Voyage AI
Voyage 3.5 — strong MTEB retrieval quality for RAG and semantic search
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates.
Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.
- Voyage 3.5 ranks highly on MTEB retrieval benchmarks
- Voyage 3.5 Lite: cost-efficient embedding option
- Voyage Code 3 for code-optimised embeddings
- Voyage Multimodal 3 for image + text embeddings
- Reranking models for two-stage retrieval pipelines
- Default embedding provider for Anthropic Claude documentation
- Embeddings and reranking only — no chat or generation models
- Smaller ecosystem than OpenAI
- No open-weight models
Voyage AI Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
voyage-3.5-lite Voyage | Embedding | budget | $0.02/ $0.00 in / out per 1M EST Jul 2026 | — | 60ms | 0 t/s | 32K | 0 | ||
rerank-2 Voyage Rerank | Reranker | budget | $0.05 per 1M tokens EST Jul 2026 | — | 120ms | 0 t/s | 16K | 0 | ||
voyage-3.5 Voyage | Embedding | budget | $0.06/ $0.00 in / out per 1M EST Jul 2026 | — | 80ms | 0 t/s | 32K | 0 | ||
voyage-multimodal-3 Voyage | Embedding | efficient | $0.12/ $0.00 in / out per 1M EST Jul 2026 | — | 100ms | 0 t/s | 32K | 0 |
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Voyage AI is a proprietary AI lab that develops and hosts its own large language models. Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates. Common use cases include RAG pipelines, Semantic search, Document retrieval, Code search. Headquartered in San Francisco, CA, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Voyage AI vs other LLM providers
Voyage AI competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content. Use the LLM provider comparison tool to see Voyage AI token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Voyage AI?
Voyage AI stands out because: Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.. The API currently offers 4 models, spanning 1 efficient, 3 budget tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Voyage AI token pricing
Voyage AI charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Voyage AI's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Voyage AI context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Voyage AI's largest context window is 32K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Voyage AI makes sense
Managed inference APIs like Voyage AI eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Voyage AI API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Voyage AI token price data is available in the LLM price history charts.
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