{ "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 Qwen3-Embedding-0.6B's 63.5 (B), and leads in 4 of 5 scored categories. Qwen3-Embedding-0.6B leads on reach.", "b": { "slug": "qwen3-embedding-0-6b", "name": "Qwen3-Embedding-0.6B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "embeddings", "summary": "Qwen3-Embedding-0.6B is Alibaba's open-weight text embedding model of 0.6B parameters, built on Qwen3-0.6B-Base, for retrieval, classification and clustering in over 100 languages and code, with 1,024-dimension vectors and a 32K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json", "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-0.6B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Embedding-0.6B-GGUF" }, { "registry": "ollama", "name": "qwen3-embedding:0.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both Qwen repositories report gated false (checked 2026-10-21). Ollama's library build pulls with no account.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it costs the hardware, 1.2 GB for the BF16 files and 0.64 GB for the Q8_0 GGUF from Qwen or Ollama. Alibaba's Model Studio sells text-embedding-v4, which it calls part of the Qwen3-Embedding series, but its dimensions up to 2,048 don't match this model's 1,024 (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", "code-retrieval", "matryoshka", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-04-20", "graded": true, "anchor": { "graded": true, "score": 63.5, "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": 8, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 56, "interface": 65, "reach": 90, "risk": 55, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.", "bestFor": "Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included", "strengths": [ "Apache 2.0 with nothing added on both it and its base, ungated, safetensors and GGUF only", "0.64 GB at Q8_0 from Qwen or Ollama, so it fits almost any machine", "Five released runtimes run it, plus Sentence Transformers and Text Embeddings Inference", "Instruction format, last-token pooling and the vector output are documented with examples", "32K-token inputs and over 100 languages, code included" ], "weaknesses": [ "Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval", "Custom dimensions from 32 to 1,024 are claimed without instructions", "No change from Alibaba since a README update on 20 April 2026, and few publisher replies in its discussions", "No safety results, agent guidance or security contact", "config.json names Qwen3ForCausalLM, with an open pull request to change it and the card's vLLM example out of date" ], "agentNotes": [ "Start it with `ollama pull qwen3-embedding:0.6b`, then POST to /api/embed with the text in `input`", "With llama.cpp, run `llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192`; without `--pooling last` the vectors are wrong", "Prefix queries with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and send documents as they are; write the instruction in English", "In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool", "In Sentence Transformers, pass `prompt_name=\"query\"` for queries; the prompt is stored in config_sentence_transformers.json", "If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut", "Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.5 } ], "editorialScores": { "capability": 56, "interface": 65, "reach": 90, "risk": 55, "upkeep": 39 }, "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:0.6b\n# or, with llama.cpp and Qwen's GGUF:\nllama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"qwen3-embedding:0.6b\", \"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-0-6b" }, "sameCompany": [ "qwen3-embedding-4b", "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": "Qwen/Qwen3-0.6B-Base (fine-tune)", "params": "0.6B", "context": 32768, "goodFor": "Multilingual and code text embeddings for retrieval, classification and clustering, with task instructions on queries and vectors up to 1,024 dimensions.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "who": "publisher", "precision": "bf16", "sizeGB": 1.21, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "Qwen3-Embedding-0.6B-GGUF Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Qwen3-Embedding-0.6B-Q8_0.gguf; the same repository holds an F16 file of 1.20 GB" }, { "name": "ollama qwen3-embedding:0.6b", "url": "https://ollama.com/library/qwen3-embedding:0.6b", "who": "ollama", "precision": "q8_0", "sizeGB": 0.64, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q8_0, digest ac6da0dfba84, 639 MB as Ollama's tags page shows it, with a 32K context. An fp16 tag of 1.2 GB is also listed" } ], "run": "ollama pull qwen3-embedding:0.6b", "runBuild": "ollama qwen3-embedding:0.6b", "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-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "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 (the registrant of qwen.ai per RDAP, carried from the Qwen3.8-27B check)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The QwenLM/Qwen3-Embedding README links the Qwen organisation's repositories on Hugging Face.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The Hugging Face commit messages say what changed (EOS added by the tokeniser and Sentence Transformers integration on 6 June 2025, vLLM and TEI usage in June 2025, a README update on 20 April 2026).", "qwen.ai/.well-known/security.txt answered with a redirect on 21 October 2026, and no SECURITY.md is in the GitHub repository." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.json", "live": { "slug": "qwen3-embedding-0-6b", "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": "2026-04-20", "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": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 4 of 5 scored categories. Qwen3-Embedding-0.6B leads on reach.", "question": "Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-0.6B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 70 against 65", "Documentation and upkeep, 80 against 39" ], "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": [ "Reach, 90 against 75" ], "also": null, "goodFor": "Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included", "slug": "qwen3-embedding-0-6b", "watchFor": "Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval" } ], "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-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-qwen3-embedding-0-6b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.json", "title": "Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b" }, { "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/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-0-6b-vs-qwen3-embedding-8b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b.json", "title": "Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b" } ], "scores": [ { "by": 3, "edge": "embeddinggemma-2", "embeddinggemma-2": 59, "key": "capability", "name": "Capability", "qwen3-embedding-0-6b": 56, "weight": 35 }, { "by": 15, "edge": "qwen3-embedding-0-6b", "embeddinggemma-2": 75, "key": "reach", "name": "Reach", "qwen3-embedding-0-6b": 90, "weight": 25 }, { "by": 5, "edge": "embeddinggemma-2", "embeddinggemma-2": 60, "key": "risk", "name": "Risk", "qwen3-embedding-0-6b": 55, "weight": 20 }, { "by": 5, "edge": "embeddinggemma-2", "embeddinggemma-2": 70, "key": "interface", "name": "Agent interface", "qwen3-embedding-0-6b": 65, "weight": 10 }, { "by": 41, "edge": "embeddinggemma-2", "embeddinggemma-2": 80, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-embedding-0-6b": 39, "weight": 10 } ], "summary": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 4 of 5 scored categories. Qwen3-Embedding-0.6B leads on reach. 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-0-6b": "A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b", "json": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.min.md" }, "markdown": "EmbeddingGemma 2 scores 66.4 (B) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 4 of 5 scored categories. Qwen3-Embedding-0.6B leads on reach. 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-0.6B: grade B, 63.5/100, rank #25 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.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 39\n\nWatch for: The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update\n\n### Qwen3-Embedding-0.6B (B)\n\nGood for: Agents that need local multilingual or code retrieval embeddings on any hardware, CPU included\n\nAhead on:\n- Reach, 90 against 75\n\nWatch for: Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval\n\n\n## Score by category\n\n| Category | Weight | EmbeddingGemma 2 | Qwen3-Embedding-0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 56 | EmbeddingGemma 2 +3 |\n| Reach | 25% (25 this run) | 75 | 90 | Qwen3-Embedding-0.6B +15 |\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 | 39 | EmbeddingGemma 2 +41 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **66.4 · B** | **63.5 · B** | |\n\n## Facts side by side\n\n| Fact | EmbeddingGemma 2 | Qwen3-Embedding-0.6B |\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 | 2026-04-20 |\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-0.6B.** A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.\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-0.6B\n\n1. Start it with `ollama pull qwen3-embedding:0.6b`, then POST to /api/embed with the text in `input`\n2. With llama.cpp, run `llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192`; without `--pooling last` the vectors are wrong\n3. Prefix queries with `Instruct: \u003cone-sentence task\u003e\\nQuery:` and send documents as they are; write the instruction in English\n4. In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool\n5. In Sentence Transformers, pass `prompt_name=\"query\"` for queries; the prompt is stored in config_sentence_transformers.json\n6. If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut\n7. Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them\n\n## Questions\n\n### Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-0.6B?\n\nEmbeddingGemma 2 scores 66.4 (B) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 4 of 5 scored categories. Qwen3-Embedding-0.6B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"embeddinggemma-2\", \"b\": \"qwen3-embedding-0-6b\"}`. From a terminal: `anchor compare embeddinggemma-2 qwen3-embedding-0-6b`\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-0-6b.json\n\n## Other comparisons with EmbeddingGemma 2 or Qwen3-Embedding-0.6B\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-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 Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md)\n- [Granite Embedding Small English R2 vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-embedding-0-6b.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- [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-0.6B vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-8b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.md)\n- [Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-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-0.6B", "url": "" } ], "description": "EmbeddingGemma 2 scores 66.4 (B) to Qwen3-Embedding-0.6B's 63.5 (B) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "EmbeddingGemma 2 B 66.4", "Qwen3-Embedding-0.6B B 63.5", "scores" ], "h1": "EmbeddingGemma 2 vs Qwen3-Embedding-0.6B", "image": "https://www.anchorterminal.com/assets/og/compare-embeddinggemma-2-vs-qwen3-embedding-0-6b.png", "path": "/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b", "published": "2026-10-01", "section": "tools", "title": "EmbeddingGemma 2 vs Qwen3-Embedding-0.6B for AI agents (2026)", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-0-6b" }, "tokens": { "markdown": 2250, "slim": 530 }, "version": 1 }