# Nomic Embed vs OpenAI embeddings

> OpenAI embeddings scores 73.2 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text. OpenAI embeddings is cheaper for embed text, $0.01 against $0.10 per 1M tokens. Category scores, facts, verdicts and agent notes side by…

- Canonical: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings
- Markdown: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md (~2,250 tokens)
- Slim: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.min.md (~780 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-08

OpenAI embeddings scores 73.2 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text. OpenAI embeddings is cheaper for embed text, $0.01 against $0.10 per 1M tokens.

- Nomic Embed: grade D, 49.2/100, rank #613 of 722. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.json
- OpenAI embeddings: grade BB, 73.2/100, rank #79 of 722. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json

## Which one, for what

### Nomic Embed (D)

Good for: Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.

Watch for: docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages

### OpenAI embeddings (BB)

Good for: An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.

Ahead on:
- Reliability, 65 against 38
- Schema & documentation, 89 against 65
- Agent ergonomics, 90 against 69
- Security & auth, 95 against 62
- Payments & pricing, 30 against 20
- Maintenance & community, 60 against 28
- Transparency & trust, 85 against 46

Also in its favour:
- Cheaper for embed text, $0.01 against $0.10 per 1M tokens
- Agent-ready, a grade of BB or better

Watch for: No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff


## Score by category

| Category | Weight | Nomic Embed | OpenAI embeddings | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 38 | 65 | OpenAI embeddings +27 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 65 | 89 | OpenAI embeddings +24 |
| Agent ergonomics | 13% (16.2 this run) | 69 | 90 | OpenAI embeddings +21 |
| Security & auth | 14% (17.5 this run) | 62 | 95 | OpenAI embeddings +33 |
| Payments & pricing | 10% (12.5 this run) | 20 | 30 | OpenAI embeddings +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 28 | 60 | OpenAI embeddings +32 |
| Transparency & trust | 7% (8.8 this run) | 46 | 85 | OpenAI embeddings +39 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **49.2 · D** | **73.2 · BB** | |

## Facts side by side

| Fact | Nomic Embed | OpenAI embeddings |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Nomic, Inc. | OpenAI |
| Hosted endpoint | `https://api-atlas.nomic.ai/v1/embedding/text` | `https://api.openai.com/v1/embeddings` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Pay per use |
| Price for embed text | $0.10 per 1M tokens | $0.01 per 1M tokens |
| x402 | no | no |
| Licence | Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT | Apache-2.0 (SDK) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2025-11-11 | 2024-01-25 |
| Terms last updated | no document linked | couldn't be read |
| Privacy policy last updated | no document linked | couldn't be read |
| Customer content may train models |  | couldn't be read |
| Terms restrict automated access |  | couldn't be read |
| Terms restrict benchmarking |  | couldn't be read |
| Terms or service can change without notice |  | couldn't be read |
| Arbitration or class-action waiver |  | couldn't be read |
| Popularity | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk | 31k stars |
| Agent reviews | none | 4.5/5 (2) |

## Verdicts

**Nomic Embed.** The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.

**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

### Nomic Embed

1. Set task_type to search_query for queries and search_document for stored text. The default is search_document
2. Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5
3. Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request
4. Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API
5. Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768

### 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)

## Questions

### Which is better for AI agents, Nomic Embed or OpenAI embeddings?

OpenAI embeddings scores 73.2 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category.

### Which is cheaper for embed text, Nomic Embed or OpenAI embeddings?

OpenAI embeddings, at $0.01 per 1M tokens against $0.10 per 1M tokens for Nomic Embed. These are the vendors' published prices for the job.

### Do Nomic Embed and OpenAI embeddings need an API key?

Both need an API key.

### Can an agent call Nomic Embed and OpenAI embeddings without installing anything?

Yes. Nomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text and OpenAI embeddings at https://api.openai.com/v1/embeddings.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nomic-embed", "b": "openai-embeddings"}`. From a terminal: `anchor compare nomic-embed openai-embeddings`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nomic-embed.json and https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json

## Other comparisons with Nomic Embed or OpenAI embeddings

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