# Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs

> NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security & auth. Both do embed text. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever
- Markdown: https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md (~2,750 tokens)
- Slim: https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.min.md (~830 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.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: 2026-10-09

NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security & auth. Both do embed text.

- Nomic Embed: grade D, 49.2/100, rank #711 of 842. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.json
- NVIDIA NeMo Retriever Embedding and Reranking NIMs: grade C, 61/100, rank #439 of 842. Markdown https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md · JSON https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.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.

Ahead on:
- Security & auth, 62 against 55

Also in its favour:
- A hosted endpoint, with nothing to install

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

### NVIDIA NeMo Retriever Embedding and Reranking NIMs (C)

Good for: Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.

Ahead on:
- Reliability, 53 against 38
- Schema & documentation, 78 against 65
- Payments & pricing, 40 against 20
- Maintenance & community, 57 against 28
- Transparency & trust, 71 against 46

Also in its favour:
- No key needed to call it

Watch for: The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs


## Score by category

| Category | Weight | Nomic Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 38 | 53 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +15 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 65 | 78 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +13 |
| Agent ergonomics | 13% (16.2 this run) | 69 | 73 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +4 |
| Security & auth | 14% (17.5 this run) | 62 | 55 | Nomic Embed +7 |
| Payments & pricing | 10% (12.5 this run) | 20 | 40 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +20 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 28 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +29 |
| Transparency & trust | 7% (8.8 this run) | 46 | 71 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +25 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **49.2 · D** | **61 · C** | |

## Facts side by side

| Fact | Nomic Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Nomic, Inc. | NVIDIA |
| Hosted endpoint | `https://api-atlas.nomic.ai/v1/embedding/text` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | API key | None |
| Pricing | Freemium | Freemium |
| Price for embed text | $0.10 per 1M tokens | not published |
| 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 | Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-11-11 | 2026-08-05 |
| Terms last updated | no document linked | 2026-05-07 |
| Privacy policy last updated | no document linked | no date given |
| Customer content may train models |  | not found in the text |
| Terms restrict automated access |  | not found in the text |
| 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 | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk | 134k PyPI/wk |

## 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.

**NVIDIA NeMo Retriever Embedding and Reranking NIMs.** Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.

## 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

### NVIDIA NeMo Retriever Embedding and Reranking NIMs

1. Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs
2. Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`
3. Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues
4. Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026
5. Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field

## Questions

### Which is better for AI agents, Nomic Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?

NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security & auth.

### Do Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?

Nomic Embed needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.

### Can an agent call Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?

Nomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.


## For agents

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

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- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)
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- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.md)
- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.md)
- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md)