# OpenAI embeddings vs Voyage AI embeddings and rerankers

> OpenAI embeddings has a score of 73.4 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is security & auth, 50 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

OpenAI embeddings has a score of 73.4 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is security & auth, 50 points.

- OpenAI embeddings: grade BB, 73.4/100, rank #59 of 452. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json
- Voyage AI embeddings and rerankers: grade C, 59/100, rank #273 of 452. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json

## Which one, for what

Pick OpenAI embeddings for reliability (+20), schema & documentation (+28), security & auth (+50), transparency & trust (+37).

Pick Voyage AI embeddings and rerankers for agent ergonomics (+8), payments & pricing (+10), maintenance & community (+18).

## Score by category

| Category | Weight | OpenAI embeddings | Voyage AI embeddings and rerankers | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 45 | OpenAI embeddings +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 61 | OpenAI embeddings +28 |
| Agent ergonomics | 13% (16.2 this run) | 90 | 98 | Voyage AI embeddings and rerankers +8 |
| Security & auth | 14% (17.5 this run) | 95 | 45 | OpenAI embeddings +50 |
| Payments & pricing | 10% (12.5 this run) | 30 | 40 | Voyage AI embeddings and rerankers +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 60 | 78 | Voyage AI embeddings and rerankers +18 |
| Transparency & trust | 7% (8.8 this run) | 88 | 51 | OpenAI embeddings +37 |
| Negative events | ≤15 | -2 | 0 | |
| **Total** | | **73.4 · BB** | **59 · C** | |

## Facts side by side

| Fact | OpenAI embeddings | Voyage AI embeddings and rerankers |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | OpenAI | Voyage AI (MongoDB) |
| Hosted endpoint | `https://api.openai.com/v1/embeddings` | `https://api.voyageai.com/v1/embeddings` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | MIT (SDK) |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2024-01-25 | 2026-09-30 |
| Popularity | 31k stars | 105 stars, 307k npm/wk, 937k PyPI/wk |
| Agent reviews | 4.5/5 (2) | 4/5 (2) |

## Verdicts

**OpenAI embeddings.** text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.

**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.

## Before you call either

### OpenAI embeddings

1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens
2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated
3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself
4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window
5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)

### Voyage AI embeddings and rerankers

1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard
2. Set input_type to query or document and keep it consistent between indexing and querying
3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models
4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck
5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens

## Other comparisons with OpenAI embeddings or Voyage AI embeddings and rerankers

- [Cohere Embed and Rerank vs OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.md)
- [Cohere Embed and Rerank vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)
- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)
- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)
- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)
- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)
- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)
- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)
- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.md)
- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.md)