{ "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": "MiMo-V2.6-Flash-RL and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.", "b": { "slug": "qwen3-5-9b", "name": "Qwen3.5-9B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-9B is Alibaba's open-weight dense vision-language model of 9B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json", "dns": "qwen3-5-9b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-9B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-9B" }, { "registry": "ollama", "name": "qwen3.5:9b" } ], "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 Apache 2.0. Running it yourself costs the hardware, 19.3 GB for the BF16 files and 6.6 GB for Ollama's default 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/Qwen3.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 60.5, "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": 69, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.", "bestFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "strengths": [ "Runs in vLLM, SGLang, llama.cpp, Ollama, LM Studio and released Transformers", "Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB", "Plain Apache 2.0, no gate, no account", "262,144 tokens native, and thinking that turns off per request", "Safetensors only, with a SHA-256 for every shard and a chat template in the repository", "Four inference providers serve it for agents without the hardware" ], "weaknesses": [ "No quantised build from Qwen itself", "No safety evaluation, agent guidance or security contact", "No reply from Qwen in any of the 40 most recent discussions", "No change to the files or card since 2 March 2026", "The tool-call format and structured output aren't documented beyond the template", "No limitations, knowledge cut-off or training data detail on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)", "On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text", "Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes", "Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported", "There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without", "Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.5 } ], "editorialScores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:9b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/Qwen3.5-9B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-9b" }, "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", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-9B-Base", "params": "9B", "context": 262144, "goodFor": "Dense vision-language model for local agents: text, images and video in, tool calling, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3.5:9b", "url": "https://ollama.com/library/qwen3.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest 56671c2ab938), also the smallest non-MLX Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 6.55, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 5.63 GB of Q4_K_M weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 8.28, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 7.36 GB of Q6_K weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-9B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 10.45, "memoryGB": 16, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 9.53 GB of Q8_0 weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-9B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-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": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "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. The registrant (Alibaba Cloud (Singapore) Private Limited) and registration date of qwen.ai are per the RDAP reading of 20 October 2026; rdap.org didn't answer on 21 October.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai redirects /.well-known/security.txt and /robots.txt to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json", "live": { "slug": "qwen3-5-9b", "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": 25, "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 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction." }, "scores": { "a": 59, "b": 34 } }, { "by": 20, "category": "reach", "leads": "qwen3-5-9b", "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 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85." }, "scores": { "a": 65, "b": 85 } }, { "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, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native." }, "scores": { "a": 85, "b": 70 } }, { "by": 10, "category": "risk", "leads": "qwen3-5-9b", "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 on load or to build prompts. Provenance 15, the Qwen organisation linked from Alibaba's QwenLM README, with a SHA-256 for every shard. Safety evaluation 0, none on the card, and the Qwen3.5 blog couldn't be read, so this is couldn't check scored as absent. Agent guidance 0, the card has no prompt-injection guidance, refusal scope or cautions. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 0, no SECURITY.md in Qwen's GitHub repositories, no security.txt on qwen.ai or alibabacloud.com, and whether Alibaba's group security centre takes reports about Qwen models couldn't be checked." }, "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": "2026-03-02", "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": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiMo-V2.6-Flash-RL and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.", "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or Qwen3.5-9B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 70", "Documentation and upkeep, 59 against 34" ], "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, 85 against 65", "Risk, 55 against 45" ], "also": null, "goodFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "slug": "qwen3-5-9b", "watchFor": "No quantised build from Qwen itself" } ], "history": null, "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", 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"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/qwen3-5-9b-vs-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next.json", "title": "Qwen3.5-9B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json", "title": "Qwen3.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json", "title": "Qwen3.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-9B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json", "title": "Qwen3.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json", "title": "Qwen3.5-9B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json", "title": "Qwen3.5-9B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "qwen3-5-9b", "key": "reach", "mimo-v2-6-flash-rl": 65, "name": "Reach", "qwen3-5-9b": 85, "weight": 25 }, { "by": 10, "edge": "qwen3-5-9b", "key": "risk", "mimo-v2-6-flash-rl": 45, "name": "Risk", "qwen3-5-9b": 55, "weight": 20 }, { "by": 15, "edge": "mimo-v2-6-flash-rl", "key": "interface", "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "qwen3-5-9b": 70, "weight": 10 }, { "by": 25, "edge": "mimo-v2-6-flash-rl", "key": "upkeep", "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "qwen3-5-9b": 34, "weight": 10 } ], "summary": "MiMo-V2.6-Flash-RL and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk. 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.", "qwen3-5-9b": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published." } }, "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-qwen3-5-9b", "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.min.md" }, "markdown": "MiMo-V2.6-Flash-RL and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk. 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- Qwen3.5-9B: grade C, 60.5/100, rank #87 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.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 34\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Reach, 85 against 65\n- Risk, 55 against 45\n\nWatch for: No quantised build from Qwen itself\n\n\n## Why the edge\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +25\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- Qwen3.5-9B (34): Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction.\n\n### Reach: Qwen3.5-9B +20\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- Qwen3.5-9B (85): Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85.\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- Qwen3.5-9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native.\n\n### Risk: Qwen3.5-9B +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- Qwen3.5-9B (55): File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the Qwen organisation linked from Alibaba's QwenLM README, with a SHA-256 for every shard. Safety evaluation 0, none on the card, and the Qwen3.5 blog couldn't be read, so this is couldn't check scored as absent. Agent guidance 0, the card has no prompt-injection guidance, refusal scope or cautions. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 0, no SECURITY.md in Qwen's GitHub repositories, no security.txt on qwen.ai or alibabacloud.com, and whether Alibaba's group security centre takes reports about Qwen models couldn't be checked.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Qwen3.5-9B +20 |\n| Risk | 20% (20 this run) | 45 | 55 | Qwen3.5-9B +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 | 34 | MiMo-V2.6-Flash-RL +25 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | Qwen3.5-9B |\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 | 2026-03-02 |\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 | 2.5/5 (4) |\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**Qwen3.5-9B.** A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.\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### Qwen3.5-9B\n\n1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)\n2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text\n3. Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes\n4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported\n5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without\n6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or Qwen3.5-9B?\n\nMiMo-V2.6-Flash-RL and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"qwen3-5-9b\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl qwen3-5-9b`\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/qwen3-5-9b.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or Qwen3.5-9B\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 Qwen3.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b.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 Qwen3.5-9B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-9b.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 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Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.md)\n- [Qwen3.5-9B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.md)\n- [Qwen3.5-9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.md)\n- [Qwen3.5-9B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": 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