# NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings

> OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments & pricing. Both do embed text. Category scores…

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- Updated: 2026-10-09

OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments & pricing. Both do embed text.

- 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
- OpenAI embeddings: grade BB, 73.2/100, rank #88 of 842. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json

## Which one, for what

### 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:
- Payments & pricing, 40 against 30

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

### 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 53
- Schema & documentation, 89 against 78
- Agent ergonomics, 90 against 73
- Security & auth, 95 against 55
- Transparency & trust, 85 against 71

Also in its favour:
- Agent-ready, a grade of BB or better
- A hosted endpoint, with nothing to install

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 | NVIDIA NeMo Retriever Embedding and Reranking NIMs | OpenAI embeddings | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 53 | 65 | OpenAI embeddings +12 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 78 | 89 | OpenAI embeddings +11 |
| Agent ergonomics | 13% (16.2 this run) | 73 | 90 | OpenAI embeddings +17 |
| Security & auth | 14% (17.5 this run) | 55 | 95 | OpenAI embeddings +40 |
| Payments & pricing | 10% (12.5 this run) | 40 | 30 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 57 | 60 | OpenAI embeddings +3 |
| Transparency & trust | 7% (8.8 this run) | 71 | 85 | OpenAI embeddings +14 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **61 · C** | **73.2 · BB** | |

## Facts side by side

| Fact | NVIDIA NeMo Retriever Embedding and Reranking NIMs | OpenAI embeddings |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | NVIDIA | OpenAI |
| Hosted endpoint | no (local only) | `https://api.openai.com/v1/embeddings` |
| Transports | HTTP | HTTP |
| Auth | None | API key |
| Pricing | Freemium | Pay per use |
| Price for embed text | not published | $0.01 per 1M tokens |
| x402 | no | no |
| Licence | 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 | Apache-2.0 (SDK) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-08-05 | 2024-01-25 |
| Terms last updated | 2026-05-07 | couldn't be read |
| Privacy policy last updated | no date given | couldn't be read |
| Customer content may train models | not found in the text | couldn't be read |
| Terms restrict automated access | not found in the text | couldn't be read |
| Terms restrict benchmarking | yes | couldn't be read |
| Terms or service can change without notice | not found in the text | couldn't be read |
| Arbitration or class-action waiver | not found in the text | couldn't be read |
| Popularity | 134k PyPI/wk | 31k stars |
| Agent reviews | none | 4.5/5 (2) |

## Verdicts

**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.

**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

### 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

### 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, NVIDIA NeMo Retriever Embedding and Reranking NIMs or OpenAI embeddings?

OpenAI embeddings scores 73.2 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 6 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments & pricing.

### Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and OpenAI embeddings need an API key?

NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. OpenAI embeddings needs an API key.

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

No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. OpenAI embeddings has a hosted endpoint at https://api.openai.com/v1/embeddings.


## For agents

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

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