{ "data": { "a": { "slug": "kimi-k3", "name": "Kimi K3", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json", "repo": "https://huggingface.co/moonshotai/Kimi-K3", "license": "Kimi K3 License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-09-02", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 24, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "strengths": [ "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn", "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card", "A third-party cyber evaluation by the UK AI Security Institute and CAISI", "Safetensors only, with a SHA-256 for every shard, downloadable with no account", "Served by Moonshot's API and five inference providers for agents without the hardware" ], "weaknesses": [ "1,561 GB of files at its smallest publisher build, far beyond any single machine", "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code", "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered", "No disclosure route for flaws and no prompt-injection guidance", "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages" ], "agentNotes": [ "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect", "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable", "Thinking is always on; set reasoning_effort to low or high when max is too slow", "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones", "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear", "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "provenanceScore": 53, "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": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n --context-length 65536 --mem-fraction-static 0.82 \\\n --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3" }, "weights": { "trainedBy": "Moonshot AI", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Kimi-K3", "url": "https://huggingface.co/moonshotai/Kimi-K3", "who": "publisher", "precision": "mxfp4", "sizeGB": 1560.9, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4; the only build Moonshot publishes" } ], "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "runBuild": "Kimi-K3", "checked": "2026-10-21", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)", "domain": "moonshot.ai", "domainRegistered": "", "domainNote": "moonshot.ai's registration date couldn't be read through RDAP on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.", "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.", "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json" }, "answer": "Qwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk 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": "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-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-27B", "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, "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": "Moonshot AI", "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": "Kimi K3 License (modified MIT)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-02", "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": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk and documentation and upkeep.", "question": "Which is better for AI agents, Kimi K3 or Qwen3.6-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 75 against 58", "Risk, 65 against 55", "Documentation and upkeep, 41 against 36" ], "also": null, "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "slug": "kimi-k3", "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine" }, { "aheadOn": [ "Reach, 85 against 50", "Agent interface, 70 against 65" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "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-kimi-k3.json", "title": "Agents-A1-4B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Apertus v1.5 8B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Apodex-1.1-mini vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "BigBang-v1 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Cosmos-Reason2-2B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Darwin-36B-Opus vs Kimi K3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "DeepSeek-V3.2 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "DeepSeek V4.1 Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "DeepSeek-V4-Flash-0731 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "DiffusionGemma 26B A4B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3" }, { "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-kimi-k3.json", "title": "Gemma 4 12B IT vs Kimi K3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3" }, { "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-kimi-k3.json", "title": 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"https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kimi K3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.json", "title": "Kimi K3 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b.json", "title": "Kolibri-1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna M.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.json", "title": "Ling-3.0-tiny vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b" }, { "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": 17, "edge": "kimi-k3", "key": "capability", "kimi-k3": 75, "name": "Capability", "qwen3-6-35b-a3b": 58, "weight": 35 }, { "by": 35, "edge": "qwen3-6-35b-a3b", "key": "reach", "kimi-k3": 50, "name": "Reach", "qwen3-6-35b-a3b": 85, "weight": 25 }, { "by": 10, "edge": "kimi-k3", "key": "risk", "kimi-k3": 65, "name": "Risk", "qwen3-6-35b-a3b": 55, "weight": 20 }, { "by": 5, "edge": "qwen3-6-35b-a3b", "key": "interface", "kimi-k3": 65, "name": "Agent interface", "qwen3-6-35b-a3b": 70, "weight": 10 }, { "by": 5, "edge": "kimi-k3", "key": "upkeep", "kimi-k3": 41, "name": "Documentation and upkeep", "qwen3-6-35b-a3b": 36, "weight": 10 } ], "summary": "Qwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "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/kimi-k3-vs-qwen3-6-35b-a3b", "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.min.md" }, "markdown": "Qwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #29 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.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### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Capability, 75 against 58\n- Risk, 65 against 55\n- Documentation and upkeep, 41 against 36\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\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- Reach, 85 against 50\n- Agent interface, 70 against 65\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\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 | Kimi K3 | Qwen3.6-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 75 | 58 | Kimi K3 +17 |\n| Reach | 25% (25 this run) | 50 | 85 | Qwen3.6-35B-A3B +35 |\n| Risk | 20% (20 this run) | 65 | 55 | Kimi K3 +10 |\n| Agent interface | 10% (10 this run) | 65 | 70 | Qwen3.6-35B-A3B +5 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 36 | Kimi K3 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **63.2 · B** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | Qwen3.6-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | 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 | Kimi K3 License (modified MIT) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 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 | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\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### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\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, Kimi K3 or Qwen3.6-35B-A3B?\n\nQwen3.6-35B-A3B and Kimi K3 score within a point of each other on agent readiness, 63.2 (B) and 62.4 (B). Kimi K3 leads on capability, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"qwen3-6-35b-a3b\"}`. 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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": "Kimi K3 vs Qwen3.6-35B-A3B", "url": "" } ], "description": "Qwen3.6-35B-A3B and Kimi K3 score within a point of each other for open-weight models you run yourself, 63.2 and 62.4 out of 100. 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