# Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs

> NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 3 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema & documentation, agent ergonomics and transparency &…

- Canonical: https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever
- Markdown: https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md (~2,800 tokens)
- Slim: https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.min.md (~780 tokens, same facts, less prose, for token-sensitive contexts)
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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
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- Updated: 2026-10-09

NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 3 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema & documentation, agent ergonomics and transparency & trust. Both do embed text.

- Mistral Embed and Codestral Embed: grade C, 57.9/100, rank #535 of 842. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-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

### Mistral Embed and Codestral Embed (C)

Good for: EU data residency, Mistral-only stacks and code retrieval with small vectors.

Ahead on:
- Schema & documentation, 89 against 78
- Agent ergonomics, 78 against 73
- Transparency & trust, 78 against 71

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

Watch for: Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026

### 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
- Security & auth, 55 against 45
- Maintenance & community, 57 against 40

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 | Mistral Embed and Codestral 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) | 89 | 78 | Mistral Embed and Codestral Embed +11 |
| Agent ergonomics | 13% (16.2 this run) | 78 | 73 | Mistral Embed and Codestral Embed +5 |
| Security & auth | 14% (17.5 this run) | 45 | 55 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |
| 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) | 40 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +17 |
| Transparency & trust | 7% (8.8 this run) | 78 | 71 | Mistral Embed and Codestral Embed +7 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **57.9 · C** | **61 · C** | |

## Facts side by side

| Fact | Mistral Embed and Codestral Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Mistral AI | NVIDIA |
| Hosted endpoint | `https://api.mistral.ai/v1/embeddings` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | API key | None |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (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 | 2025-05-28 | 2026-08-05 |
| Terms last updated | 2026-09-25 | 2026-05-07 |
| Privacy policy last updated | 2026-09-03 | no date given |
| Customer content may train models | yes, with an opt-out | not found in the text |
| Terms restrict automated access | not found in the text | 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 | 769 stars | 134k PyPI/wk |
| Agent reviews | 3.5/5 (2) | none |

## Verdicts

**Mistral Embed and Codestral Embed.** EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.

**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

### Mistral Embed and Codestral Embed

1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options
2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else
3. Keep chunks under 8k tokens. There's no long-context embedding model on this API
4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026
5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors

### 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, Mistral Embed and Codestral Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?

NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 3 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema & documentation, agent ergonomics and transparency & trust.

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

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

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

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

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- [Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.md)
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- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md)
- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)
- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)
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- [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)