{ "data": { "a": { "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" } }, "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category.", "b": { "slug": "zeroentropy", "name": "ZeroEntropy zerank and zembed", "vendor": "ZeroEntropy", "vendorUrl": "https://www.zeroentropy.dev", "kind": "http-api", "category": "embeddings", "summary": "Discontinued retrieval API acquired by Notion. Its embedding and reranking models remain available as open weights for self-hosting.", "url": "https://www.anchorterminal.com/tools/zeroentropy", "markdownUrl": "https://www.anchorterminal.com/tools/zeroentropy.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zeroentropy.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zeroentropy.json", "repo": "https://github.com/zeroentropy-ai/zeroentropy-python", "license": "Apache-2.0 (SDKs and model weights)", "transports": [ "http" ], "remoteUrl": "https://api.zeroentropy.dev/v1/models/rerank", "packages": [ { "registry": "pypi", "name": "zeroentropy" }, { "registry": "npm", "name": "zeroentropy" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from dashboard.zeroentropy.dev. The SDKs read `ZEROENTROPY_API_KEY`. An EU endpoint at eu-api.zeroentropy.dev takes the same key.", "pricing": "usage", "pricingNotes": "No longer for sale. The API was supported until 2026-09-04 and new signups closed on 2026-07-24 (https://www.zeroentropy.dev/articles/zeroentropy-is-joining-notion/). The docs and pricing page still show the old self-serve prices, $0.025 per million tokens for zerank models and $0.05 for zembed-1. The weights are free to self-host under Apache 2.0.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 221378, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://docs.zeroentropy.dev/models", "capabilities": [ "rerank", "embed.text", "embed.multilingual" ], "tags": [ "retired", "superseded", "open-weights" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 13.7, "grade": "F", "agentReady": false, "rank": 839, "ranked": true, "rankOf": 842, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 20, "maintenance": 5, "payments": 0, "reliability": 0, "schema": 31, "security": 25, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -4, "negativeNotes": [ "The API was discontinued after 2026-09-04 per ZeroEntropy's own acquisition notice of 2026-07-24, but on 2026-10-01 the models page (https://docs.zeroentropy.dev/models) and pricing page (https://www.zeroentropy.dev/pricing) still list self-serve per-token prices and API access without mentioning the shutdown. Endpoint removed while still advertised (-4). The shutdown notice is at https://www.zeroentropy.dev/articles/zeroentropy-is-joining-notion/" ], "verdict": "All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.", "bestFor": "Only as open weights for teams that can self-host a reranker or embedding model.", "strengths": [ "All four models now open weights under Apache 2.0 on Hugging Face", "A migration guide with self-hosting recipes for Baseten and Modal and named hosted alternatives", "42 days' notice before the API was retired", "Migration support promised over Slack, Discord and email" ], "weaknesses": [ "The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026", "The docs and pricing page still advertise per-token API prices without mentioning the shutdown", "Nothing published on what happens to customer documents after the shutdown", "No status page, changelog or OpenAPI file" ], "agentNotes": [ "Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says", "Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model", "Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage", "Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 1, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 13.7 } ], "editorialScores": { "ergonomics": 20, "maintenance": 5, "payments": 0, "reliability": 0, "schema": 31, "security": 25, "transparency": 45 }, "provenanceScore": 58 }, "connect": { "install": "pip install zeroentropy # or: npm i zeroentropy", "http": "curl -X POST https://api.zeroentropy.dev/v1/models/rerank \\\n -H \"Authorization: Bearer $ZEROENTROPY_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"zerank-2\",\"query\":\"reranker price per million tokens\",\"documents\":[\"zerank-2 costs $0.025 per million tokens.\",\"The office is in California.\"],\"top_n\":1}'" }, "letme": { "capability": "https://letme.dev/rerank", "tool": "https://letme.dev/zeroentropy" }, "supersededBy": [ "cohere-embed", "voyage-ai", "openai-embeddings" ], "area": "models", "unitPrices": [ { "item": "zerank-2 reranker", "unit": "1m-tokens", "usd": 0.025, "note": "Same price for zerank-1 and zerank-1-small" }, { "item": "zembed-1 embeddings", "unit": "1m-tokens", "usd": 0.05 } ], "provenance": { "legalEntity": "ZeroEntropy, Inc.", "domain": "zeroentropy.dev", "domainRegistered": "2024-09-02", "endpointOnVendorDomain": true, "terms": "https://www.zeroentropy.dev/terms", "privacy": "https://www.zeroentropy.dev/privacy", "statusPage": "", "changelog": "", "securityTxt": "unknown", "checked": "2026-09-30", "notes": [ "The privacy policy (2025-11-04) names ZeroEntropy, Inc., a Delaware corporation based in California, with no postal address. The terms (last revised 2025-10-07) name no entity and no governing law.", "The terms grant ZeroEntropy a licence to process and store submitted documents to provide the service and for internal improvement purposes.", "The proxy refused our fetch of security.txt, the docs llms.txt and the GitHub SDK page with a rate limit, so those are unchecked. The SDK's public repository was cloned instead.", "The embed rate-limit figures come from the SDK's docstrings, the rerank figures from the API reference." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/zeroentropy.json", "live": { "slug": "zeroentropy", "probe": { "target": "https://api.zeroentropy.dev/v1/models/rerank", "method": "get", "lastAt": "2026-10-09T11:46:47.056349434Z", "lastOk": false, "lastStatus": 503, "lastMs": 461, "lastNote": "server error", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 259, "samples30d": 2109, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 272, "ok": 0 }, { "date": "2026-10-05", "probes": 272, "ok": 0 }, { "date": "2026-10-06", "probes": 272, "ok": 0 }, { "date": "2026-10-07", "probes": 272, "ok": 0 }, { "date": "2026-10-08", "probes": 268, "ok": 0 }, { "date": "2026-10-09", "probes": 125, "ok": 0 } ] }, "versions": [ { "registry": "npm", "name": "zeroentropy", "version": "0.1.0-alpha.10", "seenAt": "2026-10-08T16:35:52.167598947Z" }, { "registry": "pypi", "name": "zeroentropy", "version": "0.1.0a11", "released": "2026-03-03", "seenAt": "2026-10-08T16:35:51.980267289Z" } ], "githubStars": 24, "npmWeekly": 242346, "pypiWeekly": 19946, "securityTxt": { "url": "https://zeroentropy.dev/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:41.659103904Z" }, "domain": { "domain": "zeroentropy.dev", "registered": "2024-09-02", "source": "https://pubapi.registry.google/rdap/domain/zeroentropy.dev", "checkedAt": "2026-10-04T13:04:15.835541457Z" }, "pages": [ { "url": "https://www.zeroentropy.dev/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-08T18:31:50.678044456Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e17c53db6505" }, { "url": "https://www.zeroentropy.dev/terms", "kind": "terms", "status": 304, "checkedAt": "2026-10-08T18:31:52.821689525Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "f3a8554b48fc" } ], "updatedAt": "2026-10-09T11:46:47.056349434Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "NVIDIA", "b": "ZeroEntropy", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.zeroentropy.dev/v1/models/rerank", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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", "b": "Apache-2.0 (SDKs and model weights)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-05", "b": "2026-03-02", "name": "Last release" }, { "a": "2026-05-07", "b": "2025-10-07", "name": "Terms last updated" }, { "a": "no date given", "b": "2025-11-04", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "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": "134k PyPI/wk", "b": "221k npm/wk", "name": "Popularity" }, { "a": "none", "b": "1/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category.", "question": "Which is better for AI agents, NVIDIA NeMo Retriever Embedding and Reranking NIMs or ZeroEntropy zerank and zembed?" }, { "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. ZeroEntropy zerank and zembed needs an API key.", "question": "Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and ZeroEntropy zerank and zembed need an API key?" }, { "answer": "No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. ZeroEntropy zerank and zembed has a hosted endpoint at https://api.zeroentropy.dev/v1/models/rerank.", "question": "Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and ZeroEntropy zerank and zembed without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 53 against 0", "Schema \u0026 documentation, 78 against 31", "Agent ergonomics, 73 against 20", "Security \u0026 auth, 55 against 25", "Payments \u0026 pricing, 40 against 0", "Maintenance \u0026 community, 57 against 5", "Transparency \u0026 trust, 71 against 52" ], "also": [ "No key needed to call it", "No incidents deducted, where ZeroEntropy zerank and zembed loses 4 points for them" ], "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" }, { "aheadOn": null, "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Only as open weights for teams that can self-host a reranker or embedding model.", "slug": "zeroentropy", "watchFor": "The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "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/amazon-nova-embeddings-vs-zeroentropy.json", "title": "Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy" }, { "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/cohere-embed-vs-zeroentropy.json", "title": "Cohere Embed and Rerank vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy" }, { "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/gemini-embedding-vs-zeroentropy.json", "title": "Gemini Embedding vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy" }, { "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/jina-embeddings-vs-zeroentropy.json", "title": "Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json", "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever" }, { "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/nomic-embed-vs-zeroentropy.json", "title": "Nomic Embed vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy" }, { "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/openai-embeddings-vs-zeroentropy.json", "title": "OpenAI embeddings vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy" }, { "json": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.json", "title": "Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy" } ], "scores": [ { "by": 53, "edge": "nvidia-nemo-retriever", "key": "reliability", "name": "Reliability", "nvidia-nemo-retriever": 53, "weight": 16, "zeroentropy": 0 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 47, "edge": "nvidia-nemo-retriever", "key": "schema", "name": "Schema \u0026 documentation", "nvidia-nemo-retriever": 78, "weight": 13, "zeroentropy": 31 }, { "by": 53, "edge": "nvidia-nemo-retriever", "key": "ergonomics", "name": "Agent ergonomics", "nvidia-nemo-retriever": 73, "weight": 13, "zeroentropy": 20 }, { "by": 30, "edge": "nvidia-nemo-retriever", "key": "security", "name": "Security \u0026 auth", "nvidia-nemo-retriever": 55, "weight": 14, "zeroentropy": 25 }, { "by": 40, "edge": "nvidia-nemo-retriever", "key": "payments", "name": "Payments \u0026 pricing", "nvidia-nemo-retriever": 40, "weight": 10, "zeroentropy": 0 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 52, "edge": "nvidia-nemo-retriever", "key": "maintenance", "name": "Maintenance \u0026 community", "nvidia-nemo-retriever": 57, "weight": 7, "zeroentropy": 5 }, { "by": 19, "edge": "nvidia-nemo-retriever", "key": "transparency", "name": "Transparency \u0026 trust", "nvidia-nemo-retriever": 71, "weight": 7, "zeroentropy": 52 } ], "summary": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category. Both do embed text.", "verdicts": { "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.", "zeroentropy": "All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy", "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md", "slim": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.min.md" }, "markdown": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category. Both do embed text.\n\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- ZeroEntropy zerank and zembed: grade F, 13.7/100, rank #839 of 842. Markdown https://www.anchorterminal.com/tools/zeroentropy.md · JSON https://www.anchorterminal.com/api/v1/tools/zeroentropy.json\n\n## Which one, for what\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 0\n- Schema \u0026 documentation, 78 against 31\n- Agent ergonomics, 73 against 20\n- Security \u0026 auth, 55 against 25\n- Payments \u0026 pricing, 40 against 0\n- Maintenance \u0026 community, 57 against 5\n- Transparency \u0026 trust, 71 against 52\n\nAlso in its favour:\n- No key needed to call it\n- No incidents deducted, where ZeroEntropy zerank and zembed loses 4 points for them\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### ZeroEntropy zerank and zembed (F)\n\nGood for: Only as open weights for teams that can self-host a reranker or embedding model.\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026\n\n\n## Score by category\n\n| Category | Weight | NVIDIA NeMo Retriever Embedding and Reranking NIMs | ZeroEntropy zerank and zembed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 53 | 0 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +53 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 31 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +47 |\n| Agent ergonomics | 13% (16.2 this run) | 73 | 20 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +53 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 25 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +30 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 0 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 57 | 5 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +52 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 52 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +19 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **61 · C** | **13.7 · F** | |\n\n## Facts side by side\n\n| Fact | NVIDIA NeMo Retriever Embedding and Reranking NIMs | ZeroEntropy zerank and zembed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | NVIDIA | ZeroEntropy |\n| Hosted endpoint | no (local only) | `https://api.zeroentropy.dev/v1/models/rerank` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| 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 (SDKs and model weights) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-05 | 2026-03-02 |\n| Terms last updated | 2026-05-07 | 2025-10-07 |\n| Privacy policy last updated | no date given | 2025-11-04 |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 134k PyPI/wk | 221k npm/wk |\n| Agent reviews | none | 1/5 (2) |\n\n## Verdicts\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**ZeroEntropy zerank and zembed.** All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.\n\n## Before you call either\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### ZeroEntropy zerank and zembed\n\n1. Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says\n2. Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model\n3. Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage\n4. Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible\n\n## Questions\n\n### Which is better for AI agents, NVIDIA NeMo Retriever Embedding and Reranking NIMs or ZeroEntropy zerank and zembed?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category.\n\n### Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and ZeroEntropy zerank and zembed need an API key?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. ZeroEntropy zerank and zembed needs an API key.\n\n### Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and ZeroEntropy zerank and zembed without installing anything?\n\nNo hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. ZeroEntropy zerank and zembed has a hosted endpoint at https://api.zeroentropy.dev/v1/models/rerank.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nvidia-nemo-retriever\", \"b\": \"zeroentropy\"}`. From a terminal: `anchor compare nvidia-nemo-retriever zeroentropy`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json and https://www.anchorterminal.com/api/v1/tools/zeroentropy.json\n\n## Other comparisons with NVIDIA NeMo Retriever Embedding and Reranking NIMs or ZeroEntropy zerank and zembed\n\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- [Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy.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- [Cohere Embed and Rerank vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.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- [Gemini Embedding vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy.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- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.md)\n- [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)\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- [Nomic Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nomic-embed-vs-zeroentropy.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- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.md)\n- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-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": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed", "url": "" } ], "description": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against ZeroEntropy zerank and zembed's 13.7 (F), and leads in every scored category. Both do embed text. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "ZeroEntropy zerank and zembed F 13.7", "scores" ], "h1": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed", "image": "https://www.anchorterminal.com/assets/og/compare-nvidia-nemo-retriever-vs-zeroentropy.png", "path": "/compare/nvidia-nemo-retriever-vs-zeroentropy", "published": "2026-10-01", "section": "tools", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy…", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy" }, "tokens": { "markdown": 2700, "slim": 780 }, "version": 1 }