[Voyage AI](/ai-gateway/models/labs/voyage) # voyage-3-large voyage-3-large is Voyage AI's general-purpose embedding model with a context window of 0 tokens, Matryoshka dimensionality (2048/1024/512/256), and quantization-aware training. It outperforms OpenAI text-embedding-3-large by 9.74% across 100 retrieval datasets. Your use is subject to Voyage AI's [Terms](https://www.voyageai.com/tos) & [Privacy](https://www.voyageai.com/privacy) Policies. [Use with AI Gateway](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%3Futm_source%3Dgateway-model-page%26utm_campaign%3Dai-gateway-models&title=Get+Started+with+Vercel+AI+Gateway) [View docs](https://vercel.com/docs/ai-gateway) ``` 1import { embed } from 'ai'; 2 3const result = await embed({ 4 model: 'voyage/voyage-3-large', 5 value: 'Sunny day at the beach', 6}) ``` [Read docs](https://vercel.com/docs/ai-gateway/sdks-and-apis/ai-sdk) [Overview](/ai-gateway/models/voyage-3-large) [About](/ai-gateway/models/voyage-3-large/about) [Providers](/ai-gateway/models/voyage-3-large/providers) [Similar](/ai-gateway/models/voyage-3-large/similar) [FAQ](/ai-gateway/models/voyage-3-large/faq) ## [Copy link to heading](#providers)Providers Route requests across multiple providers. Copy a provider slug to set your preference. Visit the [docs](/docs/ai-gateway/provider-options) for more info. Using a provider means you agree to their terms, listed under Legal. | Provider | | --- | | Context | Input | Capabilities | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [Voyage AI](/ai-gateway/models/providers/voyage) Legal:[Terms](https://www.voyageai.com/tos)•[Privacy](https://www.voyageai.com/privacy) | | $0.18/M | | | | | 01/07/2025 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | ## [Copy link to heading](#more-models-by-voyage-ai)More models by Voyage AI All Embed Rerank | Model | | --- | | Context | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | Providers | ZDR | No Training | Free Tier | Release Date | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/voyage\-4-large](/ai-gateway/models/voyage-4-large) | 32K | | | $0.12/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 01/15/2026 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/voyage-4](/ai-gateway/models/voyage-4) | 32K | | | $0.06/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 01/15/2026 | | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/voyage-4-lite](/ai-gateway/models/voyage-4-lite) | 32K | | | $0.02/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 01/15/2026 | | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/rerank-2.5](/ai-gateway/models/rerank-2.5) | 32K | | | $0.05/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 08/11/2025 | | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/rerank-2.5-lite](/ai-gateway/models/rerank-2.5-lite) | 32K | | | $0.02/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 08/11/2025 | | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) [voyage/voyage-3.5-lite](/ai-gateway/models/voyage-3.5-lite) | | | | $0.02/M | | | — | | ![voyage logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fvoyage.png&w=48&q=75) | | | | 05/20/2025 | | ## [Copy link to heading](#about-voyage-3-large)About voyage-3-large voyage-3-large is Voyage AI's general-purpose embedding model, released January 7, 2025. It supports a context window of 0 tokens and produces embeddings in four dimensions: 2048, 1024, 512, and 256 through Matryoshka learning. You can tune the tradeoff between retrieval accuracy and vector storage cost without retraining or running multiple models. Across 100 datasets spanning eight domains, voyage-3-large outperforms OpenAI text-embedding-3-large by 9.74% and Cohere Embed v3 English by 20.71%. Quantization-aware training enables multiple precision formats: 32-bit float, signed and unsigned 8-bit integer, and binary. Binary 512-dimensional embeddings outperform OpenAI text-embedding-3-large at full 3072-dimensional float precision while requiring 200x less storage. Int8 precision at 1024 dimensions loses only 0.31% quality versus full-precision 2048-dimensional output, cutting storage by 8x. These options make voyage-3-large practical for large-scale production indices. Voyage AI evaluates voyage-3-large across technical documentation, code, legal, financial, web, multilingual, long-document, and conversational domains. It outperforms Voyage AI's own domain-specific models on legal and financial retrieval tasks. That makes it a practical single-model choice when you need broad domain coverage without managing multiple specialized embedding endpoints. ## [Copy link to heading](#what-to-consider-when-choosing-a-provider)What To Consider When Choosing a Provider - Configuration: voyage-3-large offers four embedding dimensions and multiple quantization levels. Start with 1024-dimensional `int8` embeddings for most production workloads. This configuration loses only 0.31% quality versus full precision at 2048 dimensions while using 8x less storage. Drop to 512 or 256 dimensions only when storage constraints are severe. - Configuration: Confirm your vector database supports the embedding dimension and precision format you pick before indexing. Switching dimensions after indexing requires re-embedding your entire corpus. - Configuration: voyage-3-large outperforms Voyage AI's own domain-specific models on legal and financial tasks. If your retrieval spans multiple domains, use voyage-3-large instead of running separate specialized models. - Zero Data Retention: Zero Data Retention is offered on a per-provider and model basis. See the [documentation](https://vercel.com/docs/ai-gateway/security-and-compliance/zdr) for details. - Authentication: AI Gateway authenticates requests using an [API key](https://vercel.com/docs/ai-gateway/authentication-and-byok#api-key-authentication) or [OIDC token](https://vercel.com/docs/ai-gateway/authentication-and-byok#oidc-token-authentication). You do not need to manage provider credentials directly. ## [Copy link to heading](#when-to-use-voyage-3-large)When to Use voyage-3-large ### Best for - General-purpose semantic search: One embedding model covers technical, legal, financial, and conversational content - RAG pipelines: Large corpora where the context window of 0 tokens fits longer documents and retrieval chunks without truncation - Storage-sensitive deployments: Matryoshka dimensionality and quantization reduce vector database costs by up to 200x with binary embeddings - Multilingual retrieval: Applications that need a single embedding index across languages - High-accuracy retrieval: The 9.74% improvement over OpenAI text-embedding-3-large translates to measurably better recall in production ### Consider alternatives when - Cost is the primary constraint: Voyage-3.5-lite offers high retrieval quality at a lower price point when accuracy requirements are moderate - Your corpus is exclusively source code: Voyage-code-3 is purpose-built for that domain - You need multimodal embeddings: Cohere Embed v4 supports images, screenshots, and interleaved content natively - Your workload is latency-sensitive: A lighter model reduces per-query response time at very high query volumes ## [Copy link to heading](#conclusion)Conclusion voyage-3-large works well as a default for teams that need one high-accuracy embedding model across diverse retrieval domains. Its Matryoshka dimensionality and quantization options give you practical cost controls at scale. The context window of 0 tokens handles longer documents without chunking compromises. Route it through AI Gateway for unified API access, usage tracking, and provider flexibility.