{ "data": { "a": { "slug": "embeddinggemma-2", "name": "EmbeddingGemma 2", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "embeddings", "summary": "EmbeddingGemma 2 is Google DeepMind's open-weight multimodal embedding model of 740M parameters, mapping text, code, images, video and audio into one 768-dimension vector space from up to 8,192 tokens, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/embeddinggemma-2", "markdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json", "repo": "https://huggingface.co/google/embeddinggemma-2", "license": "Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/embeddinggemma-2" } ], "auth": "none", "authNotes": "Not gated on Hugging Face. The files download with no account and no terms to accept (gated false in the Hub's API, checked 2026-10-30).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with the card adding that deployments must adhere to the Gemma Prohibited Use Policy. No inference provider serves it on Hugging Face. Running it takes little hardware, 1.53 GB of BF16 files, or about 0.5 GB in the 4-bit ONNX and LiteRT builds others publish (checked 2026-10-30).", "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 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://ai.google.dev/gemma/docs/embeddinggemma", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multimodal", "vision", "audio", "video", "multilingual", "matryoshka", "apache-2.0", "safetensors", "sentence-transformers", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 66.4, "grade": "B", "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": 1239, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 59, "interface": 70, "reach": 75, "risk": 60, "upkeep": 80 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.", "bestFor": "Agents that need one small local embedding model for text, code, images, video and audio search", "strengths": [ "Apache 2.0, not gated, with safetensors only in Google's verified organisation and a checksum on every file", "Text, images, video and audio in one 8,192-token input and one 768-dimension vector, truncatable to 512, 256 or 128", "Runs in Transformers 5.19.0 with Sentence Transformers 6.1.0, vLLM 0.32.0, SGLang 0.5.22, llama.cpp and Ollama 0.40.13", "Vision and audio encoders load only when needed, so text alone is 270M parameters", "A card that documents every modality's token cost, the task prefixes and a January 2025 cut-off" ], "weaknesses": [ "The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update", "Google publishes no quantised build of its own; the 4-bit and GGUF builds are other organisations'", "An 8,192-token input shared by every modality, about 29 images or 327 seconds of audio", "No safety results, and no inference provider hosts it", "MTEB's rows for it sit below the card's figures on every tab read, and below EmbeddingGemma 300M on the two text tabs" ], "agentNotes": [ "Install sentence-transformers 6.1.0 or later and transformers 5.19.0 or later; older Sentence Transformers ignore the order of multimodal dict inputs", "Use prompt_name SearchQuery for queries and Document for untitled documents; write `title: {title} | text: {content}` yourself for titled ones, and pass images, video and audio with no prefix", "For text only, load with config_kwargs {\"vision_config\": None, \"audio_config\": None} to skip 470M parameters of encoders", "Run in bfloat16 or float32, never float16, which returns NaN or degraded vectors without an error", "Pass truncate_dim and normalize_embeddings=True for smaller vectors, and keep queries and documents at the same size", "Supply audio as mono 16 kHz; video is sampled at 1 frame a second by default" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.4 } ], "editorialScores": { "capability": 59, "interface": 70, "reach": 75, "risk": 60, "upkeep": 80 }, "provenanceScore": 70, "checked": { "categories": { "capability": "2026-10-30", "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U \"sentence-transformers\u003e=6.1.0\" \"transformers\u003e=5.19.0\"\npython -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('google/embeddinggemma-2', config_kwargs={'vision_config': None, 'audio_config': None}); print(m.encode('Which planet is red?', prompt_name='SearchQuery').shape)\"" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/embeddinggemma-2" }, "sameCompany": [ "embeddinggemma-300m", "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 4 architecture (the card says it builds on Gemma 4's advancements; no base checkpoint named)", "params": "740M", "context": 8192, "dimensions": 768, "goodFor": "Small multimodal embeddings for search, retrieval, classification and clustering across text, code, images, video and audio in one vector space, on laptops, phones or in a browser.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp", "Ollama" ], "builds": [ { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "who": "publisher", "precision": "bf16", "sizeGB": 1.53, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers", "vLLM", "SGLang" ], "note": "All three encoders. Text only loads 270M of the 740M parameters" }, { "name": "ggml-org embeddinggemma-2-Q8_0", "url": "https://huggingface.co/ggml-org/embeddinggemma-2-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 0.86, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "llama.cpp's organisation converted it automatically from Google's 6 October commit. The text model is 0.31 GB and the multimodal projector 0.55 GB; BF16 files are there too" }, { "name": "onnx-community embeddinggemma-2-ONNX q4", "url": "https://huggingface.co/onnx-community/embeddinggemma-2-ONNX", "who": "onnx-community", "precision": "q4", "sizeGB": 0.5, "memoryGB": 8, "format": "onnx", "runsOn": [ "Transformers.js" ], "note": "Hugging Face's ONNX community build for Transformers.js, WebGPU, WASM or Node. q4 text model 0.17 GB, vision encoder 0.11 GB, audio encoder 0.19 GB, tokeniser 0.03 GB; fp32, fp16, q4f16 and 8-bit files too" }, { "name": "litert-community embeddinggemma-2-740m-litert-lm", "url": "https://huggingface.co/litert-community/embeddinggemma-2-740m-litert-lm", "who": "litert-community", "precision": "int4 (QAT, int8 vision, mixed audio)", "sizeGB": 0.48, "memoryGB": 8, "format": "litertlm", "runsOn": [ "LiteRT-LM" ], "note": "The LiteRT Community organisation's on-device build, linked to Google AI Edge's apps and demos; text-only (165 MB) and text and vision (388 MB) builds sit in their own repositories. Not counted as Google's own, since no first-hand Google page read here links it" } ], "run": "python -c \"from sentence_transformers import SentenceTransformer; SentenceTransformer('google/embeddinggemma-2')\"", "runBuild": "embeddinggemma-2", "checked": "2026-10-30", "minMemoryGB": 8 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" } ], "olderCount": 171 }, "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://ai.google.dev/gemma/docs/gemma_4_license", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases.md", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "The registrant, registration date, releases page and security.txt findings are carried from the embeddinggemma-300m and gemma-4 facts checked on 21 October 2026; gemma.ai and ai.google.dev couldn't be fetched in this session.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The repository was created privately on 14 September 2026 and published on 6 October 2026 (the super-squash commit, MTEB's release date and Transformers' contribution note agree). The card last changed on 28 October 2026.", "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. Google's company-wide security.txt points to its vulnerability reward programme." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/embeddinggemma-2.json" }, "answer": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category.", "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": 1239, "categoryRank": 16, "methodology": "0.7", "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": "0.7", "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": 73, "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": 73 }, "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": "2026-10-30T15:38:34.773289334Z" }, "domain": { "domain": "jina.ai", "registered": "2020-01-20", "source": "https://rdap.identitydigital.services/rdap/domain/jina.ai", "checkedAt": "2026-10-25T14:00:13.223605844Z" }, "pages": [ { "url": "https://huggingface.co/jinaai/jina-embeddings-v5-omni-small/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-30T05:42:09.875701375Z", "changedAt": "2026-10-30T05:42:09.875701375Z", "fingerprint": "86913f9f9adf" } ], "updatedAt": "2026-10-30T15:38:34.773289334Z" } }, "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": "Apache 2.0", "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": "2026-10-28", "b": "2026-10-11", "name": "Last release" }, { "a": "2026-04-01", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category.", "question": "Which is better for AI agents, EmbeddingGemma 2 or jina-embeddings-v5-omni-small?" } ], "goodFor": [ { "aheadOn": [ "Capability, 59 against 33", "Reach, 75 against 65", "Risk, 60 against 45", "Agent interface, 70 against 65", "Documentation and upkeep, 80 against 69" ], "also": null, "goodFor": "Agents that need one small local embedding model for text, code, images, video and audio search", "slug": "embeddinggemma-2", "watchFor": "The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update" }, { "aheadOn": null, "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-2-vs-embeddinggemma-300m.json", "title": "EmbeddingGemma 2 vs EmbeddingGemma 300M", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-embeddinggemma-300m" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-granite-embedding-small-english-r2.json", "title": "EmbeddingGemma 2 vs Granite Embedding Small English R2", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-granite-embedding-small-english-r2" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.json", "title": "EmbeddingGemma 2 vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.json", "title": "EmbeddingGemma 2 vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.json", "title": "EmbeddingGemma 2 vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.json", "title": "EmbeddingGemma 2 vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-8b.json", "title": "EmbeddingGemma 2 vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json", "title": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small" }, { "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": 26, "edge": "embeddinggemma-2", "embeddinggemma-2": 59, "jina-embeddings-v5-omni-small": 33, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 10, "edge": "embeddinggemma-2", "embeddinggemma-2": 75, "jina-embeddings-v5-omni-small": 65, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 15, "edge": "embeddinggemma-2", "embeddinggemma-2": 60, "jina-embeddings-v5-omni-small": 45, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 5, "edge": "embeddinggemma-2", "embeddinggemma-2": 70, "jina-embeddings-v5-omni-small": 65, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 11, "edge": "embeddinggemma-2", "embeddinggemma-2": 80, "jina-embeddings-v5-omni-small": 69, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category. Both do text embeddings.", "tutorials": [], "verdicts": { "embeddinggemma-2": "A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.", "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-2-vs-jina-embeddings-v5-omni-small", "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.min.md" }, "markdown": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category. Both do text embeddings.\n\n- EmbeddingGemma 2: grade B, 66.4/100, rank #16 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/embeddinggemma-2.md · JSON https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json\n- jina-embeddings-v5-omni-small: grade D, 50.2/100, rank #108 of 117 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 81 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md\n\n## Which one, for what\n\n### EmbeddingGemma 2 (B)\n\nGood for: Agents that need one small local embedding model for text, code, images, video and audio search\n\nAhead on:\n- Capability, 59 against 33\n- Reach, 75 against 65\n- Risk, 60 against 45\n- Agent interface, 70 against 65\n- Documentation and upkeep, 80 against 69\n\nWatch for: The card requires deployments to follow the Gemma Prohibited Use Policy, which 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\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 2 | jina-embeddings-v5-omni-small | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 33 | EmbeddingGemma 2 +26 |\n| Reach | 25% (25 this run) | 75 | 65 | EmbeddingGemma 2 +10 |\n| Risk | 20% (20 this run) | 60 | 45 | EmbeddingGemma 2 +15 |\n| Agent interface | 10% (10 this run) | 70 | 65 | EmbeddingGemma 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 80 | 69 | EmbeddingGemma 2 +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **66.4 · B** | **50.2 · D** | |\n\n## Facts side by side\n\n| Fact | EmbeddingGemma 2 | 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 | Apache 2.0 | CC-BY-NC-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-28 | 2026-10-11 |\n| Terms last updated | 2026-04-01 | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\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 | not found in the text | |\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| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**EmbeddingGemma 2.** A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.\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 2\n\n1. Install sentence-transformers 6.1.0 or later and transformers 5.19.0 or later; older Sentence Transformers ignore the order of multimodal dict inputs\n2. Use prompt_name SearchQuery for queries and Document for untitled documents; write `title: {title} | text: {content}` yourself for titled ones, and pass images, video and audio with no prefix\n3. For text only, load with config_kwargs {\"vision_config\": None, \"audio_config\": None} to skip 470M parameters of encoders\n4. Run in bfloat16 or float32, never float16, which returns NaN or degraded vectors without an error\n5. Pass truncate_dim and normalize_embeddings=True for smaller vectors, and keep queries and documents at the same size\n6. Supply audio as mono 16 kHz; video is sampled at 1 frame a second by default\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 2 or jina-embeddings-v5-omni-small?\n\nEmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"embeddinggemma-2\", \"b\": \"jina-embeddings-v5-omni-small\"}`. From a terminal: `anchor compare embeddinggemma-2 jina-embeddings-v5-omni-small`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json and https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json\n\n## Other comparisons with EmbeddingGemma 2 or jina-embeddings-v5-omni-small\n\n- [EmbeddingGemma 2 vs EmbeddingGemma 300M](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-embeddinggemma-300m.md)\n- [EmbeddingGemma 2 vs Granite Embedding Small English R2](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-granite-embedding-small-english-r2.md)\n- [EmbeddingGemma 2 vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.md)\n- [EmbeddingGemma 2 vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.md)\n- [EmbeddingGemma 2 vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.md)\n- [EmbeddingGemma 2 vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.md)\n- [EmbeddingGemma 2 vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-8b.md)\n- [EmbeddingGemma 300M vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.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": "2026-10-31", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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 2 vs jina-embeddings-v5-omni-small", "url": "" } ], "description": "EmbeddingGemma 2 scores 66.4 (B) to jina-embeddings-v5-omni-small's 50.2 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "EmbeddingGemma 2 B 66.4", "jina-embeddings-v5-omni-small D 50.2", "scores" ], "h1": "EmbeddingGemma 2 vs jina-embeddings-v5-omni-small", "image": "https://www.anchorterminal.com/assets/og/compare-embeddinggemma-2-vs-jina-embeddings-v5-omni-small.png", "path": "/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small", "published": "2026-10-01", "section": "tools", "title": "EmbeddingGemma 2 vs jina-embeddings-v5-omni-small for AI agents (2026)", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small" }, "tokens": { "markdown": 2350, "slim": 580 }, "version": 1 }