{ "data": { "a": { "slug": "mistral-embeddings", "name": "Mistral Embed and Codestral Embed", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "http-api", "category": "embeddings", "summary": "Mistral's API for generating text and code embeddings.", "url": "https://www.anchorterminal.com/tools/mistral-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json", "repo": "https://github.com/mistralai/client-python", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.mistral.ai/v1/embeddings", "packages": [ { "registry": "pypi", "name": "mistralai" }, { "registry": "npm", "name": "@mistralai/mistralai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from La Plateforme. Same key as the chat models. Regional EU and US endpoints are opt-in at 1.1 times the price.", "pricing": "freemium", "pricingNotes": "mistral-embed $0.10 and codestral-embed $0.15 per million input tokens (https://mistral.ai/pricing/api/). Batch processing at half price, regional endpoints 1.1x. The free Experiment tier needs a phone number, no card, and its data may be used for training (https://docs.mistral.ai/admin/user-management-finops/tier).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 769, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://docs.mistral.ai/capabilities/embeddings/overview", "llmsTxt": "https://docs.mistral.ai/llms.txt", "openapi": "https://docs.mistral.ai/openapi.yaml", "capabilities": [ "embed.text", "embed.code" ], "tags": [ "official", "hosted", "freemium", "free-tier", "eu", "openapi", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2025-05-28", "graded": true, "anchor": { "graded": true, "score": 57.9, "grade": "C", "agentReady": false, "rank": 535, "ranked": true, "rankOf": 842, "categoryRank": 8, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 78 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "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.", "bestFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.", "strengths": [ "EU and US regional endpoints and a French legal entity", "codestral-embed with up to 3072 dimensions, first-n truncation and int8 or binary output", "OpenAPI document and llms.txt for the whole API", "Free Experiment tier with no card, and batch at half price", "Same key, billing and SDKs as Mistral's chat models" ], "weaknesses": [ "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026", "8k context on both models, and text or code only", "mistral-embed dates from December 2023 with fixed 1024-dimension float output, and nothing new since May 2025", "No reranker, no published rate limits and no language list for the embedding models", "Free-tier data may be used for training" ], "agentNotes": [ "Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options", "Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else", "Keep chunks under 8k tokens. There's no long-context embedding model on this API", "Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026", "Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.9 } ], "editorialScores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 65 }, "provenanceScore": 91 }, "connect": { "install": "pip install mistralai # or: npm i @mistralai/mistralai", "http": "curl -X POST https://api.mistral.ai/v1/embeddings \\\n -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"codestral-embed\",\"input\":[\"def two_sum(nums, target): ...\"],\"output_dimension\":512,\"output_dtype\":\"int8\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/mistral-embeddings" }, "sameCompany": [ "mistral-api", "mistral-moderation", "mistral-voxtral-transcribe", "mistral-ocr" ], "area": "models", "unitPrices": [ { "item": "mistral-embed", "unit": "1m-tokens", "usd": 0.1 }, { "item": "codestral-embed", "unit": "1m-tokens", "usd": 0.15 } ], "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": true, "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service", "privacy": "https://legal.mistral.ai/terms/privacy-policy", "statusPage": "https://status.mistral.ai", "changelog": "https://docs.mistral.ai/resources/changelogs", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same account, terms and data handling as the Mistral AI API listing. The embedding docs and the public docs repository were read on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "The model catalogue in the docs repository (mistralai/platform-docs-public) is the source for context length, release dates and prices." ], "score": 91 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json", "live": { "slug": "mistral-embeddings", "probe": { "target": "https://api.mistral.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-09T11:46:34.61700632Z", "lastOk": true, "lastStatus": 401, "lastMs": 58, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 58, "p95ms24h": 103, "samples24h": 259, "samples30d": 2109, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 125, "ok": 125 } ] }, "vendorStatus": { "page": "https://status.mistral.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-08T19:38:47.713113131Z" }, "versions": [ { "registry": "github", "name": "mistralai/client-python", "version": "v3.1.0", "released": "2026-10-06", "seenAt": "2026-10-08T16:21:17.2631398Z" }, { "registry": "npm", "name": "@mistralai/mistralai", "version": "2.7.0", "seenAt": "2026-10-08T16:21:16.999265692Z" }, { "registry": "pypi", "name": "mistralai", "version": "3.1.0", "released": "2026-10-06", "seenAt": "2026-10-08T16:21:16.88093713Z" } ], "githubStars": 773, "npmWeekly": 9359288, "pypiWeekly": 3274123, "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-10-08T15:38:55.944328005Z" }, "llmsTxt": { "url": "https://docs.mistral.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:39.19683216Z" }, "domain": { "domain": "mistral.ai", "registered": "2019-05-15", "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai", "checkedAt": "2026-10-04T13:08:59.683466691Z" }, "updatedAt": "2026-10-09T11:46:34.61700632Z" } }, "answer": "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 \u0026 documentation, agent ergonomics and transparency \u0026 trust.", "b": { "slug": "nvidia-nemo-retriever", "name": "NVIDIA NeMo Retriever Embedding and Reranking NIMs", "vendor": "NVIDIA", "vendorUrl": "https://www.nvidia.com", "kind": "http-api", "category": "embeddings", "summary": "NVIDIA's NeMo Retriever Embedding and Reranking NIMs are GPU containers that run text and image embedding models and rerankers behind a local REST API, with `/v1/embeddings` in the OpenAI shape and `/v1/ranking`.", "url": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever", "markdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json", "license": "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", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "langchain-nvidia-ai-endpoints" } ], "auth": "none", "authNotes": "The NIM's own API takes no credential. NVIDIA's security page says the deployer must secure the endpoints and suggests a proxy with HTTPS. Pulling images from `nvcr.io` needs a personal NGC API key, created by a person at org.ngc.nvidia.com and sent as the password for the user `$oauthtoken`. Model weights download from Hugging Face by default with `HF_TOKEN`, or from NGC with `NGC_API_KEY`. Some models need their licence terms accepted on the NGC catalogue page first. TLS is built in through `NIM_SERVER_TLS_CERT_PATH` and `NIM_SERVER_TLS_KEY_PATH`.", "pricing": "freemium", "pricingNotes": "Free for research, development and testing on up to 16 GPUs through the NVIDIA developer programme, with no card. Production use needs NVIDIA AI Enterprise, listed at $4,500 a GPU a year through partners or $1 a GPU-hour on AWS, Azure, Google Cloud and Oracle marketplaces, plus the instance. A 90-day AI Enterprise trial licence is available on request. The hosted trial endpoints on build.nvidia.com are free for prototyping (https://docs.nvidia.com/ai-enterprise/planning-resource/licensing-guide/latest/pricing.html, checked 2026-10-08).", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the NIM docs, the OpenAPI files or the AI Enterprise pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 133630, "asOf": "2026-10-08" }, "docsUrl": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/overview.html", "openapi": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/_downloads/9957cfb9472fcf23c93820c1922c4343/openai-api.openapi.yaml", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "rerank" ], "tags": [ "self-hosted", "docker", "kubernetes", "gpu", "openai-compatible", "openapi", "proprietary", "enterprise", "free-for-development", "multimodal" ], "lastRelease": "2026-08-05", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 439, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "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.", "bestFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "strengths": [ "OpenAPI 3.1 files for both services, with enums for `input_type`, `modality`, `embedding_type` and `truncate` and no extra properties allowed", "`/v1/embeddings` follows the OpenAI shape, and a `-query` or `-passage` model suffix replaces `input_type` for OpenAI clients", "Output can be shrunk with `dimensions` from 128 to 2048 on three models, or with `int8`, `uint8`, `binary` and `ubinary` types", "NGC images are signed, built for amd64 and arm64, and were last scanned on 5 October 2026 per the NGC registry record", "The AI Enterprise lifecycle pages give support periods per branch and an end-of-life table with dates" ], "weaknesses": [ "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs", "Production use needs an NVIDIA AI Enterprise licence, $4,500 a GPU a year or $1 a GPU-hour on cloud marketplaces, plus the GPU", "Release notes carry no dates, and environment variable names changed between 2.0 and 2.3 without a note in the 2.2 or 2.3 notes we read", "The NVIDIA Software Licence Agreement forbids disclosing benchmark results without written permission, apart from a published exception", "Closed-source runtime with no public issue tracker or test suite, and no llms.txt entry or Markdown pages for these docs" ], "agentNotes": [ "Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs", "Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`", "Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues", "Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026", "Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 59 }, "provenanceScore": 83 }, "connect": { "install": "echo \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\ndocker run -it --rm --runtime=nvidia --gpus all --shm-size=16GB -e HF_TOKEN -v ~/.cache/nim/cache:/opt/cache -v ~/.cache/nim/weights:/model -u $(id -u) -p 8000:8000 nvcr.io/nim/nvidia/nemotron-3-embed-1b:2.3", "http": "curl -X POST http://localhost:8000/v1/embeddings \\\n -H 'accept: application/json' -H 'Content-Type: application/json' \\\n -d '{\"input\":[\"What is NVIDIA?\"],\"model\":\"nvidia/nemotron-3-embed-1b\",\"input_type\":\"query\",\"modality\":\"text\",\"embedding_type\":\"float\",\"encoding_format\":\"float\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/nvidia-nemo-retriever" }, "sameCompany": [ "nemo-guardrails" ], "area": "models", "unitPrices": [ { "item": "NVIDIA AI Enterprise on a cloud marketplace, per GPU", "unit": "gpu-hour", "usd": 1, "note": "Licence only, plus the cloud instance. Self-managed systems are $4,500 a GPU a year" } ], "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "domainNote": "Self-hosted software. The API answers on the deployer's own host, port 8000 by default. Images come from nvcr.io and the docs are on docs.nvidia.com.", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/", "privacy": "https://www.nvidia.com/en-us/about-nvidia/privacy-policy/", "statusPage": "https://status.ngc.nvidia.com", "changelog": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/release-notes.html", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The governing terms page for the Embedding NIM names the NVIDIA Software Licence Agreement (version of 7 May 2026) and the Product-Specific Terms for AI Products (15 April 2026) for the container, with a separate model licence per model.", "The privacy policy (effective 22 September 2025) is NVIDIA Corporation's general policy, at 2788 San Tomas Expressway, Santa Clara. The model cards name it as the applicable privacy policy. No product-specific privacy document was found.", "www.nvidia.com/.well-known/security.txt answers 403 with an access-denied body and www.nvidia.com/security.txt answers 404. Vulnerability reports go to NVIDIA PSIRT.", "status.ngc.nvidia.com covers NGC, the registry the images are pulled from, and NVIDIA Build. It does not cover a self-hosted NIM.", "RDAP for nvidia.com gives a registration date of 1993-04-20.", "The hosted trial endpoints are on integrate.api.nvidia.com and ai.api.nvidia.com, under the NVIDIA API Trial Terms of Service, a PDF on assets.ngc.nvidia.com that we did not read." ], "score": 83 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.json", "live": { "slug": "nvidia-nemo-retriever", "vendorStatus": { "page": "https://status.ngc.nvidia.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:40:00.175922925Z" }, "updatedAt": "2026-10-09T11:40:00.175922925Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Mistral AI", "b": "NVIDIA", "name": "Vendor" }, { "a": "https://api.mistral.ai/v1/embeddings", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDK)", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2025-05-28", "b": "2026-08-05", "name": "Last release" }, { "a": "2026-09-25", "b": "2026-05-07", "name": "Terms last updated" }, { "a": "2026-09-03", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "769 stars", "b": "134k PyPI/wk", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "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 \u0026 documentation, agent ergonomics and transparency \u0026 trust.", "question": "Which is better for AI agents, Mistral Embed and Codestral Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?" }, { "answer": "Mistral Embed and Codestral Embed needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.", "question": "Do Mistral Embed and Codestral Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?" }, { "answer": "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.", "question": "Can an agent call Mistral Embed and Codestral Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 89 against 78", "Agent ergonomics, 78 against 73", "Transparency \u0026 trust, 78 against 71" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.", "slug": "mistral-embeddings", "watchFor": "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026" }, { "aheadOn": [ "Reliability, 53 against 38", "Security \u0026 auth, 55 against 45", "Maintenance \u0026 community, 57 against 40" ], "also": [ "No key needed to call it" ], "goodFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "slug": "nvidia-nemo-retriever", "watchFor": "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.json", "title": "Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json", "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.json", "title": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.json", "title": "Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.json", "title": "Gemini Embedding vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.json", "title": "Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.json", "title": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.json", "title": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json", "title": "Mistral Embed and Codestral Embed vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.json", "title": "Mistral Embed and Codestral Embed vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.json", "title": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.json", "title": "Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.json", "title": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy" } ], "scores": [ { "by": 15, "edge": "nvidia-nemo-retriever", "key": "reliability", "mistral-embeddings": 38, "name": "Reliability", "nvidia-nemo-retriever": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 11, "edge": "mistral-embeddings", "key": "schema", "mistral-embeddings": 89, "name": "Schema \u0026 documentation", "nvidia-nemo-retriever": 78, "weight": 13 }, { "by": 5, "edge": "mistral-embeddings", "key": "ergonomics", "mistral-embeddings": 78, "name": "Agent ergonomics", "nvidia-nemo-retriever": 73, "weight": 13 }, { "by": 10, "edge": "nvidia-nemo-retriever", "key": "security", "mistral-embeddings": 45, "name": "Security \u0026 auth", "nvidia-nemo-retriever": 55, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "mistral-embeddings": 40, "name": "Payments \u0026 pricing", "nvidia-nemo-retriever": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 17, "edge": "nvidia-nemo-retriever", "key": "maintenance", "mistral-embeddings": 40, "name": "Maintenance \u0026 community", "nvidia-nemo-retriever": 57, "weight": 7 }, { "by": 7, "edge": "mistral-embeddings", "key": "transparency", "mistral-embeddings": 78, "name": "Transparency \u0026 trust", "nvidia-nemo-retriever": 71, "weight": 7 } ], "summary": "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 \u0026 documentation, agent ergonomics and transparency \u0026 trust. Both do embed text.", "verdicts": { "mistral-embeddings": "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": "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever", "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md", "slim": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.min.md" }, "markdown": "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 \u0026 documentation, agent ergonomics and transparency \u0026 trust. Both do embed text.\n\n- 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\n- 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\n\n## Which one, for what\n\n### Mistral Embed and Codestral Embed (C)\n\nGood for: EU data residency, Mistral-only stacks and code retrieval with small vectors.\n\nAhead on:\n- Schema \u0026 documentation, 89 against 78\n- Agent ergonomics, 78 against 73\n- Transparency \u0026 trust, 78 against 71\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch 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\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs (C)\n\nGood for: Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.\n\nAhead on:\n- Reliability, 53 against 38\n- Security \u0026 auth, 55 against 45\n- Maintenance \u0026 community, 57 against 40\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs\n\n\n## Score by category\n\n| Category | Weight | Mistral Embed and Codestral Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 53 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 78 | Mistral Embed and Codestral Embed +11 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 73 | Mistral Embed and Codestral Embed +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 55 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 40 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 40 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +17 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 78 | 71 | Mistral Embed and Codestral Embed +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.9 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Embed and Codestral Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Mistral AI | NVIDIA |\n| Hosted endpoint | `https://api.mistral.ai/v1/embeddings` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| 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 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2025-05-28 | 2026-08-05 |\n| Terms last updated | 2026-09-25 | 2026-05-07 |\n| Privacy policy last updated | 2026-09-03 | no date given |\n| Customer content may train models | yes, with an opt-out | not found in the text |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 769 stars | 134k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**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.\n\n**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.\n\n## Before you call either\n\n### Mistral Embed and Codestral Embed\n\n1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options\n2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else\n3. Keep chunks under 8k tokens. There's no long-context embedding model on this API\n4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026\n5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n1. 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\n2. Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`\n3. 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\n4. 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\n5. 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\n\n## Questions\n\n### Which is better for AI agents, Mistral Embed and Codestral Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?\n\nNVIDIA 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 \u0026 documentation, agent ergonomics and transparency \u0026 trust.\n\n### Do Mistral Embed and Codestral Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?\n\nMistral Embed and Codestral Embed needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.\n\n### Can an agent call Mistral Embed and Codestral Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?\n\nMistral 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.\n\n\n## For agents\n\n- 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\n- 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`\n- 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\n\n## Other comparisons with Mistral Embed and Codestral Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n- [Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.md)\n- [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)\n- [Cohere Embed and Rerank vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.md)\n- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.md)\n- [Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.md)\n- [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)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "" } ], "description": "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 \u0026 documentation, agent ergonomics and transparency \u0026…", "facts": [ "Mistral Embed and Codestral Embed C 57.9", "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "scores" ], "h1": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "image": "https://www.anchorterminal.com/assets/og/compare-mistral-embeddings-vs-nvidia-nemo-retriever.png", "path": "/compare/mistral-embeddings-vs-nvidia-nemo-retriever", "published": "2026-10-01", "section": "tools", "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding…", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever" }, "tokens": { "markdown": 2800, "slim": 780 }, "version": 1 }