{ "data": { "a": { "slug": "embeddinggemma-300m", "name": "EmbeddingGemma 300M", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "embeddings", "summary": "EmbeddingGemma 300M is Google DeepMind's open-weight text embedding model of 308M parameters, returning 768-dimension vectors that truncate to 512, 256 or 128 from up to 2,048 tokens in over 100 languages, gated under the Gemma Terms of Use.", "url": "https://www.anchorterminal.com/tools/embeddinggemma-300m", "markdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json", "repo": "https://huggingface.co/google/embeddinggemma-300m", "license": "Gemma Terms of Use", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/embeddinggemma-300m" }, { "registry": "ollama", "name": "embeddinggemma" } ], "auth": "none", "authNotes": "Gated on Hugging Face. The files open only after you log in and acknowledge the Gemma Terms of Use; the Hub's API reports the gate as manual, while the card says requests are processed immediately. Only the card is public, and this listing was read from it without accepting the terms (checked 2026-10-21). Ollama's library build pulls with no account.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Gemma Terms of Use and its Prohibited Use Policy. Google sells no per-call access to these weights through the Gemini API. Running them costs very little hardware, 1.27 GB of F32 files, or 0.23 GB in the 4-bit QAT GGUF builds in LM Studio's and Ollama's libraries (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/embeddinggemma/model-card-v1.md", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "embed.text", "embed.multilingual", "embed.code", "model.open-weights" ], "tags": [ "open-weights", "model", "embeddings", "multilingual", "matryoshka", "gated", "gemma-terms", "safetensors", "self-hosted" ], "lastRelease": "2025-09-25", "graded": true, "anchor": { "graded": true, "score": 50.7, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 14, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 40, "interface": 60, "reach": 70, "risk": 45, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.", "bestFor": "Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms", "strengths": [ "308M parameters, 0.23 GB as a 4-bit QAT GGUF and 1.27 GB in F32", "Runs in Transformers with Sentence Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio", "Eight documented task prompts and Matryoshka output at 768, 512, 256 or 128 dimensions", "Safetensors only in Google's own organisation, loaded with no repository code", "A newer model, EmbeddingGemma 2, sits in its own repository with dated release notes, and this one stays published" ], "weaknesses": [ "Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update", "The card's Terms and Prohibited Use Policy links now lead to a page that says the documentation moved", "A 2,048-token input limit", "No change to the files since 25 September 2025 and few publisher replies in the discussions", "No safety results and no training data cut-off" ], "agentNotes": [ "Without a Hugging Face account, run `ollama pull embeddinggemma:300m-qat-q4_0` and POST to /api/embed", "Prepend the task prompt yourself outside Sentence Transformers, `task: search result | query: ` for queries and `title: none | text: ` for documents", "In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added", "To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise", "Run it in float32 or bfloat16, never float16", "Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.7 } ], "editorialScores": { "capability": 40, "interface": 60, "reach": 70, "risk": 45, "upkeep": 42 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama pull embeddinggemma:300m-qat-q4_0\n# or, after accepting the terms on Hugging Face:\npip install -U sentence-transformers\npython -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('google/embeddinggemma-300m'); print(m.encode_query('Which planet is red?').shape)\"", "http": "curl http://localhost:11434/api/embed \\\n -d '{\"model\": \"embeddinggemma:300m-qat-q4_0\", \"input\": [\"task: search result | query: Which planet is red?\"]}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/embeddinggemma-300m" }, "sameCompany": [ "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "Gemma 3 (with T5Gemma initialisation, per the card)", "params": "308M", "context": 2048, "dimensions": 768, "goodFor": "Small multilingual text embeddings for search, retrieval, classification and clustering on modest hardware, with task prompts and truncatable dimensions.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "embeddinggemma-300m", "url": "https://huggingface.co/google/embeddinggemma-300m", "who": "publisher", "precision": "f32", "sizeGB": 1.27, "memoryGB": 8, "format": "safetensors", "note": "Gated, the Gemma Terms of Use must be acknowledged on Hugging Face" }, { "name": "ollama embeddinggemma:300m-qat-q4_0", "url": "https://ollama.com/library/embeddinggemma:300m-qat-q4_0", "who": "ollama", "precision": "q4_0", "sizeGB": 0.24, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Google's QAT Q4_0 weights (digest 101341d65c2c), 239 MB as Ollama's tags page shows it. The default tag is BF16 at 622 MB" }, { "name": "lmstudio-community embeddinggemma-300m-qat-Q4_0", "url": "https://huggingface.co/lmstudio-community/embeddinggemma-300m-qat-GGUF", "who": "lmstudio", "precision": "q4_0", "sizeGB": 0.23, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "The build LM Studio's catalogue entry google/embedding-gemma-300m points to" } ], "run": "ollama pull embeddinggemma:300m-qat-q4_0", "runBuild": "ollama embeddinggemma:300m-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "https://gemma.ai/resources/terms.md", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases.md", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026.", "Nothing is hosted, so the Gemma Terms of Use stand in for terms of service and no privacy policy is scored.", "gemma.ai's releases page dates the release (4 September 2025) and EmbeddingGemma 2 (6 October 2026). The repository's commit history is behind the gate; the Hub's metadata gives a last change of 25 September 2025.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/embeddinggemma-300m.json" }, "answer": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on 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": 1197, "categoryRank": 3, "methodology": "0.6", "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": "0.6", "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": 49, "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": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.json" }, "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": "Gemma Terms of Use", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2025-09-25", "b": "2025-08-03", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep.", "question": "Which is better for AI agents, EmbeddingGemma 300M or Qwen3-Embedding-4B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 42 against 21" ], "also": null, "goodFor": "Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms", "slug": "embeddinggemma-300m", "watchFor": "Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update" }, { "aheadOn": [ "Capability, 81 against 40", "Reach, 100 against 70", "Risk, 55 against 45", "Agent interface, 65 against 60" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "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-300m-vs-granite-embedding-small-english-r2.json", "title": "EmbeddingGemma 300M vs Granite Embedding Small English R2", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json", "title": "EmbeddingGemma 300M vs jina-embeddings-v5-omni-small", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-0.6B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b.json", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.json", "title": "EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b" }, { "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.json", "title": "EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b" }, { "json": "https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-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": 41, "edge": "qwen3-embedding-4b", "embeddinggemma-300m": 40, "key": "capability", "name": "Capability", "qwen3-embedding-4b": 81, "weight": 35 }, { "by": 30, "edge": "qwen3-embedding-4b", "embeddinggemma-300m": 70, "key": "reach", "name": "Reach", "qwen3-embedding-4b": 100, "weight": 25 }, { "by": 10, "edge": "qwen3-embedding-4b", "embeddinggemma-300m": 45, "key": "risk", "name": "Risk", "qwen3-embedding-4b": 55, "weight": 20 }, { "by": 5, "edge": "qwen3-embedding-4b", "embeddinggemma-300m": 60, "key": "interface", "name": "Agent interface", "qwen3-embedding-4b": 65, "weight": 10 }, { "by": 21, "edge": "embeddinggemma-300m", "embeddinggemma-300m": 42, "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 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep. Both do text embeddings.", "tutorials": [], "verdicts": { "embeddinggemma-300m": "A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.", "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-300m-vs-qwen3-embedding-4b", "json": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.min.md" }, "markdown": "Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep. Both do text embeddings.\n\n- EmbeddingGemma 300M: grade D, 50.7/100, rank #91 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/embeddinggemma-300m.md · JSON https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json\n- Qwen3-Embedding-4B: grade BB, 72.9/100, rank #3 of 100 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 73 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md\n\n## Which one, for what\n\n### EmbeddingGemma 300M (D)\n\nGood for: Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms\n\nAhead on:\n- Documentation and upkeep, 42 against 21\n\nWatch for: Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy 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 40\n- Reach, 100 against 70\n- Risk, 55 against 45\n- Agent interface, 65 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5\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 300M | Qwen3-Embedding-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 40 | 81 | Qwen3-Embedding-4B +41 |\n| Reach | 25% (25 this run) | 70 | 100 | Qwen3-Embedding-4B +30 |\n| Risk | 20% (20 this run) | 45 | 55 | Qwen3-Embedding-4B +10 |\n| Agent interface | 10% (10 this run) | 60 | 65 | Qwen3-Embedding-4B +5 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 21 | EmbeddingGemma 300M +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.7 · D** | **72.9 · BB** | |\n\n## Facts side by side\n\n| Fact | EmbeddingGemma 300M | 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 | Gemma Terms of Use | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2025-09-25 | 2025-08-03 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**EmbeddingGemma 300M.** A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.\n\n**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 300M\n\n1. Without a Hugging Face account, run `ollama pull embeddinggemma:300m-qat-q4_0` and POST to /api/embed\n2. Prepend the task prompt yourself outside Sentence Transformers, `task: search result | query: ` for queries and `title: none | text: ` for documents\n3. In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added\n4. To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise\n5. Run it in float32 or bfloat16, never float16\n6. Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model\n\n### 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 300M or Qwen3-Embedding-4B?\n\nQwen3-Embedding-4B scores 72.9 (BB) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. EmbeddingGemma 300M leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"embeddinggemma-300m\", \"b\": \"qwen3-embedding-4b\"}`. From a terminal: `anchor compare embeddinggemma-300m qwen3-embedding-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.json\n\n## Other comparisons with EmbeddingGemma 300M or Qwen3-Embedding-4B\n\n- [EmbeddingGemma 300M vs Granite Embedding Small English R2](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-granite-embedding-small-english-r2.md)\n- [EmbeddingGemma 300M vs jina-embeddings-v5-omni-small](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md)\n- [EmbeddingGemma 300M vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-8b.md)\n- [EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.md)\n- [EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.md)\n- [Granite Embedding Small English R2 vs 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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "EmbeddingGemma 300M vs Qwen3-Embedding-4B", "url": "" } ], "description": "Qwen3-Embedding-4B scores 72.9 (BB) to EmbeddingGemma 300M's 50.7 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "EmbeddingGemma 300M D 50.7", "Qwen3-Embedding-4B BB 72.9", "scores" ], "h1": "EmbeddingGemma 300M vs Qwen3-Embedding-4B", "image": "https://www.anchorterminal.com/assets/og/compare-embeddinggemma-300m-vs-qwen3-embedding-4b.png", "path": "/compare/embeddinggemma-300m-vs-qwen3-embedding-4b", "published": "2026-10-01", "section": "tools", "title": "EmbeddingGemma 300M vs Qwen3-Embedding-4B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-4b" }, "tokens": { "markdown": 2200, "slim": 530 }, "version": 1 }