Text embedding model for code retrieval and AI applications. 32K context length. Multiple output dimensions and embedding quantization. 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-code-3:

• 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

• Supports embeddings of 2048, 1024, 512, and 256 dimensions

• Offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8)

• Supports a 32K-token context length, compared to OpenAI (8K) and CodeSage large (1K)