# Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs

> Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 5 of 7 scored categories. Both do embed text. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever
- Markdown: https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md (~2,700 tokens)
- Slim: https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.min.md (~780 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cohere-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

Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 5 of 7 scored categories. Both do embed text.

- Cohere Embed and Rerank: grade BB, 72.5/100, rank #100 of 842. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-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

### Cohere Embed and Rerank (BB)

Good for: Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.

Ahead on:
- Reliability, 73 against 53
- Schema & documentation, 92 against 78
- Agent ergonomics, 87 against 73
- Maintenance & community, 90 against 57

Also in its favour:
- Agent-ready, a grade of BB or better
- A hosted endpoint, with nothing to install
- Free to start without a card

Watch for: Terms, training notice and security page disagree on whether API data trains models or goes to third parties

### 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.

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 | Cohere Embed and Rerank | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 73 | 53 | Cohere Embed and Rerank +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 92 | 78 | Cohere Embed and Rerank +14 |
| Agent ergonomics | 13% (16.2 this run) | 87 | 73 | Cohere Embed and Rerank +14 |
| Security & auth | 14% (17.5 this run) | 55 | 55 | even |
| Payments & pricing | 10% (12.5 this run) | 40 | 40 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 90 | 57 | Cohere Embed and Rerank +33 |
| Transparency & trust | 7% (8.8 this run) | 72 | 71 | Cohere Embed and Rerank +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.5 · BB** | **61 · C** | |

## Facts side by side

| Fact | Cohere Embed and Rerank | NVIDIA NeMo Retriever Embedding and Reranking NIMs |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Cohere | NVIDIA |
| Hosted endpoint | `https://api.cohere.com/v2/embed` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | API key | None |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | MIT (SDK) | 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 | yes | no |
| Last release | 2026-09-30 | 2026-08-05 |
| Terms last updated | 2022-09-07 | 2026-05-07 |
| Privacy policy last updated | 2026-05-01 | no date given |
| Customer content may train models | yes | 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 | yes | not found in the text |
| Arbitration or class-action waiver | not found in the text | not found in the text |
| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 134k PyPI/wk |
| Agent reviews | 3.5/5 (2) | none |

## Verdicts

**Cohere Embed and Rerank.** Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.

**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

### Cohere Embed and Rerank

1. Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it
2. Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (the Python SDK merge drops types missing from the first response)
3. Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. One query with up to 100 documents is one search, and a document over 500 tokens counts as several
4. Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models
5. Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage

### 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, Cohere Embed and Rerank or NVIDIA NeMo Retriever Embedding and Reranking NIMs?

Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 5 of 7 scored categories.

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

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

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

Cohere Embed and Rerank has a hosted endpoint at https://api.cohere.com/v2/embed. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.


## For agents

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

## Other comparisons with Cohere Embed and Rerank or NVIDIA NeMo Retriever Embedding and Reranking NIMs

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