{ "data": { "a": { "slug": "mimo-v2-6-flash-rl", "name": "MiMo-V2.6-Flash-RL", "vendor": "Xiaomi (MiMo team)", "vendorUrl": "https://mimo.xiaomi.com", "kind": "weights", "category": "open-weights", "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.", "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl", "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json", "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com", "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "license": "MIT (metadata only, no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "audio", "video", "reasoning", "tool-calling", "agentic", "long-context", "mit", "safetensors", "fp4", "custom-code", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1518, "categoryRank": 59, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 45, "upkeep": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.", "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "strengths": [ "MIT, ungated, with hosted options", "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang", "A 1M context and four input types", "Trained with reinforcement learning across agent harnesses", "A GGUF from the llama.cpp project's own organisation" ], "weaknesses": [ "178 GB at 4 bits, so it needs about 200 GB of memory", "The card's serve commands pass trust-remote-code", "A pickle file sits in the draft model's folder", "No safety results, limitations or data sources on the card", "Xiaomi replied in a few of the discussions only" ], "agentNotes": [ "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita", "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`", "Use temperature 1.0 and top_p 0.95, as the card recommends", "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in", "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle", "The Use Restrictions section asks for human oversight of high-risk actions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 45, "upkeep": 59 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags", "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mimo-v2-6-flash-rl" }, "sameCompany": [ "mimo-v2-6-distill-qwen-9b" ], "weights": { "trainedBy": "Xiaomi (MiMo team)", "params": "309B", "active": "15B", "context": 1048576, "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiMo-V2.6-Flash-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "who": "publisher", "precision": "fp4", "sizeGB": 177.74, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build Xiaomi publishes, with most weights at 4 bits" }, { "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)", "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF", "who": "llama.cpp", "precision": "mxfp4", "sizeGB": 168.93, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026" } ], "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo", "runBuild": "MiMo-V2.6-Flash-RL", "checked": "2026-11-10", "minMemoryGB": 256 }, "family": { "lab": "Xiaomi (MiMo)", "family": "MiMo-V2 (V2, V2.5, V2.6)", "url": "https://www.anchorterminal.com/open-weights/#xiaomi", "others": [ { "name": "MiMo-V2.6-Distill-Qwen-9B", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B", "params": "9B (Hub safetensors count 9.41B)", "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b" }, { "name": "MiMo-V2.6-Pro-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Flash-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Pro-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-ASR", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR", "params": "7.62B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base", "params": "309.77B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash", "params": "309.77B (Hub safetensors count; the name states none)" } ], "olderCount": 12 }, "area": "models", "provenance": { "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)", "domain": "mimo.xiaomi.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.", "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 13 commits from 21 September to 28 October 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json" }, "answer": "Qwen3.6-27B scores 66.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep.", "b": { "slug": "qwen3-6-27b", "name": "Qwen3.6-27B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.6-27B is Alibaba's open-weight dense vision-language model of 27B parameters, tuned for agentic coding, with thinking on by default, preserved thinking across turns and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-6-27b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json", "dns": "qwen3-6-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.6-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.6-27B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.6-27B-FP8" }, { "registry": "ollama", "name": "qwen3.6:27b" } ], "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. Running it yourself costs the hardware, 55.6 GB for the BF16 files, 30.9 GB for Alibaba's FP8 build and 17 to 18 GB for Ollama's 4-bit builds (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/Qwen3.6-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-04-22", "graded": true, "anchor": { "graded": true, "score": 66.7, "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": 1518, "categoryRank": 12, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 66, "interface": 70, "reach": 90, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.", "bestFor": "Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers", "Ollama's 4-bit build fits in about 22 GB", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a hash on every file in the publisher's own organisation", "Thinking that turns off per request or is kept across turns, several tool calls a turn and 262,144 tokens of native context", "Served by DeepInfra, Featherless AI, OVHcloud and Alibaba's Model Studio" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "Tool format is only in the template and structured output isn't documented", "No limitations, languages, knowledge cut-off or training data on the card", "No reply from Qwen to any of the 40 most recent discussions, including template and tool-calling reports", "Files unchanged since 24 April 2026, and Qwen3.8-27B succeeds it at the same size without a note" ], "agentNotes": [ "Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector", "Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later", "Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call", "Thinking is on by default, pass `\"chat_template_kwargs\": {\"enable_thinking\": false}` for short tasks, and `/think` and `/nothink` aren't supported", "`\"preserve_thinking\": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt", "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without", "Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens", "Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented", "Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.7 } ], "editorialScores": { "capability": 66, "interface": 70, "reach": 90, "risk": 55, "upkeep": 31 }, "provenanceScore": 58, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.6:27b\n# or, with the card's vLLM command on Alibaba's FP8 build:\nvllm serve Qwen/Qwen3.6-27B-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\": \"qwen3.6:27b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-6-27b" }, "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", "qwen-agentworld-35b-a3b", "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", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "27B", "context": 262144, "goodFor": "Dense vision-language model for agentic coding and tool calling, text, images and video in, thinking on by default with reasoning kept across turns if asked.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3.6:27b", "url": "https://ollama.com/library/qwen3.6:27b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with an F16 vision projector (digest bcbdbd4bcf86 on its page, 1e2b3d172b7c on the tags list, 18 to 19 GB across platform variants). The text-only qwen3.6:27b-q4_K_M is 17 GB. Size as Ollama's pages show it, rounded to whole GB above 10 GB" }, { "name": "LM Studio qwen/qwen3.6-27b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3.6-27B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 17.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Qwen3.6-27B-Q4_K_M.gguf (16.55 GB) plus mmproj-Qwen3.6-27B-BF16.gguf (0.93 GB), summed from the Hub's file list. LM Studio's catalogue entry also lists MLX builds, the 4-bit one at 16.1 GB" }, { "name": "Qwen3.6-27B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.6-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.9, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.6-27B-FP8", "checked": "2026-10-21", "minMemoryGB": 24 }, "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": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "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 Alibaba Cloud. The domain's registration date is carried from the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's 13 messages are uploads, README edits, a generation and tokenizer config update and the LICENSE upload, all from 21 to 24 April 2026. The News list in github.com/QwenLM/Qwen3.6 dates the release to 22 April 2026.", "qwen.ai answers every path with its web app, so there is no security.txt, and neither QwenLM/Qwen3.6 nor QwenLM/.github has a SECURITY.md." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-6-27b.json", "live": { "slug": "qwen3-6-27b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 28, "category": "upkeep", "leads": "mimo-v2-6-flash-rl", "name": "Documentation and upkeep", "reasons": { "a": "Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.", "b": "Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply." }, "scores": { "a": 59, "b": 31 } }, { "by": 25, "category": "reach", "leads": "qwen3-6-27b", "name": "Reach", "reasons": { "a": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.", "b": "Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card." }, "scores": { "a": 65, "b": 90 } }, { "by": 15, "category": "interface", "leads": "mimo-v2-6-flash-rl", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native." }, "scores": { "a": 85, "b": 70 } }, { "by": 10, "category": "risk", "leads": "qwen3-6-27b", "name": "Risk", "reasons": { "a": "File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.", "b": "File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a hash on the Hub. Safety evaluation 0, none published on the card or README. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, SECURITY.md or reporting route was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent." }, "scores": { "a": 45, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Xiaomi (MiMo team)", "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": "MIT (metadata only, no LICENSE file)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-28", "b": "2026-04-22", "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": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.6-27B scores 66.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or Qwen3.6-27B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 70", "Documentation and upkeep, 59 against 31" ], "also": null, "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "slug": "mimo-v2-6-flash-rl", "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory" }, { "aheadOn": [ "Reach, 90 against 65", "Risk, 55 against 45" ], "also": null, "goodFor": "Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.", "slug": "qwen3-6-27b", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "history": { "mimo-v2-6-flash-rl": null, "qwen3-6-27b": [ { "causes": [ "time" ], "date": "2026-11-01", "delta": -0.5, "from": 67.2, "fromGrade": "B", "to": 66.7, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.json", "title": "Agents-A1-4B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-27b.json", "title": "Agents-A1-4B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.json", "title": "Apertus v1.5 8B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-27b.json", "title": "Apertus v1.5 8B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-27b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b.json", "title": "Qwen3.5-2B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-27b.json", "title": "Qwen3.5-4B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.json", "title": "Qwen3.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b" }, { "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/qwen3-6-27b-vs-qwen3-8-27b.json", "title": "Qwen3.6-27B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next.json", "title": "Qwen3.6-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.json", "title": "Qwen3.6-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.json", "title": "Qwen3.6-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.6-27B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.json", "title": "Qwen3.6-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.json", "title": "Qwen3.6-27B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.json", "title": "Qwen3.6-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "qwen3-6-27b", "key": "reach", "mimo-v2-6-flash-rl": 65, "name": "Reach", "qwen3-6-27b": 90, "weight": 25 }, { "by": 10, "edge": "qwen3-6-27b", "key": "risk", "mimo-v2-6-flash-rl": 45, "name": "Risk", "qwen3-6-27b": 55, "weight": 20 }, { "by": 15, "edge": "mimo-v2-6-flash-rl", "key": "interface", "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "qwen3-6-27b": 70, "weight": 10 }, { "by": 28, "edge": "mimo-v2-6-flash-rl", "key": "upkeep", "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "qwen3-6-27b": 31, "weight": 10 } ], "summary": "Qwen3.6-27B scores 66.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.", "qwen3-6-27b": "A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b", "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.min.md" }, "markdown": "Qwen3.6-27B scores 66.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n- Qwen3.6-27B: grade B, 66.7/100, rank #16 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Agent interface, 85 against 70\n- Documentation and upkeep, 59 against 31\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### Qwen3.6-27B (B)\n\nGood for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.\n\nAhead on:\n- Reach, 90 against 65\n- Risk, 55 against 45\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +28\n\n- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.\n- Qwen3.6-27B (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.\n\n### Reach: Qwen3.6-27B +25\n\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n- Qwen3.6-27B (90): Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card.\n\n### Agent interface: MiMo-V2.6-Flash-RL +15\n\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Qwen3.6-27B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native.\n\n### Risk: Qwen3.6-27B +10\n\n- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.\n- Qwen3.6-27B (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a hash on the Hub. Safety evaluation 0, none published on the card or README. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, SECURITY.md or reporting route was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | Qwen3.6-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | Qwen3.6-27B +25 |\n| Risk | 20% (20 this run) | 45 | 55 | Qwen3.6-27B +10 |\n| Agent interface | 10% (10 this run) | 85 | 70 | MiMo-V2.6-Flash-RL +15 |\n| Documentation and upkeep | 10% (10 this run) | 59 | 31 | MiMo-V2.6-Flash-RL +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | Qwen3.6-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | 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 | MIT (metadata only, no LICENSE file) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-04-22 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n### Qwen3.6-27B\n\n- -0.5 on 1 November 2026: time\n\n## Verdicts\n\n**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\n\n**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.\n\n## Before you call either\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\n\n### Qwen3.6-27B\n\n1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector\n2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later\n3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call\n4. Thinking is on by default, pass `\"chat_template_kwargs\": {\"enable_thinking\": false}` for short tasks, and `/think` and `/nothink` aren't supported\n5. `\"preserve_thinking\": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt\n6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without\n7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens\n8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented\n9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or Qwen3.6-27B?\n\nQwen3.6-27B scores 66.7 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"qwen3-6-27b\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl qwen3-6-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or Qwen3.6-27B\n\n- [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.md)\n- [Agents-A1-4B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-27b.md)\n- [Apertus v1.5 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.md)\n- [Apertus v1.5 8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-27b.md)\n- [Apodex-1.1-mini vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Apodex-1.1-mini vs Qwen3.6-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-27b.md)\n- [Arete-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b.md)\n- [BigBang-v1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.md)\n- [BigBang-v1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b.md)\n- [Carbon-X vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl.md)\n- [Carbon-X vs Qwen3.6-27B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-27b.md)\n- [Cola2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.md)\n- [Cola2 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/cola2-vs-qwen3-6-27b.md)\n- [Cosmos-Reason2-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl.md)\n- [Cosmos-Reason2-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-27b.md)\n- [Darwin-36B-Opus vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl.md)\n- [Darwin-36B-Opus vs Qwen3.6-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-27b.md)\n- [DeepSeek-V3.2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek-V3.2 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-27b.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.md)\n- [DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-27b.md)\n- [Devstral Small 2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl.md)\n- [Devstral Small 2 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-27b.md)\n- [DiffusionGemma 26B A4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-27b.md)\n- [EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.md)\n- [EXAONE 4.5 33B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-6-27b.md)\n- [Gemma 4 12B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 12B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 26B A4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 26B A4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 31B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 31B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 E2B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 E2B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 E4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 E4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-27b.md)\n- [GLM-4.5-Air vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl.md)\n- [GLM-4.5-Air vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-27b.md)\n- [GLM-4.7-Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.md)\n- [GLM-4.7-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b.md)\n- [GLM-5.3-Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl.md)\n- [GLM-5.3-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.md)\n- [GLM-5.3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.md)\n- [GLM-5.3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-27b.md)\n- [gpt-oss-120b vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl.md)\n- [gpt-oss-120b vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-27b.md)\n- [gpt-oss-20b vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.md)\n- [gpt-oss-20b vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-27b.md)\n- [Granite 4.2 30B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 30B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-27b.md)\n- [Granite 4.2 3B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-27b.md)\n- [Granite 4.2 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-6-27b.md)\n- [Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.md)\n- [Hy-Memory-30B-A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b.md)\n- [Hy3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.md)\n- [Hy3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b.md)\n- [Inkling-Small vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl.md)\n- [Inkling-Small vs Qwen3.6-27B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-27b.md)\n- [Inkling vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.md)\n- [Inkling vs Qwen3.6-27B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-6-27b.md)\n- [Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.md)\n- [Kimi K3.1 Pro vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b.md)\n- [Kimi K3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.md)\n- [Kimi K3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b.md)\n- [Kolibri-1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.md)\n- [Kolibri-1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b.md)\n- [Kronos vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.md)\n- [Kronos vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-27b.md)\n- [Laguna M.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna M.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.md)\n- [Laguna S 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna S 2.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b.md)\n- [Laguna XS 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna XS 2.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-27b.md)\n- [LBS-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.md)\n- [LBS-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.md)\n- [LFM2.5-2.6B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.md)\n- [LFM2.5-2.6B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.md)\n- [LFM3-1.7B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-1.7B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-27b.md)\n- [LFM3-Audio-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-Audio-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-6-27b.md)\n- [LFM3-Omni-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-Omni-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.md)\n- [LFM3-VL-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-VL-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b.md)\n- [Ling-3.0-tiny vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.md)\n- [Ling-3.0-tiny vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.md)\n- [Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl.md)\n- [Ling-3.1-flash-1019 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-6-27b.md)\n- [LongCat-2.5 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.md)\n- [LongCat-2.5 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-27b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM5-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M2.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M2.7](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m3-1-flash.md)\n- [MiMo-V2.6-Flash-RL vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4.md)\n- [MiMo-V2.6-Flash-RL vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-muse-glimmer-30b.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b.md)\n- [MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B 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true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "MiMo-V2.6-Flash-RL vs Qwen3.6-27B", "url": "" } ], "description": "Qwen3.6-27B scores 66.7 (B) to MiMo-V2.6-Flash-RL's 61 (C) for open-weight models you run yourself. 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