{ "data": { "a": { "slug": "embeddinggemma-300m", "name": "EmbeddingGemma 300M", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "embeddings", "summary": "EmbeddingGemma 300M is Google DeepMind's open-weight text embedding model of 308M parameters, returning 768-dimension vectors that truncate to 512, 256 or 128 from up to 2,048 tokens in over 100 languages, gated under the Gemma Terms of Use.", "url": "https://www.anchorterminal.com/tools/embeddinggemma-300m", "markdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json", "repo": "https://huggingface.co/google/embeddinggemma-300m", "license": "Gemma Terms of Use", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/embeddinggemma-300m" }, { "registry": "ollama", "name": "embeddinggemma" } ], "auth": "none", "authNotes": "Gated on Hugging Face. The files open only after you log in and acknowledge the Gemma Terms of Use; the Hub's API reports the gate as manual, while the card says requests are processed immediately. Only the card is public, and this listing was read from it without accepting the terms (checked 2026-10-21). Ollama's library build pulls with no account.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Gemma Terms of Use and its Prohibited Use Policy. Google sells no per-call access to these weights through the Gemini API. Running them costs very little hardware, 1.27 GB of F32 files, or 0.23 GB in the 4-bit QAT GGUF builds in LM Studio's and Ollama's libraries (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/embeddinggemma/model-card-v1.md", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "embed.text", "embed.multilingual", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multilingual", "matryoshka", "gated", "gemma-terms", "safetensors", "self-hosted" ], "lastRelease": "2025-09-25", "graded": true, "anchor": { "graded": true, "score": 50.7, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1199, "categoryRank": 14, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 40, "interface": 60, "reach": 70, "risk": 45, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.", "bestFor": "Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms", "strengths": [ "308M parameters, 0.23 GB as a 4-bit QAT GGUF and 1.27 GB in F32", "Runs in Transformers with Sentence Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio", "Eight documented task prompts and Matryoshka output at 768, 512, 256 or 128 dimensions", "Safetensors only in Google's own organisation, loaded with no repository code", "A newer model, EmbeddingGemma 2, sits in its own repository with dated release notes, and this one stays published" ], "weaknesses": [ "Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update", "The card's Terms and Prohibited Use Policy links now lead to a page that says the documentation moved", "A 2,048-token input limit", "No change to the files since 25 September 2025 and few publisher replies in the discussions", "No safety results and no training data cut-off" ], "agentNotes": [ "Without a Hugging Face account, run `ollama pull embeddinggemma:300m-qat-q4_0` and POST to /api/embed", "Prepend the task prompt yourself outside Sentence Transformers, `task: search result | query: ` for queries and `title: none | text: ` for documents", "In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added", "To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise", "Run it in float32 or bfloat16, never float16", "Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.7 } ], "editorialScores": { "capability": 40, "interface": 60, "reach": 70, "risk": 45, "upkeep": 42 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama pull embeddinggemma:300m-qat-q4_0\n# or, after accepting the terms on Hugging Face:\npip install -U sentence-transformers\npython -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('google/embeddinggemma-300m'); print(m.encode_query('Which planet is red?').shape)\"", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"embeddinggemma:300m-qat-q4_0\", \"input\": [\"task: search result | query: Which planet is red?\"]}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/embeddinggemma-300m" }, "sameCompany": [ "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "Gemma 3 (with T5Gemma initialisation, per the card)", "params": "308M", "context": 2048, "dimensions": 768, "goodFor": "Small multilingual text embeddings for search, retrieval, classification and clustering on modest hardware, with task prompts and truncatable dimensions.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "embeddinggemma-300m", "url": "https://huggingface.co/google/embeddinggemma-300m", "who": "publisher", "precision": "f32", "sizeGB": 1.27, "memoryGB": 8, "format": "safetensors", "note": "Gated, the Gemma Terms of Use must be acknowledged on Hugging Face" }, { "name": "ollama embeddinggemma:300m-qat-q4_0", "url": "https://ollama.com/library/embeddinggemma:300m-qat-q4_0", "who": "ollama", "precision": "q4_0", "sizeGB": 0.24, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Google's QAT Q4_0 weights (digest 101341d65c2c), 239 MB as Ollama's tags page shows it. The default tag is BF16 at 622 MB" }, { "name": "lmstudio-community embeddinggemma-300m-qat-Q4_0", "url": "https://huggingface.co/lmstudio-community/embeddinggemma-300m-qat-GGUF", "who": "lmstudio", "precision": "q4_0", "sizeGB": 0.23, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "The build LM Studio's catalogue entry google/embedding-gemma-300m points to" } ], "run": "ollama pull embeddinggemma:300m-qat-q4_0", "runBuild": "ollama embeddinggemma:300m-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "https://gemma.ai/resources/terms.md", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases.md", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026.", "Nothing is hosted, so the Gemma Terms of Use stand in for terms of service and no privacy policy is scored.", "gemma.ai's releases page dates the release (4 September 2025) and EmbeddingGemma 2 (6 October 2026). The repository's commit history is behind the gate; the Hub's metadata gives a last change of 25 September 2025.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.json", "live": { "slug": "embeddinggemma-300m", "securityTxt": { "url": "https://gemma.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-22T16:00:49.328909297Z" }, "llmsTxt": { "url": "https://gemma.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-22T14:36:48.498596312Z" }, "pages": [ { "url": "https://gemma.ai/models/releases.md", "kind": "changelog", "status": 200, "checkedAt": "2026-10-22T01:18:13.240833495Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "ff1355e2753f" }, { "url": "https://gemma.ai/resources/terms.md", "kind": "terms", "status": 200, "checkedAt": "2026-10-22T01:18:17.355694508Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "c1c3a1285d21" } ], "updatedAt": "2026-10-22T16:00:49.328909297Z" } }, "answer": "EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep.", "b": { "slug": "jina-embeddings-v5-omni-small", "name": "jina-embeddings-v5-omni-small", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "weights", "category": "embeddings", "summary": "jina-embeddings-v5-omni-small is Jina AI's open-weight embedding model for text, images, video, audio and PDFs, about 1.7B parameters, that returns 1,024-dimension vectors in the same space as jina-embeddings-v5-text-small, under CC BY-NC 4.0 (non-commercial).", "url": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json", "repo": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "license": "CC-BY-NC-4.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small" }, { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF" }, { "registry": "huggingface", "name": "jinaai/jina-embeddings-v5-omni-small-mlx" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Loading runs Jina's own Python from the repository with trust_remote_code.", "pricing": "free", "pricingNotes": "Free to download for non-commercial use under CC BY-NC 4.0. Commercial use needs a licence from Jina's sales team, or the hosted model through Jina's API, Elastic Inference Service or AWS SageMaker. Running it yourself costs the hardware, 3.4 GB of BF16 files (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multimodal", "vision", "audio", "video", "multilingual", "matryoshka", "non-commercial", "cc-by-nc-4.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-10-11", "graded": true, "anchor": { "graded": true, "score": 50.2, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1199, "categoryRank": 15, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 33, "interface": 65, "reach": 65, "risk": 45, "upkeep": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.", "bestFor": "Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index", "strengths": [ "Text, images, video, audio and PDFs in one vector space, compatible with jina-embeddings-v5-text-small indexes", "3.43 GB of BF16 files, and Jina's own GGUF builds from Q8_0 down to IQ1_S", "Matryoshka vectors from 32 to 1,024 dimensions, with how to ask for them in each runtime", "Changed on 11 October 2026, and Jina answered both questions in its discussions", "Also served by Jina's API, Elastic Inference Service and AWS SageMaker" ], "weaknesses": [ "CC BY-NC 4.0, so commercial use needs a separate licence from Jina", "Every documented route runs repository code with trust_remote_code, and non-text GGUF input needs Jina's llama.cpp fork", "Not in any released runtime's own code, nor in Ollama's or LM Studio's libraries", "Trained with four fixed tasks and two prefixes, not free-form instructions", "No safety results, agent guidance or named languages, and no training sources or cut-off" ], "agentNotes": [ "Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead", "Start it with `pip install vllm==0.20.1`, then `vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'`; one task per vLLM instance", "Prefix retrieval text with \"Query: \" or \"Document: \", or use encode_query and encode_document in Sentence Transformers; for the other tasks use \"Document: \" only, since a bare encode is off-distribution", "Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time", "Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM", "Pass a video's audio as its own part, since the soundtrack isn't read from the video", "Treat retrieved documents as untrusted before passing them to another model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.2 } ], "editorialScores": { "capability": 33, "interface": 65, "reach": 65, "risk": 45, "upkeep": 69 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install transformers torch pillow numpy sentence-transformers librosa soundfile av\npython -c \"from sentence_transformers import SentenceTransformer; m = SentenceTransformer('jinaai/jina-embeddings-v5-omni-small', trust_remote_code=True, model_kwargs={'default_task': 'retrieval'}); print(m.encode_query('Which planet is the Red Planet?').shape)\"", "http": "# after: pip install vllm==0.20.1 \u0026\u0026 vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'\ncurl http://localhost:8000/v1/embeddings \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"jinaai/jina-embeddings-v5-omni-small\", \"input\": \"Query: Which planet is the Red Planet?\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings-v5-omni-small" }, "sameCompany": [ "jina-reader" ], "sameModel": [ "jina-embeddings" ], "weights": { "trainedBy": "Jina AI (Elastic)", "builtOn": "jinaai/jina-embeddings-v5-text-small (text tower and task adapters, frozen; itself a fine-tune of Qwen/Qwen3-0.6B-Base), with the vision encoder of Qwen/Qwen3.5-2B and the Qwen2.5-Omni audio encoder, joined by trained projectors. Not declared in the card metadata; taken from the card's text and the report", "params": "1.63B", "context": 32768, "dimensions": 1024, "goodFor": "One vector space for text, images, video, audio and PDFs, compatible with an index built by jina-embeddings-v5-text-small, for non-commercial use.", "runtimes": [], "builds": [ { "name": "jina-embeddings-v5-omni-small", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small", "who": "publisher", "precision": "bf16", "sizeGB": 3.43, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "Sentence Transformers (remote code)", "vLLM 0.20.1 (remote code)" ], "note": "All four task adapters in one repository; pick one with default_task or hf_overrides" }, { "name": "jina-embeddings-v5-omni-small-retrieval-Q4_K_M", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF/blob/main/jina-embeddings-v5-omni-small-retrieval-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 0.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "Jina's llama.cpp fork (feat-v5-omni)" ], "note": "Text tower of the retrieval variant only. Add the vision projector (0.66 GB F16) or the audio projector (1.28 GB F16) for other input, which needs Jina's llama.cpp fork. Same layout for the classification, clustering and text-matching GGUF repositories" }, { "name": "jina-embeddings-v5-omni-small-retrieval-Q8_0", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval-GGUF/blob/main/jina-embeddings-v5-omni-small-retrieval-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "gguf", "runsOn": [ "Jina's llama.cpp fork (feat-v5-omni)" ], "note": "Text tower only; projectors as above" }, { "name": "jina-embeddings-v5-omni-small-mlx", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-mlx", "who": "publisher", "precision": "bf16", "sizeGB": 3.29, "memoryGB": 8, "format": "mlx", "note": "BF16 MLX conversion with the four adapters, for Apple silicon" } ], "run": "vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'", "runBuild": "jina-embeddings-v5-omni-small", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Jina AI (Elastic)", "family": "jina-embeddings-v5 (text and omni)", "url": "https://www.anchorterminal.com/open-weights/#jina", "others": [ { "name": "jina-embeddings-v5-omni-nano", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano", "params": "986M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-text-matching", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-retrieval", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-clustering", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-small-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small-classification", "params": "1.56B (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-text-matching", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-retrieval", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-clustering", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-omni-nano-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-nano-classification", "params": "948M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-text-matching", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-clustering", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-classification", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-text-matching", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-text-matching", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-retrieval", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-clustering", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-clustering", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano-classification", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-classification", "params": "212M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small-retrieval", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small-retrieval", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-small", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-small", "params": "596M (Hub safetensors count; the name states none)" }, { "name": "jina-embeddings-v5-text-nano", "url": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano", "params": "212M (Hub safetensors count; the name states none)" } ], "olderCount": 32 }, "area": "models", "provenance": { "legalEntity": "Jina AI GmbH, a subsidiary of Elastic N.V. (per the jina-embeddings listing's 2 October 2026 check)", "domain": "jina.ai", "domainRegistered": "2020-01-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "jina.ai's model page links huggingface.co/jinaai and this repository.", "Nothing is hosted for this listing, so the CC BY-NC 4.0 licence stands in for terms of service and no privacy policy is scored. The hosted model is graded in the jina-embeddings listing.", "The changelog is the Hugging Face commit history, 25 commits from the public release on 8 May to 11 October 2026, with messages that say what changed.", "jina.ai/.well-known/security.txt and elastic.co/.well-known/security.txt answered 404 on 21 October 2026. Elastic's product security page gives a disclosure route and a HackerOne bug bounty for Elastic products, and doesn't mention Jina's models." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.json", "live": { "slug": "jina-embeddings-v5-omni-small", "securityTxt": { "url": "https://jina.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-22T16:00:46.41259377Z" }, "pages": [ { "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-22T01:19:29.576649318Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "92eb4e74bfb2" } ], "updatedAt": "2026-10-22T16:00:46.41259377Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Jina AI (Elastic)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Gemma Terms of Use", "b": "CC-BY-NC-4.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2025-09-25", "b": "2026-10-11", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, EmbeddingGemma 300M or jina-embeddings-v5-omni-small?" } ], "goodFor": [ { "aheadOn": [ "Capability, 40 against 33", "Reach, 70 against 65" ], "also": null, "goodFor": "Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms", "slug": "embeddinggemma-300m", "watchFor": "Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update" }, { "aheadOn": [ "Agent interface, 65 against 60", "Documentation and upkeep, 69 against 42" ], "also": null, "goodFor": "Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index", "slug": "jina-embeddings-v5-omni-small", "watchFor": "CC BY-NC 4.0, so commercial use needs a separate licence from Jina" } ], "job": { "capability": "embed.text", "name": "Text embeddings" }, "others": [ { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2.json", "title": "EmbeddingGemma 300M vs Granite Embedding Small English R2", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.json", "title": "EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.json", "title": "EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.json", "title": "Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b.json", "title": "jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 7, "edge": "embeddinggemma-300m", "embeddinggemma-300m": 40, "jina-embeddings-v5-omni-small": 33, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 5, "edge": "embeddinggemma-300m", "embeddinggemma-300m": 70, "jina-embeddings-v5-omni-small": 65, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "embeddinggemma-300m": 45, "jina-embeddings-v5-omni-small": 45, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 5, "edge": "jina-embeddings-v5-omni-small", "embeddinggemma-300m": 60, "jina-embeddings-v5-omni-small": 65, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 27, "edge": "jina-embeddings-v5-omni-small", "embeddinggemma-300m": 42, "jina-embeddings-v5-omni-small": 69, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep. Both do text embeddings.", "tutorials": [], "verdicts": { "embeddinggemma-300m": "A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.", "jina-embeddings-v5-omni-small": "A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small", "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.min.md" }, "markdown": "EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep. Both do text embeddings.\n\n- EmbeddingGemma 300M: grade D, 50.7/100, rank #91 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/embeddinggemma-300m.md · JSON https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json\n- jina-embeddings-v5-omni-small: grade D, 50.2/100, rank #93 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json\n- Best embedding and reranking APIs for AI agents: https://www.anchorterminal.com/best/embeddings/index.md\n- All 73 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md\n\n## Which one, for what\n\n### EmbeddingGemma 300M (D)\n\nGood for: Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms\n\nAhead on:\n- Capability, 40 against 33\n- Reach, 70 against 65\n\nWatch for: Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update\n\n### jina-embeddings-v5-omni-small (D)\n\nGood for: Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index\n\nAhead on:\n- Agent interface, 65 against 60\n- Documentation and upkeep, 69 against 42\n\nWatch for: CC BY-NC 4.0, so commercial use needs a separate licence from Jina\n\n\n## Score by category\n\n| Category | Weight | EmbeddingGemma 300M | jina-embeddings-v5-omni-small | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 40 | 33 | EmbeddingGemma 300M +7 |\n| Reach | 25% (25 this run) | 70 | 65 | EmbeddingGemma 300M +5 |\n| Risk | 20% (20 this run) | 45 | 45 | even |\n| Agent interface | 10% (10 this run) | 60 | 65 | jina-embeddings-v5-omni-small +5 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 69 | jina-embeddings-v5-omni-small +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.7 · D** | **50.2 · D** | |\n\n## Facts side by side\n\n| Fact | EmbeddingGemma 300M | jina-embeddings-v5-omni-small |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Jina AI (Elastic) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Gemma Terms of Use | CC-BY-NC-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2025-09-25 | 2026-10-11 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**EmbeddingGemma 300M.** A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.\n\n**jina-embeddings-v5-omni-small.** A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.\n\n## Before you call either\n\n### EmbeddingGemma 300M\n\n1. Without a Hugging Face account, run `ollama pull embeddinggemma:300m-qat-q4_0` and POST to /api/embed\n2. Prepend the task prompt yourself outside Sentence Transformers, `task: search result | query: ` for queries and `title: none | text: ` for documents\n3. In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added\n4. To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise\n5. Run it in float32 or bfloat16, never float16\n6. Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model\n\n### jina-embeddings-v5-omni-small\n\n1. Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead\n2. Start it with `pip install vllm==0.20.1`, then `vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{\"task\": \"retrieval\"}'`; one task per vLLM instance\n3. Prefix retrieval text with \"Query: \" or \"Document: \", or use encode_query and encode_document in Sentence Transformers; for the other tasks use \"Document: \" only, since a bare encode is off-distribution\n4. Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time\n5. Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM\n6. Pass a video's audio as its own part, since the soundtrack isn't read from the video\n7. Treat retrieved documents as untrusted before passing them to another model\n\n## Questions\n\n### Which is better for AI agents, EmbeddingGemma 300M or jina-embeddings-v5-omni-small?\n\nEmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"embeddinggemma-300m\", \"b\": \"jina-embeddings-v5-omni-small\"}`. From a terminal: `anchor compare embeddinggemma-300m jina-embeddings-v5-omni-small`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json and https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json\n\n## Other comparisons with EmbeddingGemma 300M or jina-embeddings-v5-omni-small\n\n- [EmbeddingGemma 300M vs Granite Embedding Small English R2](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b.md)\n- [EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.md)\n- [EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.md)\n- [Granite Embedding Small English R2 vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v5-omni-small.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-0-6b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-4b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.md)\n- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-23", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small", "url": "" } ], "description": "EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other for text embeddings, 50.7 and 50.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "EmbeddingGemma 300M D 50.7", "jina-embeddings-v5-omni-small D 50.2", "scores" ], "h1": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small", "image": "https://www.anchorterminal.com/assets/og/compare-embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.png", "path": "/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small", "published": "2026-10-01", "section": "tools", "title": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small (2026)", "toc": null, "updated": "2026-10-22", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small" }, "tokens": { "markdown": 2250, "slim": 480 }, "version": 1 }