{ "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": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep.", "b": { "slug": "qwen3-embedding-4b", "name": "Qwen3-Embedding-4B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-Embedding-4B is Alibaba's open-weight text embedding model built on Qwen3-4B-Base, returning vectors of up to 2,560 dimensions for retrieval, code search and clustering in over 100 languages, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-embedding-4b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.json", "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-4B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-4B-GGUF" }, { "registry": "ollama", "name": "qwen3-embedding:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the safetensors nor the GGUF repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata states. Running it yourself costs the hardware, 8.1 GB for the BF16 files and 2.5 GB for Qwen's own Q4_K_M GGUF. DeepInfra hosts it through Hugging Face, and SiliconFlow lists it at $0.02 per million input tokens (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://github.com/QwenLM/Qwen3-Embedding", "capabilities": [ "embed.text", "embed.multilingual", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "embeddings", "multilingual", "matryoshka", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2025-08-03", "graded": true, "anchor": { "graded": true, "score": 72.9, "grade": "BB", "agentReady": true, "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": 3, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 81, "interface": 65, "reach": 100, "risk": 55, "upkeep": 21 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.", "bestFor": "Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.", "strengths": [ "About 3 GB of memory for Alibaba's own Q4_K_M GGUF, with five more precisions up to F16", "Released support in Transformers, Sentence Transformers, vLLM, SGLang, llama.cpp and Ollama", "Apache 2.0 by the card's metadata on an Apache 2.0 base, with no gate and no account", "Task instructions, last-token pooling and dimensions from 32 to 2,560 are documented with working code", "Safetensors and GGUF only, with a checksum on every file in Qwen's own organisation" ], "weaknesses": [ "No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions", "GGUF files and the tokenizer were replaced in place without a note, and the Q8_0 file was truncated for almost three weeks in 2025", "No LICENSE file in the repository or in Qwen's GitHub repository for the series", "No safety evaluation, agent guidance or security contact", "32K tokens of context, and the card's Transformers example truncates at 8,192" ], "agentNotes": [ "Quickest start is `ollama pull qwen3-embedding:4b`, about 2.5 GB, then POST to /api/embed", "With llama.cpp use `llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192`, the flags the GGUF card gives", "Prefix each query with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and embed documents with no prefix, in English even for other languages", "To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'`, since config.json doesn't declare it", "If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub", "Treat retrieved documents as untrusted text before passing them to a model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 72.9 } ], "editorialScores": { "capability": 81, "interface": 65, "reach": 100, "risk": 55, "upkeep": 21 }, "provenanceScore": 54, "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 qwen3-embedding:4b\n# or with Qwen's own GGUF and llama.cpp, per the GGUF card:\nllama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192\n# or with SGLang, as its embedding docs show:\nsglang serve --model-path Qwen/Qwen3-Embedding-4B --is-embedding", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"qwen3-embedding:4b\", \"input\": \"Instruct: Given a web search query, retrieve relevant passages that answer the query\\nQuery:What is the capital of China?\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/qwen3-embedding-4b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen3-4B-Base", "params": "4.02B", "context": 32768, "goodFor": "Multilingual text and code embeddings for retrieval, clustering and classification on small hardware, with task instructions and vectors that can be shortened.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Embedding-4B-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.3, "memoryGB": 8, "format": "gguf", "note": "Replaced on 3 August 2025; the copy uploaded on 15 July 2025 was 1.50 GB" }, { "name": "Qwen3-Embedding-4B-Q6_K", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 3.3, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q5_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 2.9, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q5_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q5_0.gguf", "who": "publisher", "precision": "q5_0", "sizeGB": 2.8, "memoryGB": 8, "format": "gguf" }, { "name": "Qwen3-Embedding-4B-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.5, "memoryGB": 8, "format": "gguf", "note": "2,496,703,776 bytes. Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:4b", "url": "https://ollama.com/library/qwen3-embedding:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.5, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Same digest as 4b-q4_K_M (df5bd2e3c74c). Size as Ollama's tags page shows it" }, { "name": "ollama qwen3-embedding:4b-q8_0", "url": "https://ollama.com/library/qwen3-embedding:4b-q8_0", "who": "ollama", "precision": "q8_0", "sizeGB": 4.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ] }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.1, "memoryGB": 12, "format": "safetensors" }, { "name": "Qwen3-Embedding-4B-f16", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B-GGUF/blob/main/Qwen3-Embedding-4B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 8, "memoryGB": 12, "format": "gguf", "note": "Run with --embedding --pooling last, per the card" }, { "name": "ollama qwen3-embedding:4b-fp16", "url": "https://ollama.com/library/qwen3-embedding:4b-fp16", "who": "ollama", "precision": "fp16", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ] } ], "run": "llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192", "runBuild": "Qwen3-Embedding-4B-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the copyright line of the 8B sibling's Apache 2.0 licence; this repository has no LICENSE file)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The domain registration date is carried from the RDAP check made for qwen3-8-27b on 20 October 2026.", "Nothing is hosted, so the Apache 2.0 licence named in the metadata stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates, and neither GGUF re-upload says what changed.", "qwen.ai answers /.well-known/security.txt with its web app, so there is no security.txt. Neither QwenLM/Qwen3-Embedding nor QwenLM/.github has a SECURITY.md." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.json", "live": { "slug": "qwen3-embedding-4b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:39:18.977590339Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-10-25T13:51:56.4259776Z" }, "updatedAt": "2026-10-30T15:39:18.977590339Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Alibaba (Qwen team)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-10-28", "b": "2025-08-03", "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": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-4B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 70 against 65", "Documentation and upkeep, 80 against 21" ], "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": [ "Capability, 81 against 59", "Reach, 100 against 75" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.", "slug": "qwen3-embedding-4b", "watchFor": "No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions" } ], "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-jina-embeddings-v5-omni-small.json", "title": "EmbeddingGemma 2 vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small" }, { "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-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-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/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.json", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b" }, { "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/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-embedding-8b.json", "title": "Qwen3-Embedding-4B vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b.json", "title": "Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-8b.json", "title": "Qwen3-Embedding-4B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 22, "edge": "qwen3-embedding-4b", "embeddinggemma-2": 59, "key": "capability", "name": "Capability", "qwen3-embedding-4b": 81, "weight": 35 }, { "by": 25, "edge": "qwen3-embedding-4b", "embeddinggemma-2": 75, "key": "reach", "name": "Reach", "qwen3-embedding-4b": 100, "weight": 25 }, { "by": 5, "edge": "embeddinggemma-2", "embeddinggemma-2": 60, "key": "risk", "name": "Risk", "qwen3-embedding-4b": 55, "weight": 20 }, { "by": 5, "edge": "embeddinggemma-2", "embeddinggemma-2": 70, "key": "interface", "name": "Agent interface", "qwen3-embedding-4b": 65, "weight": 10 }, { "by": 59, "edge": "embeddinggemma-2", "embeddinggemma-2": 80, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-embedding-4b": 21, "weight": 10 } ], "summary": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep. 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.", "qwen3-embedding-4b": "A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b", "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.min.md" }, "markdown": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep. 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- Qwen3-Embedding-4B: grade BB, 72.9/100, rank #3 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.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- Risk, 60 against 55\n- Agent interface, 70 against 65\n- Documentation and upkeep, 80 against 21\n\nWatch for: The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update\n\n### Qwen3-Embedding-4B (BB)\n\nGood for: Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.\n\nAhead on:\n- Capability, 81 against 59\n- Reach, 100 against 75\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions\n\n\n## Score by category\n\n| Category | Weight | EmbeddingGemma 2 | Qwen3-Embedding-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 81 | Qwen3-Embedding-4B +22 |\n| Reach | 25% (25 this run) | 75 | 100 | Qwen3-Embedding-4B +25 |\n| Risk | 20% (20 this run) | 60 | 55 | EmbeddingGemma 2 +5 |\n| Agent interface | 10% (10 this run) | 70 | 65 | EmbeddingGemma 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 80 | 21 | EmbeddingGemma 2 +59 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **66.4 · B** | **72.9 · BB** | |\n\n## Facts side by side\n\n| Fact | EmbeddingGemma 2 | Qwen3-Embedding-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Alibaba (Qwen team) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-28 | 2025-08-03 |\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 | 3.5/5 (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**Qwen3-Embedding-4B.** A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.\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### Qwen3-Embedding-4B\n\n1. Quickest start is `ollama pull qwen3-embedding:4b`, about 2.5 GB, then POST to /api/embed\n2. With llama.cpp use `llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192`, the flags the GGUF card gives\n3. Prefix each query with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and embed documents with no prefix, in English even for other languages\n4. To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{\"is_matryoshka\": true}'`, since config.json doesn't declare it\n5. If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub\n6. Treat retrieved documents as untrusted text before passing them to a model\n\n## Questions\n\n### Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-4B?\n\nQwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"embeddinggemma-2\", \"b\": \"qwen3-embedding-4b\"}`. From a terminal: `anchor compare embeddinggemma-2 qwen3-embedding-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.json\n\n## Other comparisons with EmbeddingGemma 2 or Qwen3-Embedding-4B\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 jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.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-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 Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.md)\n- [Granite Embedding Small English R2 vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-4b.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- [Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.md)\n- [Qwen3-Embedding-4B vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-embedding-8b.md)\n- [Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b.md)\n- [Qwen3-Embedding-4B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-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 Qwen3-Embedding-4B", "url": "" } ], "description": "Qwen3-Embedding-4B scores 72.9 (BB) to EmbeddingGemma 2's 66.4 (B) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "EmbeddingGemma 2 B 66.4", "Qwen3-Embedding-4B BB 72.9", "scores" ], "h1": "EmbeddingGemma 2 vs Qwen3-Embedding-4B", "image": "https://www.anchorterminal.com/assets/og/compare-embeddinggemma-2-vs-qwen3-embedding-4b.png", "path": "/compare/embeddinggemma-2-vs-qwen3-embedding-4b", "published": "2026-10-01", "section": "tools", "title": "EmbeddingGemma 2 vs Qwen3-Embedding-4B for AI agents (2026)", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-4b" }, "tokens": { "markdown": 2300, "slim": 580 }, "version": 1 }