# Gemini Embedding vs Nomic Embed

> Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed
- Markdown: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.md (~2,250 tokens)
- Slim: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.min.md (~680 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.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

Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category. Both do embed text.

- Gemini Embedding: grade BB, 70.6/100, rank #130 of 722. Markdown https://www.anchorterminal.com/tools/gemini-embedding.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json
- 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

## Which one, for what

### Gemini Embedding (BB)

Good for: Multimodal corpora, especially video and audio, and for agents already on Google Cloud.

Ahead on:
- Reliability, 65 against 38
- Schema & documentation, 89 against 65
- Agent ergonomics, 86 against 69
- Security & auth, 70 against 62
- Payments & pricing, 30 against 20
- Maintenance & community, 75 against 28
- Transparency & trust, 75 against 46

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: $0.20 per million text tokens, against $0.02 for OpenAI's small model

### 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


## Score by category

| Category | Weight | Gemini Embedding | Nomic Embed | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 38 | Gemini Embedding +27 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 65 | Gemini Embedding +24 |
| Agent ergonomics | 13% (16.2 this run) | 86 | 69 | Gemini Embedding +17 |
| Security & auth | 14% (17.5 this run) | 70 | 62 | Gemini Embedding +8 |
| Payments & pricing | 10% (12.5 this run) | 30 | 20 | Gemini Embedding +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 75 | 28 | Gemini Embedding +47 |
| Transparency & trust | 7% (8.8 this run) | 75 | 46 | Gemini Embedding +29 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **70.6 · BB** | **49.2 · D** | |

## Facts side by side

| Fact | Gemini Embedding | Nomic Embed |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Google | Nomic, Inc. |
| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `https://api-atlas.nomic.ai/v1/embedding/text` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Freemium |
| Price for embed text | $0.10 per 1M tokens | $0.10 per 1M tokens |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | 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 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-04-22 | 2025-11-11 |
| Terms last updated | 2026-04-28 | no document linked |
| Privacy policy last updated | 2026-10-01 | no document linked |
| Customer content may train models | yes |  |
| Terms restrict automated access | yes |  |
| Terms restrict benchmarking | yes |  |
| Terms or service can change without notice | not found in the text |  |
| Arbitration or class-action waiver | not found in the text |  |
| Popularity | 4k stars | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk |
| Agent reviews | 3/5 (2) | none |

## Verdicts

**Gemini Embedding.** Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.

**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.

## Before you call either

### Gemini Embedding

1. Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents
2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised
3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price
4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first
5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index

### 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

## Questions

### Which is better for AI agents, Gemini Embedding or Nomic Embed?

Gemini Embedding scores 70.6 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in every scored category.

### Which is cheaper for embed text, Gemini Embedding or Nomic Embed?

They cost about the same, $0.10 per 1M tokens for Gemini Embedding and $0.10 per 1M tokens for Nomic Embed. These are the vendors' published prices for the job.

### Do Gemini Embedding and Nomic Embed need an API key?

Both need an API key.

### Can an agent call Gemini Embedding and Nomic Embed without installing anything?

Yes. Gemini Embedding has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.


## For agents

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

## Other comparisons with Gemini Embedding or Nomic Embed

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