# Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs

> Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security & auth, payments & pricing and transparency & trust.…

- Canonical: https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever
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- Updated: 2026-10-09

Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security & auth, payments & pricing and transparency & trust. Both do embed text.

- Jina Embeddings and Reranker: grade C, 61/100, rank #438 of 842. Markdown https://www.anchorterminal.com/tools/jina-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings.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

### Jina Embeddings and Reranker (C)

Good for: Reranking large candidate sets and multimodal corpora with audio or video.

Ahead on:
- Reliability, 65 against 53
- Schema & documentation, 84 against 78
- Agent ergonomics, 86 against 73
- Maintenance & community, 62 against 57

Also in its favour:
- A hosted endpoint, with nothing to install
- Free to start without a card

Watch for: No price per token in any currency on the public 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:
- Security & auth, 55 against 35
- Payments & pricing, 40 against 30
- Transparency & trust, 71 against 58

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 | Jina Embeddings and Reranker | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 53 | Jina Embeddings and Reranker +12 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 84 | 78 | Jina Embeddings and Reranker +6 |
| Agent ergonomics | 13% (16.2 this run) | 86 | 73 | Jina Embeddings and Reranker +13 |
| Security & auth | 14% (17.5 this run) | 35 | 55 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +20 |
| Payments & pricing | 10% (12.5 this run) | 30 | 40 | 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) | 62 | 57 | Jina Embeddings and Reranker +5 |
| Transparency & trust | 7% (8.8 this run) | 58 | 71 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | Jina Embeddings and Reranker | NVIDIA NeMo Retriever Embedding and Reranking NIMs |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Jina AI (Elastic) | NVIDIA |
| Hosted endpoint | `https://api.jina.ai/v1/embeddings` | no (local only) |
| Transports | HTTP, Streamable HTTP | HTTP |
| Auth | API key | None |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (MCP server) | 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 |
| Tools exposed | 12 | none |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-09-18 | 2026-08-05 |
| Terms last updated | 2026-05-04 | 2026-05-07 |
| Privacy policy last updated | 2026-09-07 | no date given |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | yes |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | not found in the text | not found in the text |
| Popularity | 841 stars | 134k PyPI/wk |
| Agent reviews | 3/5 (2) | none |

## Verdicts

**Jina Embeddings and Reranker.** jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.

**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

### Jina Embeddings and Reranker

1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks
2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise
3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls
4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings
5. Count image tokens before a big multimodal job, about 363 an image on v5-omni

### 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, Jina Embeddings and Reranker or NVIDIA NeMo Retriever Embedding and Reranking NIMs?

Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on security & auth, payments & pricing and transparency & trust.

### Do Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?

Jina Embeddings and Reranker needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.

### Can an agent call Jina Embeddings and Reranker and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?

Jina Embeddings and Reranker has a hosted endpoint at https://api.jina.ai/v1/embeddings. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.


## For agents

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

## Other comparisons with Jina Embeddings and Reranker or NVIDIA NeMo Retriever Embedding and Reranking NIMs

- [Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.md)
- [Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md)
- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)
- [Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)
- [Gemini Embedding vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/gemini-embedding-vs-jina-embeddings.md)
- [Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.md)
- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-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)
- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)
- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.md)
- [Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)
- [Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md)
- [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)