Head to head · Embed text · October 2026 research run
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.
Which one, for what
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 this run | Nomic Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 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 | 65 | 78 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +13 |
| Agent ergonomics | 13%16.2 | 69 | 73 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +4 |
| Security & auth | 14%17.5 | 62 | 55 | Nomic Embed +7 |
| Payments & pricing | 10%12.5 | 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 | 28 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +29 |
| Transparency & trust | 7%8.8 | 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
- Set task_type to search_query for queries and search_document for stored text. The default is search_document
- 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
- Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request
- Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API
- Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768
NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Send
input_typeasqueryorpassageon every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs - Do not send
dimensionsandembedding_typetogether, and send only 2048 or nothing fordimensionsonnvidia/nemotron-3-embed-1b - Poll
/v1/health/readybefore the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues - 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 - Put a proxy with authentication and TLS in front of port 8000, and sort
/v1/rankingresults 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.
Other comparisons with Nomic Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Amazon Nova Multimodal Embeddings vs Nomic Embed
- Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Cohere Embed and Rerank vs Nomic Embed
- Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Gemini Embedding vs Nomic Embed
- Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Jina Embeddings and Reranker vs Nomic Embed
- Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Mistral Embed and Codestral Embed vs Nomic Embed
- Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs
- Nomic Embed vs OpenAI embeddings
- Nomic Embed vs Voyage AI embeddings and rerankers
- Nomic Embed vs ZeroEntropy zerank and zembed
- NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings
- NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers
- NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed
Machine-readable
- This page as Markdown
/compare/nomic-embed-vs-nvidia-nemo-retriever.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nomic-embed.json·/api/v1/tools/nvidia-nemo-retriever.json - From a terminal
anchor compare nomic-embed nvidia-nemo-retriever(the CLI) - Over MCP
compare_tools {"a": "nomic-embed", "b": "nvidia-nemo-retriever"}at/mcp, no key