-81666db7eaf9.60b3bd04-de93-4d4e-ba68-9b94ed0767c4
voyage-3-large Embedding Models
MongoDB, Inc.
voyage-3-large Embedding Models
MongoDB, Inc.
voyage-3-large Embedding Models
MongoDB, Inc.
Embedding model for state-of-the-art multilingual and domain retrieval/search. 32K context length
State-of-the-art text embedding model with the best general-purpose and multilingual retrieval quality. 32K context length.
Throughput varies significantly by workload pattern based on factors like GPU type, model size, sequence length, batch size, and vector dimensionality. Typically we see ~5k~15k tokens/sec for this model on A100 GPUs. We recommend customers benchmark their own throughput and token volume during testing to inform token TCO estimates.
voyage-3-large:
Outperforms OpenAI-v3-large and Cohere-v3-English by 9.74% and 20.71% on average across domains
Supports embeddings of 2048, 1024, 512, and 256 dimensions
Offers multiple quantization formats, including float, int8, uint8, and binary
Maintains a 32K-token context length, compared to OpenAI (8K) and Cohere (512)
Enables up to 200x storage cost savings while preserving or exceeding baseline retrieval quality