{ "data": { "a": { "slug": "minicpm-o-4-5", "name": "MiniCPM-o 4.5", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json", "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-awq" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm-o4.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/OpenSQZ/MiniCPM-V-CookBook", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "omni", "vision", "speech", "voice-cloning", "full-duplex", "reasoning", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-16", "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": 1197, "categoryRank": 35, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.", "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "strengths": [ "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB", "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation", "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too", "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds", "Repository changed 5 days ago and earlier versions stay in their own repositories" ], "weaknesses": [ "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni", "Two pickle files (flow.pt and hift.pt) in the speech stage", "No safety results or guidance, though it clones voices from a short clip", "Tool calling, structured output and where the reasoning comes back aren't documented", "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions" ], "agentNotes": [ "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB", "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does", "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni", "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off", "Keep prompts within 40,960 tokens", "Don't rely on tool calls or JSON output, neither is documented; validate any you get" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "provenanceScore": 53, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n --max-model-len 4096 --max-num-batched-tokens 2048", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-o-4-5" }, "sameCompany": [ "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)", "params": "9.4B", "context": 40960, "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.", "runtimes": [ "vLLM", "llama.cpp", "Ollama" ], "builds": [ { "name": "MiniCPM-o-4_5-gguf Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni" }, { "name": "MiniCPM-o-4_5-gguf Q4_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 5.87, "memoryGB": 8, "format": "gguf", "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair" }, { "name": "ollama openbmb/minicpm-o4.5:latest", "url": "https://ollama.com/openbmb/minicpm-o4.5/tags", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it" }, { "name": "MiniCPM-o-4_5-gguf Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "note": "8.71 GB language file plus the 1.10 GB vision file" }, { "name": "MiniCPM-o-4_5-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 9.77, "memoryGB": 12, "format": "safetensors", "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "MiniCPM-o-4_5-awq", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq", "who": "publisher", "precision": "awq-int4", "sizeGB": 13.07, "memoryGB": 16, "format": "safetensors", "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code" }, { "name": "MiniCPM-o-4_5-gguf F16", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "f16", "sizeGB": 17.48, "memoryGB": 24, "format": "gguf", "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input" }, { "name": "MiniCPM-o-4_5", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "who": "publisher", "precision": "bf16", "sizeGB": 20.05, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "Transformers (remote code)" ], "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0" } ], "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048", "runBuild": "MiniCPM-o-4_5", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-o 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "olderCount": 22 }, "alsoIn": [ "voice-agents" ], "area": "models", "provenance": { "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.", "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json" }, "answer": "Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "b": { "slug": "qwen3-5-397b-a17b", "name": "Qwen3.5-397B-A17B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-397B-A17B is Alibaba's largest open-weight Qwen3.5 model, a mixture-of-experts vision-language model of 397B parameters with 17B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json", "repo": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-GPTQ-Int4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says Qwen3.5-Plus on Alibaba Cloud Model Studio is the hosted version of this model, and several inference providers serve it per token. Running it yourself costs the hardware, 806.8 GB for the BF16 files, 406.2 GB in FP8 and 235.8 GB for Alibaba's GPTQ 4-bit 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/Qwen3.5-397B-A17B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "large", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gptq", "self-hosted" ], "lastRelease": "2026-02-16", "graded": true, "anchor": { "graded": true, "score": 64.6, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 15, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.", "bestFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "strengths": [ "Released support in llama.cpp, vLLM, SGLang, Transformers and LM Studio", "Alibaba's own FP8 and GPTQ 4-bit builds", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "262,144 tokens native, several tool calls per turn and thinking that turns off per request", "Hosted by Alibaba as Qwen3.5-Plus and by at least five other providers" ], "weaknesses": [ "No build fits a 256 GB machine, and Ollama has no local build", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Alibaba to any of the 40 most recent discussions, including template bug reports", "Tool format is only in the template and structured output isn't documented", "No limitations, knowledge cut-off or training data sources on the card" ], "agentNotes": [ "The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory", "It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)", "On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`", "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without", "For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`", "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.6 } ], "editorialScores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 39 }, "provenanceScore": 49, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's vLLM command, tensor parallel on 8 GPUs:\nvllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Qwen/Qwen3.5-397B-A17B-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-397b-a17b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "397B total, 17B active", "context": 262144, "goodFor": "Alibaba's largest open Qwen3.5, a mixture-of-experts vision-language model for coding, agents and tool calling on a multi-GPU server, thinking on by default.", "runtimes": [ "llama.cpp", "LM Studio", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.5-397B-A17B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 406.2, "memoryGB": 512, "format": "safetensors" }, { "name": "Qwen3.5-397B-A17B-GPTQ-Int4", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4", "who": "publisher", "precision": "gptq-int4", "sizeGB": 235.8, "memoryGB": 512, "format": "safetensors", "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-MLX-4bit", "who": "lmstudio", "precision": "mlx-4bit", "sizeGB": 223.9, "memoryGB": 512, "format": "mlx", "runsOn": [ "LM Studio (Apple silicon)" ], "note": "The smallest build at 4 bits or better from the publisher or a runtime's library, summed from the Hub's file list. LM Studio's catalogue entry states a minimum of 230 GB of system memory" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 241.5, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "240.6 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-397B-A17B-FP8", "checked": "2026-10-21", "minMemoryGB": 512 }, "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": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "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 domain's registration date is per 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 messages are uploads, README updates and two merged pull requests (a KTransformers guide on 20 February and a corrected medical VQA score on 15 March 2026), the last commit on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 16 February 2026.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenBMB", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-16", "b": "2026-02-16", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, MiniCPM-o 4.5 or Qwen3.5-397B-A17B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 70", "Documentation and upkeep, 66 against 39" ], "also": null, "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "slug": "minicpm-o-4-5", "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni" }, { "aheadOn": [ "Risk, 55 against 40", "Agent interface, 70 against 35" ], "also": null, "goodFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "slug": "qwen3-5-397b-a17b", "watchFor": "No build fits a 256 GB machine, and Ollama has no local build" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json", "title": "Agents-A1-4B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b.json", "title": "Agents-A1-4B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.json", "title": "Apertus v1.5 8B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.json", "title": "Apertus v1.5 8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.json", "title": "Apodex-1.1-mini vs MiniCPM-o 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"json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5.json", "title": "Gemma 4 E2B IT vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 E2B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.json", "title": "Gemma 4 E4B IT vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 E4B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.json", "title": "GLM-4.5-Air vs 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"https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.json", "title": "GLM-5.3-Flash vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5.json", "title": "GLM-5.3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b.json", "title": "GLM-5.3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5.json", "title": "gpt-oss-120b vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-397b-a17b.json", "title": "gpt-oss-120b vs Qwen3.5-397B-A17B", "url": 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"https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.json", "title": "Hy3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b.json", "title": "Hy3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.json", "title": "Inkling-Small vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b.json", "title": "Inkling-Small vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.json", "title": "Inkling vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b.json", "title": "Inkling vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-397b-a17b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.json", "title": "Kimi K3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.json", "title": "Kimi K3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.json", "title": "Kolibri-1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.json", "title": "Kolibri-1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.json", "title": "Laguna M.1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.json", "title": "Laguna M.1 vs Qwen3.5-397B-A17B", "url": 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"https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json", "title": "LFM2.5-2.6B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json", "title": "LFM2.5-2.6B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5.json", "title": "Ling-3.0-tiny vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b.json", "title": "Ling-3.0-tiny vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5.json", "title": "MiMo-V2.6 Distill Qwen 9B vs 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"json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.json", "title": "MiniCPM-o 4.5 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b" }, { "json": 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"Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.json", "title": "Qwen3.5-397B-A17B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-397B-A17B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "minicpm-o-4-5", "key": "reach", "minicpm-o-4-5": 90, "name": "Reach", "qwen3-5-397b-a17b": 70, "weight": 25 }, { "by": 15, "edge": "qwen3-5-397b-a17b", "key": "risk", "minicpm-o-4-5": 40, "name": "Risk", "qwen3-5-397b-a17b": 55, "weight": 20 }, { "by": 35, "edge": "qwen3-5-397b-a17b", "key": "interface", "minicpm-o-4-5": 35, "name": "Agent interface", "qwen3-5-397b-a17b": 70, "weight": 10 }, { "by": 27, "edge": "minicpm-o-4-5", "key": "upkeep", "minicpm-o-4-5": 66, "name": "Documentation and upkeep", "qwen3-5-397b-a17b": 39, "weight": 10 } ], "summary": "Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.", "qwen3-5-397b-a17b": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b", "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.min.md" }, "markdown": "Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n- Qwen3.5-397B-A17B: grade B, 64.6/100, rank #18 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Reach, 90 against 70\n- Documentation and upkeep, 66 against 39\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n### Qwen3.5-397B-A17B (B)\n\nGood for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.\n\nAhead on:\n- Risk, 55 against 40\n- Agent interface, 70 against 35\n\nWatch for: No build fits a 256 GB machine, and Ollama has no local build\n\n\n## Score by category\n\n| Category | Weight | MiniCPM-o 4.5 | Qwen3.5-397B-A17B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 70 | MiniCPM-o 4.5 +20 |\n| Risk | 20% (20 this run) | 40 | 55 | Qwen3.5-397B-A17B +15 |\n| Agent interface | 10% (10 this run) | 35 | 70 | Qwen3.5-397B-A17B +35 |\n| Documentation and upkeep | 10% (10 this run) | 66 | 39 | MiniCPM-o 4.5 +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **64.6 · B** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-o 4.5 | Qwen3.5-397B-A17B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-02-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\n\n**Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.\n\n## Before you call either\n\n### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n### Qwen3.5-397B-A17B\n\n1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory\n2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)\n3. On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`\n4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n5. For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-o 4.5 or Qwen3.5-397B-A17B?\n\nQwen3.5-397B-A17B scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-o-4-5\", \"b\": \"qwen3-5-397b-a17b\"}`. From a terminal: `anchor compare minicpm-o-4-5 qwen3-5-397b-a17b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json\n\n## Other comparisons with MiniCPM-o 4.5 or Qwen3.5-397B-A17B\n\n- [Agents-A1-4B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.md)\n- [Agents-A1-4B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b.md)\n- [Apertus v1.5 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.md)\n- [Apertus v1.5 8B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.md)\n- [Apodex-1.1-mini vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [Apodex-1.1-mini vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-397b-a17b.md)\n- 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"https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "MiniCPM-o 4.5 vs Qwen3.5-397B-A17B", "url": "" } ], "description": "Qwen3.5-397B-A17B scores 64.6 (B) to MiniCPM-o 4.5's 61 (C) for open-weight models you run yourself. 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