Voyage 3 M Exp

Voyage-3-m-exp is a general-purpose text embedding model optimized for datasets similar to those in the MTEB benchmark. It produces 2048-dimensional embeddings with a context length of 32,000 tokens (model size: 6918M parameters). This model is an intermediate snapshot trained on the training sets of MTEB datasets and uses task-specific prompts to achieve strong performance across retrieval, classification, clustering, and semantic similarity tasks.

For optimal MTEB reproduction, the model requires prepending specific prompts per task — for example, "Represent the sentence to retrieve similar sentence: " for STS tasks, "Classify the text: " for classification tasks, "Cluster the text: " for clustering, and specialized prompts for retrieval datasets such as MS MARCO, NQ, FEVER, and others. Because the model was tuned on MTEB training data, results on those benchmarks reflect this exposure.

Note: For production use cases, Voyage AI recommends voyage-3-large, which is likely strictly better than this intermediate snapshot. Voyage-3-m-exp is accessible through the gigarouter API under the model name voyage-3-m-exp.