OpenAI leads on micro-pricing ($0.020/1M). Voyage AI leads on MTEB dense retrieval accuracy (68.2%). Is the higher accuracy worth the small price premium?
| Metric / Feature | Voyage-3 | Voyage-3-lite | OpenAI text-embedding-3-small | OpenAI text-embedding-3-large |
|---|---|---|---|---|
| Cost / 1M Tokens | $0.120 | $0.070 | $0.020 | $0.130 |
| MTEB Retrieval Score | 68.2% | 65.8% | 62.3% | 64.6% |
| Max Context Window | 32,000 | 32,000 | 8,191 | 8,191 |
| Native Vector Dimensions | 1024 | 512 | 1536 (truncatable to 512) | 3072 |
| Best Production Use Case | High-stakes legal / medical RAG | High-throughput balanced RAG | General semantic search | Cross-lingual document matching |
In RAG pipelines, sending irrelevant context chunks to your generation model (like Claude 3.7 or GPT-4o) inflates your input token bill and causes hallucinations.
Because Voyage-3 retrieves the exact relevant paragraph on the top-1 or top-3 rank (rather than needing top-10 chunks), you can inject 60% fewer tokens into your final LLM prompt. Saving 3,000 input tokens on Claude ($0.009 per query) completely eclipses the $0.0001 difference in embedding cost.