Voyage 3.5
Voyage 3.5 is Voyage AI's Voyage 3.5 general-purpose embedding model with a context window of 0 tokens. It surpasses OpenAI text-embedding-3-large by 8.26% and Cohere Embed v4 by 1.63% across eight retrieval domains, with Matryoshka dimensionality and quantization-aware training. Your use subject to Voyage AI's Terms & Privacy Policies.
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-3.5', value: 'Sunny day at the beach',})Providers
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More models by Voyage AI
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| 32K | $0.12/M | — |  | 01/15/2026 | ||||||||
| 32K | $0.02/M | — | | 01/15/2026 | ||||||||
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| 32K | $0.05/M | — | | 08/11/2025 | ||||||||
$0.12/M | — | | 06/03/2024 | |||||||||
$0.12/M | — | | 04/15/2024 |
About Voyage 3.5
Voyage 3.5 is Voyage AI's Voyage 3.5 general-purpose embedding model, released May 20, 2025. It supports a context window of 0 tokens and produces embeddings in four dimensions: 2048, 1024, 512, and 256. Voyage 3.5 surpasses OpenAI text-embedding-3-large by 8.26% and Cohere Embed v4 by 1.63% on average across eight retrieval domains including technical documentation, code, law, and finance.
Like its predecessor, Voyage 3.5 uses Matryoshka learning and quantization-aware training. It supports 32-bit float, 8-bit integer, and binary precision formats. int8 quantization at 2048 dimensions reduces vector database costs by 83% compared to OpenAI's offering while achieving better retrieval results. Binary rescoring yields up to 6.38% quality improvements, making aggressive compression practical for high-volume production indices.
Voyage 3.5 is the premium tier in Voyage AI's Voyage 3.5 general-purpose lineup. It delivers the highest retrieval accuracy among Voyage 3.5 general-purpose options on evaluated domains. For cost-sensitive workloads where a small accuracy tradeoff is acceptable, voyage-3.5-lite provides comparable coverage at one-third the price.
What To Consider When Choosing a Provider
- Configuration: Voyage 3.5 is Voyage AI's highest-accuracy embedding model in the Voyage 3.5 lineup. If your workload is cost-sensitive and can tolerate a small accuracy gap, voyage-3.5-lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost.
- Configuration: Use
int8precision at 2048 dimensions to cut vector storage costs by 83% compared to OpenAI's float embeddings. Binary precision with rescoring adds another compression layer for very large indices. - Configuration: Voyage 3.5 improves on voyage-3-large across all domains. Switching requires re-embedding your corpus but delivers measurable accuracy gains.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Voyage 3.5
Best for
- Highest-accuracy semantic search: Retrieval quality directly impacts application outcomes like customer-facing search or legal discovery
- RAG pipelines: Precise document retrieval across technical, legal, and financial content
- Multi-domain retrieval: A single embedding model handles diverse content types without domain-specific tuning
- Enterprise knowledge bases: Long documents benefit from the context window of 0 tokens
- Production systems: Int8 and binary quantization reduce infrastructure costs at scale without meaningful quality loss
Consider alternatives when
- Budget is the primary constraint: Voyage-3.5-lite achieves within 0.3% of Cohere Embed v4 at one-sixth the cost
- Your corpus is exclusively source code: Voyage-code-3 is purpose-built for code
- You need multimodal embeddings: Cohere Embed v4 supports interleaved text and image inputs
- Your workload is latency-critical: A lighter model may reduce per-query response time at very high query volumes
Conclusion
Voyage 3.5 delivers Voyage AI's highest retrieval accuracy among Voyage 3.5 general-purpose options across evaluated domains. Its Matryoshka dimensionality, quantization-aware training, and context window of 0 tokens give you both quality and cost flexibility through precision and dimension tuning. Route it through AI Gateway for unified access across providers.