# Cohere Embed and Rerank vs OpenAI embeddings

> OpenAI embeddings has a score of 73.4 (BB) against Cohere Embed and Rerank's 72.5 (BB). Both do embed text. The largest gap is security & auth, 45 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

OpenAI embeddings has a score of 73.4 (BB) against Cohere Embed and Rerank's 72.5 (BB). Both do embed text. The largest gap is security & auth, 45 points.

- Cohere Embed and Rerank: grade BB, 72.5/100, rank #69 of 452. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-embed.json
- 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

## Which one, for what

Pick Cohere Embed and Rerank for reliability (+18), payments & pricing (+5), maintenance & community (+27).

Pick OpenAI embeddings for security & auth (+45), transparency & trust (+19).

## Score by category

| Category | Weight | Cohere Embed and Rerank | OpenAI embeddings | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 83 | 65 | Cohere Embed and Rerank +18 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 92 | 89 | Cohere Embed and Rerank +3 |
| Agent ergonomics | 13% (16.2 this run) | 87 | 90 | OpenAI embeddings +3 |
| Security & auth | 14% (17.5 this run) | 50 | 95 | OpenAI embeddings +45 |
| Payments & pricing | 10% (12.5 this run) | 35 | 30 | Cohere Embed and Rerank +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 87 | 60 | Cohere Embed and Rerank +27 |
| Transparency & trust | 7% (8.8 this run) | 69 | 88 | OpenAI embeddings +19 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **72.5 · BB** | **73.4 · BB** | |

## Facts side by side

| Fact | Cohere Embed and Rerank | OpenAI embeddings |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Cohere | OpenAI |
| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://api.openai.com/v1/embeddings` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Pay per use |
| x402 | no | no |
| Licence | MIT (SDK) | Apache-2.0 (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 | 2026-09-30 | 2024-01-25 |
| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 31k stars |
| Agent reviews | 3.5/5 (2) | 4.5/5 (2) |

## Verdicts

**Cohere Embed and Rerank.** Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.

**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.

## Before you call either

### Cohere Embed and Rerank

1. Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it
2. Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (an open SDK bug drops types missing from the first response)
3. Budget rerank by searches. One query with up to 100 documents is one search, and a document over 500 tokens counts as several
4. Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models
5. Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store

### 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)

## Other comparisons with Cohere Embed and Rerank or OpenAI embeddings

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