{ "data": { "a": { "slug": "nomic-embed", "name": "Nomic Embed", "vendor": "Nomic, Inc.", "vendorUrl": "https://www.nomic.ai", "kind": "http-api", "category": "embeddings", "summary": "Nomic's hosted embedding endpoints on the Atlas API turn text and images into vectors with the open-weight Nomic Embed models. Agents call them over HTTP with an API key or through the Python and TypeScript clients.", "url": "https://www.anchorterminal.com/tools/nomic-embed", "markdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nomic-embed.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nomic-embed.json", "repo": "https://github.com/nomic-ai/nomic", "license": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT", "transports": [ "http" ], "remoteUrl": "https://api-atlas.nomic.ai/v1/embedding/text", "packages": [ { "registry": "pypi", "name": "nomic" }, { "registry": "npm", "name": "@nomic-ai/atlas" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a Nomic API key created in the Atlas dashboard at atlas.nomic.ai/data. Keys are tied to a user and billed to the organisation, and can be scoped to an organisation, a dataset or a user. The clients also accept a refresh token. Sign-up is in a browser, and we couldn't confirm that new accounts are still accepted (https://docs.nomic.ai/api/getting-started/getting-started).", "pricing": "freemium", "pricingNotes": "No rendered public page prices the embedding endpoint. www.nomic.ai/pricing lists only the Nomic Platform plans (Free, Individual at $20 a month, Business at $40 a user a month). The Atlas web app's script lists a starter plan with 10M tokens free and paid plans at $1 per 10M tokens with 10M or 100M included a month, and images at $1 per 50,000. We couldn't render that page or confirm whether a card is needed (checked 2026-10-08).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the API reference, the OpenAPI document or the pricing pages (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 1881, "npmWeekly": 8551, "pypiWeekly": 3833, "asOf": "2026-10-08" }, "docsUrl": "https://docs.nomic.ai/reference/api/embed-text-v-1-embedding-text-post", "openapi": "https://api-atlas.nomic.ai/v1/api-reference/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "embed.code" ], "tags": [ "hosted", "freemium", "api-key", "openapi", "open-weights", "python", "typescript" ], "lastRelease": "2025-11-11", "graded": true, "anchor": { "graded": true, "score": 49.2, "grade": "D", "agentReady": false, "rank": 711, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 28, "payments": 20, "reliability": 38, "schema": 65, "security": 62, "transparency": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.", "bestFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.", "strengths": [ "Weights for nomic-embed-text-v1, v1.5, v2-moe, nomic-embed-code and nomic-embed-vision-v1.5 are Apache-2.0 on Hugging Face", "Public OpenAPI 3.1 document for the Atlas API (v0.57.0) with typed request and response schemas for both embedding endpoints", "nomic-embed-text-v1.5 accepts a dimensionality from 64 to 768, and inputs up to 8,192 tokens per text", "The Python client retries 429 and 5xx responses with exponential backoff and can run the same model locally with inference_mode set to local", "API keys can be scoped to an organisation, a dataset or a user, per the Atlas access-control page" ], "weaknesses": [ "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages", "No rendered public page states a price for the endpoint. The $1 per 10M tokens figure comes from the Atlas web app's script", "status.nomic.ai has no component for api-atlas.nomic.ai, and no SLA was found", "The published terms and privacy policy cover the Nomic Platform at app.nomic.ai and don't name Atlas or the embedding API", "The Python client's last release was 3.9.0 on 11 November 2025, and the API reference documents only a 422 error" ], "agentNotes": [ "Set task_type to search_query for queries and search_document for stored text. The default is search_document", "Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5", "Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request", "Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API", "Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.2 } ], "editorialScores": { "ergonomics": 69, "maintenance": 28, "payments": 20, "reliability": 38, "schema": 65, "security": 62, "transparency": 47 }, "provenanceScore": 45 }, "connect": { "install": "pip install nomic", "http": "curl -X POST https://api-atlas.nomic.ai/v1/embedding/text \\\n -H \"Authorization: Bearer $NOMIC_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"texts\":[\"The text you want to embed.\"],\"model\":\"nomic-embed-text-v1.5\",\"task_type\":\"search_document\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/nomic-embed" }, "sameCompany": [ "gpt4all" ], "area": "models", "unitPrices": [ { "item": "Text embedding tokens beyond the plan's monthly allowance", "unit": "1m-tokens", "usd": 0.1, "note": "$1 per 10M tokens, read from the Atlas web app's script, not a rendered pricing page" } ], "provenance": { "legalEntity": "Nomic, Inc.", "domain": "nomic.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/nomic-ai/nomic/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "terms and privacy are left out. Nomic publishes Terms of Service for Business and Enterprise accounts (20 April 2026), Individual Terms of Service for Free and Individual accounts (25 August 2026) and a privacy policy (15 January 2026) that covers www.nomic.ai and the platform at app.nomic.ai. None names Atlas, atlas.nomic.ai or the embedding API (https://www.nomic.ai/legal.json).", "Both sets of terms name Nomic, Inc., a Delaware corporation, under Delaware law.", "status.nomic.ai lists the Nomic Platform in four regions (drive.nomic.ai, au, eu and uk), each with a Nomic API and a Platform Web Service component. It has no component for api-atlas.nomic.ai, so statusPage is left empty.", "www.nomic.ai, docs.nomic.ai and api-atlas.nomic.ai each return 404 for /.well-known/security.txt. The security page gives security@nomic.ai for vulnerability reports.", "The changelog is the Python client's. No changelog for the Atlas API was found." ], "score": 45 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nomic-embed.json", "live": { "slug": "nomic-embed", "probe": { "target": "https://api-atlas.nomic.ai/v1/embedding/text", "method": "get", "lastAt": "2026-10-09T10:42:50.615075919Z", "lastOk": true, "lastStatus": 405, "lastMs": 485, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 431, "p95ms24h": 471, "samples24h": 184, "samples30d": 184, "days": [ { "date": "2026-10-08", "probes": 70, "ok": 70 }, { "date": "2026-10-09", "probes": 114, "ok": 114 } ] }, "pages": [ { "url": "https://raw.githubusercontent.com/nomic-ai/nomic/main/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:24:31.732147651Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "5cd028b474d0" } ], "updatedAt": "2026-10-09T10:42:50.615075919Z" } }, "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.", "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": "2026-10-09T10:41:50.255876868Z" }, "updatedAt": "2026-10-09T10:41:50.255876868Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Nomic, Inc.", "b": "NVIDIA", "name": "Vendor" }, { "a": "https://api-atlas.nomic.ai/v1/embedding/text", "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": "$0.10 per 1M tokens", "b": "not published", "name": "Price for embed text" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT", "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": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-11-11", "b": "2026-08-05", "name": "Last release" }, { "a": "no document linked", "b": "2026-05-07", "name": "Terms last updated" }, { "a": "no document linked", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk", "b": "134k PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.", "question": "Which is better for AI agents, Nomic Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?" }, { "answer": "Nomic Embed needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.", "question": "Do Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?" }, { "answer": "Nomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.", "question": "Can an agent call Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 62 against 55" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.", "slug": "nomic-embed", "watchFor": "docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages" }, { "aheadOn": [ "Reliability, 53 against 38", "Schema \u0026 documentation, 78 against 65", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 57 against 28", "Transparency \u0026 trust, 71 against 46" ], "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-nomic-embed.json", "title": "Amazon Nova Multimodal Embeddings vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed" }, { "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-nomic-embed.json", "title": "Cohere Embed and Rerank vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed" }, { "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-nomic-embed.json", "title": "Gemini Embedding vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed" }, { "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-nomic-embed.json", "title": "Jina Embeddings and Reranker vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed" }, { "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-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/nomic-embed-vs-openai-embeddings.json", "title": "Nomic Embed vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json", "title": "Nomic Embed vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai" }, { "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/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", "name": "Reliability", "nomic-embed": 38, "nvidia-nemo-retriever": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 13, "edge": "nvidia-nemo-retriever", "key": "schema", "name": "Schema \u0026 documentation", "nomic-embed": 65, "nvidia-nemo-retriever": 78, "weight": 13 }, { "by": 4, "edge": "nvidia-nemo-retriever", "key": "ergonomics", "name": "Agent ergonomics", "nomic-embed": 69, "nvidia-nemo-retriever": 73, "weight": 13 }, { "by": 7, "edge": "nomic-embed", "key": "security", "name": "Security \u0026 auth", "nomic-embed": 62, "nvidia-nemo-retriever": 55, "weight": 14 }, { "by": 20, "edge": "nvidia-nemo-retriever", "key": "payments", "name": "Payments \u0026 pricing", "nomic-embed": 20, "nvidia-nemo-retriever": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 29, "edge": "nvidia-nemo-retriever", "key": "maintenance", "name": "Maintenance \u0026 community", "nomic-embed": 28, "nvidia-nemo-retriever": 57, "weight": 7 }, { "by": 25, "edge": "nvidia-nemo-retriever", "key": "transparency", "name": "Transparency \u0026 trust", "nomic-embed": 46, "nvidia-nemo-retriever": 71, "weight": 7 } ], "summary": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.", "verdicts": { "nomic-embed": "The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.", "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/nomic-embed-vs-nvidia-nemo-retriever", "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md", "slim": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.min.md" }, "markdown": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text.\n\n- Nomic Embed: grade D, 49.2/100, rank #711 of 842. Markdown https://www.anchorterminal.com/tools/nomic-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/nomic-embed.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### Nomic Embed (D)\n\nGood for: Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.\n\nAhead on:\n- Security \u0026 auth, 62 against 55\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages\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- Schema \u0026 documentation, 78 against 65\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 57 against 28\n- Transparency \u0026 trust, 71 against 46\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 | Nomic 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) | 65 | 78 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +13 |\n| Agent ergonomics | 13% (16.2 this run) | 69 | 73 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 62 | 55 | Nomic Embed +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 40 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 28 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +29 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 46 | 71 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +25 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.2 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nomic Embed | NVIDIA NeMo Retriever Embedding and Reranking NIMs |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Nomic, Inc. | NVIDIA |\n| Hosted endpoint | `https://api-atlas.nomic.ai/v1/embedding/text` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Freemium | Freemium |\n| Price for embed text | $0.10 per 1M tokens | not published |\n| x402 | no | no |\n| Licence | Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT | 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 | no | no |\n| Last release | 2025-11-11 | 2026-08-05 |\n| Terms last updated | no document linked | 2026-05-07 |\n| Privacy policy last updated | no document linked | no date given |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | not found in the text |\n| Terms restrict benchmarking | | yes |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | not found in the text |\n| Popularity | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk | 134k PyPI/wk |\n\n## Verdicts\n\n**Nomic Embed.** The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.\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### Nomic Embed\n\n1. Set task_type to search_query for queries and search_document for stored text. The default is search_document\n2. Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5\n3. Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request\n4. Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API\n5. Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768\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, Nomic Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth.\n\n### Do Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?\n\nNomic Embed needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.\n\n### Can an agent call Nomic Embed and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?\n\nNomic Embed has a hosted endpoint at https://api-atlas.nomic.ai/v1/embedding/text. 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/nomic-embed-vs-nvidia-nemo-retriever.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nomic-embed\", \"b\": \"nvidia-nemo-retriever\"}`. From a terminal: `anchor compare nomic-embed nvidia-nemo-retriever`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nomic-embed.json and https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json\n\n## Other comparisons with Nomic Embed or NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n- [Amazon Nova Multimodal Embeddings vs Nomic Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed.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 Nomic Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.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 Nomic Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-nomic-embed.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 Nomic Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-nomic-embed.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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)\n- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [Nomic Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.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- [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": "Nomic 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 Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security \u0026 auth. Both do embed text. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Nomic Embed D 49.2", "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "scores" ], "h1": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "image": "https://www.anchorterminal.com/assets/og/compare-nomic-embed-vs-nvidia-nemo-retriever.png", "path": "/compare/nomic-embed-vs-nvidia-nemo-retriever", "published": "2026-10-01", "section": "tools", "title": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever" }, "tokens": { "markdown": 2750, "slim": 830 }, "version": 1 }