{ "data": { "a": { "slug": "gemma-4-e2b-it", "name": "Gemma 4 E2B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-e2b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json", "repo": "https://huggingface.co/google/gemma-4-E2B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-E2B-it" }, { "registry": "huggingface", "name": "google/gemma-4-E2B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-E2B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:e2b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 11.4 GB at BF16, 5.7 GB at SFP8, 2.9 GB at Q4_0, 1.1 GB for the mobile build and 0.84 GB for the mobile build text only to load the weights per Google (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/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "on-device", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 60.4, "grade": "C", "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": 47, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 19, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 5.2 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "131,072 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks", "Chat-template reports since June wait without a reply" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio", "With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.4 } ], "editorialScores": { "capability": 19, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "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 run gemma4:e2b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:e2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-e2b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-E2B", "params": "5.1B", "context": 131072, "goodFor": "Small on-device assistant and agent model, 2.3B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-E2B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-q4_0-gguf/blob/main/gemma-4-E2B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 4.34, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 3.35 GB model and the 0.99 GB projector (gemma-4-E2B-it-mmproj.gguf) for image and audio input" }, { "name": "gemma-4-E2B-it-qat-mobile-ct", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-ct", "who": "publisher", "precision": "wNa8o8", "sizeGB": 2.7, "memoryGB": 8, "format": "safetensors", "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format" }, { "name": "gemma-4-E2B-it-qat-mobile-transformers", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-transformers", "who": "publisher", "precision": "wNa8o8", "sizeGB": 2.49, "memoryGB": 8, "format": "safetensors", "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM" }, { "name": "ollama gemma4:e2b", "url": "https://ollama.com/library/gemma4:e2b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 4.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (3.5 GB) with a BF16 vision projector (987 MB) and a Q8_0 draft model (98 MB) (digest b37049369adf), the llama.cpp variant. gemma4:e2b-it-qat (4.3 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-e2b", "url": "https://lmstudio.ai/models/google/gemma-4-e2b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 4.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E2B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-E2B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 8.35, "memoryGB": 12, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "who": "publisher", "precision": "bf16", "sizeGB": 10.28, "memoryGB": 16, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072", "runBuild": "gemma-4-E2B_q4_0-it", "checked": "2026-10-21", "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-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" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.", "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/gemma-4-e2b-it.json" }, "answer": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "qwen3-6-35b-a3b", "name": "Qwen3.6-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.6-35B-A3B is Alibaba's open-weight mixture-of-experts vision-language model, 35B parameters with 3B active and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json", "repo": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B-FP8" }, { "registry": "ollama", "name": "qwen3.6:35b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the FP8 repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card's Qwen-Agent example also names a hosted version on Alibaba Cloud Model Studio, which isn't listed here. Running it yourself costs the hardware, 71.9 GB for the BF16 files, 37.5 GB for FP8 and 23 to 24 GB for Ollama's default 35b build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-04-16", "graded": true, "anchor": { "graded": true, "score": 63.2, "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": 1197, "categoryRank": 20, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 58, "interface": 70, "reach": 85, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.", "bestFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with vLLM and SGLang recipe pages", "Apache 2.0 with nothing added, files ungated, and three inference providers live on Hugging Face", "Safetensors only with a hash for every file, in the publisher's own organisation", "262,144 tokens native, several tool calls to a turn, and thinking that can be turned off per request" ], "weaknesses": [ "No safety evaluation, no agent-use guidance and no way to report a security flaw", "No reply from a Qwen member in any of its 61 discussions, including reports on tool calling and the template", "No 4-bit or GGUF build from Alibaba, only FP8", "Card leaves out limitations, languages, knowledge cut-off and training data", "Structured output isn't documented, and the files haven't changed since 24 April 2026" ], "agentNotes": [ "Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)", "On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151", "Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later", "Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning", "Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8", "Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "capability": 58, "interface": 70, "reach": 85, "risk": 55, "upkeep": 36 }, "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 run qwen3.6:35b\n# or, with the card's vLLM tool-call command, without its 8-GPU tensor parallel flag:\nvllm serve Qwen/Qwen3.6-35B-A3B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.6:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-6-35b-a3b" }, "sameCompany": [ "qwen3-embedding-0-6b", "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", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "35B", "active": "3B", "context": 262144, "goodFor": "Fast mixture-of-experts vision-language model for coding agents and tool calling, text, images and video in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama qwen3.6:35b", "url": "https://ollama.com/library/qwen3.6:35b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with an F16 vision projector (digest a7eb95c53bcf). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3.6-35B-A3B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.6-35B-A3B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.6-35B-A3B-FP8", "checked": "2026-10-21", "minMemoryGB": 32 }, "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": 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"olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository has three commits, the initial release on 15 April 2026, the LICENSE on 22 April and a README upload on 24 April.", "qwen.ai redirects /robots.txt and /.well-known/security.txt to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.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": "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": "2026-07-20", "b": "2026-04-16", "name": "Last release" }, { "a": "no document linked", "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": "3.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Gemma 4 E2B IT or Qwen3.6-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 85", "Risk, 70 against 55", "Agent interface, 85 against 70", "Documentation and upkeep, 75 against 36" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "slug": "gemma-4-e2b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": [ "Capability, 58 against 19" ], "also": null, "goodFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "slug": "qwen3-6-35b-a3b", "watchFor": "No safety evaluation, no agent-use guidance and no way to report a security flaw" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.json", "title": "Agents-A1-4B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b.json", "title": "Agents-A1-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.json", "title": "Apertus v1.5 8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.json", "title": "Apodex-1.1-mini vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.json", "title": "BigBang-v1 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b.json", "title": "BigBang-v1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b.json", "title": "Cosmos-Reason2-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b.json", "title": "Darwin-36B-Opus vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V3.2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 12B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 26B A4B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 31B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it.json", "title": "Gemma 4 E2B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.json", "title": "Gemma 4 E2B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 E2B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3.json", "title": "Gemma 4 E2B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 E2B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 E2B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b.json", "title": "Gemma 4 E2B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 E2B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.json", "title": "Gemma 4 E2B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling.json", "title": "Gemma 4 E2B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small.json", "title": "Gemma 4 E2B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.json", "title": "Gemma 4 E2B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1.json", "title": "Gemma 4 E2B IT vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1.json", "title": "Gemma 4 E2B IT vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.json", "title": "Gemma 4 E2B IT vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.json", "title": "Gemma 4 E2B IT vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E2B IT vs LFM2.5-2.6B", "url": 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}, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Gemma 4 E2B IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-4b-instruct-2507.json", "title": "Gemma 4 E2B IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-4b-instruct-2507" }, { "json": 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"json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.6-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json", "title": "Qwen3.6-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 39, "edge": "qwen3-6-35b-a3b", "gemma-4-e2b-it": 19, "key": "capability", "name": "Capability", "qwen3-6-35b-a3b": 58, "weight": 35 }, { "by": 10, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 95, "key": "reach", "name": "Reach", "qwen3-6-35b-a3b": 85, "weight": 25 }, { "by": 15, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 70, "key": "risk", "name": "Risk", "qwen3-6-35b-a3b": 55, "weight": 20 }, { "by": 15, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 85, "key": "interface", "name": "Agent interface", "qwen3-6-35b-a3b": 70, "weight": 10 }, { "by": 39, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-6-35b-a3b": 36, "weight": 10 } ], "summary": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-e2b-it": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "qwen3-6-35b-a3b": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b", "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.min.md" }, "markdown": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Gemma 4 E2B IT: grade C, 60.4/100, rank #54 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json\n- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 E2B IT (C)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 85\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: No safety evaluation figures published\n\n### Qwen3.6-35B-A3B (B)\n\nGood for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.\n\nAhead on:\n- Capability, 58 against 19\n\nWatch for: No safety evaluation, no agent-use guidance and no way to report a security flaw\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 E2B IT | Qwen3.6-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 19 | 58 | Qwen3.6-35B-A3B +39 |\n| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 E2B IT +10 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 E2B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E2B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 36 | Gemma 4 E2B IT +39 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.4 · C** | **63.2 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E2B IT | Qwen3.6-35B-A3B |\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 | 2026-07-20 | 2026-04-16 |\n| Terms last updated | no document linked | 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 | 3.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 E2B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.\n\n**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.\n\n## Before you call either\n\n### Gemma 4 E2B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n\n### Qwen3.6-35B-A3B\n\n1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)\n2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151\n3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later\n4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning\n5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8\n6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E2B IT or Qwen3.6-35B-A3B?\n\nQwen3.6-35B-A3B scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e2b-it\", \"b\": \"qwen3-6-35b-a3b\"}`. From a terminal: `anchor compare gemma-4-e2b-it qwen3-6-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json\n\n## Other comparisons with Gemma 4 E2B IT or Qwen3.6-35B-A3B\n\n- [Agents-A1-4B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.md)\n- [Agents-A1-4B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b.md)\n- [Apertus v1.5 8B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.md)\n- [Apertus v1.5 8B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.md)\n- [Apodex-1.1-mini vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.md)\n- [Apodex-1.1-mini vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.md)\n- [BigBang-v1 vs 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"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": "Gemma 4 E2B IT vs Qwen3.6-35B-A3B", "url": "" } ], "description": "Qwen3.6-35B-A3B scores 63.2 (B) to Gemma 4 E2B IT's 60.4 (C) for open-weight models you run yourself. 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