1{ 2 "embedding": { 3 "voyage-2": { 4 "description": "General-purpose embedding model optimized for a balance between cost, latency, and retrieval quality.", 5 "inputTokens": 4000, 6 "inputTokenPrice": 0.1, 7 "dimensions": 1024 8 }, 9 "voyage-large-2": { 10 "description": "General-purpose embedding model that is optimized for retrieval quality.", 11 "inputTokens": 16000, 12 "inputTokenPrice": 0.12, 13 "dimensions": 1536 14 }, 15 "voyage-law-2": { 16 "description": "Optimized for legal and long-context retrieval and RAG. Also improved performance across all domains.", 17 "inputTokens": 16000, 18 "inputTokenPrice": 0.12, 19 "dimensions": 1024 20 }, 21 "voyage-code-2": { 22 "description": "Optimized for code retrieval (17% better than alternatives). ", 23 "inputTokens": 16000, 24 "inputTokenPrice": 0.12, 25 "dimensions": 1536 26 }, 27 "voyage-multilingual": { 28 "description": "Optimized for multilingual retrieval and RAG", 29 "inputTokens": 32000, 30 "inputTokenPrice": 0.12, 31 "dimensions": 1024 32 }, 33 "voyage-finance-2": { 34 "description": "Optimized for finance retrieval and RAG.", 35 "inputTokens": 32000, 36 "inputTokenPrice": 0.12, 37 "dimensions": 1024 38 } 39 } 40}