{ "data": { "a": { "slug": "minicpm-v-4-7-35b-a3b", "name": "MiniCPM-V 4.7 35B-A3B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json", "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:35b-a3b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS GGUF (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/openbmb/MiniCPM-V-4.7-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "video", "on-device", "tool-calling", "apache-2.0", "safetensors", "gguf", "fp8", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-11-03", "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": 61, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.", "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence", "strengths": [ "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "OpenBMB answered 8 of 12 discussions and dates its releases", "Images, several images and video in one model, with thinking off by default" ], "weaknesses": [ "No limitations section, safety results, agent-use guidance or security contact", "Training data isn't described, and the card gives no context length, languages or knowledge cut-off", "The card shows a tool-calling request but not the reply, and names no parser", "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`", "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost", "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace", "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`", "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends", "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format", "Validate JSON output yourself, since structured output isn't documented" ], "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": 70, "reach": 90, "risk": 55, "upkeep": 68 }, "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": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b" }, "sameCompany": [ "minicpm-v-4-7-1b", "minicpm-o-4-5", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)", "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts", "params": "35.2B (3B active)", "context": 262144, "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed" }, { "name": "MiniCPM-V-4.7-35B-A3B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-35B-A3B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-35B-A3B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4", "who": "publisher", "precision": "int4", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide" }, { "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 24.7, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder" }, { "name": "ollama openbmb/minicpm-v4.7:35b-a3b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it" }, { "name": "MiniCPM-V-4.7-35B-A3B-FP8", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 38, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 28.5, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4.7-35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 70.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 69.4, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "params": "1.55B (1,554,476,272 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/OpenBMB/MiniCPM-V", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.", "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.", "unchecked: the domain's registration date." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json" }, "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "qwen3-5-0-8b", "name": "Qwen3.5-0.8B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-0.8B is the smallest model in Alibaba's Qwen3.5 family, 0.8B language parameters with a vision encoder, thinking off by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0 for prototyping, fine-tuning and research.", "url": "https://www.anchorterminal.com/tools/qwen3-5-0-8b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json", "dns": "qwen3-5-0-8b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-0.8B" }, { "registry": "ollama", "name": "qwen3.5:0.8b" } ], "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, 1.8 GB for the BF16 files and about 1 GB for Ollama's 8-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/Qwen3.5-0.8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 46.7, "grade": "D", "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": 111, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 17, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-05" }, "negative": 0, "verdict": "A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.", "bestFor": "Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU", "strengths": [ "Runs in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers, in about 1 GB at 4 or 8 bits", "Apache 2.0 with nothing added, no account or gate to download", "Safetensors only, no remote code, files hashed in the publisher's organisation", "262,144 tokens native and several tool calls per turn", "The card names prototyping and fine-tuning as the intended uses for its size and warns about thinking loops" ], "weaknesses": [ "No quantised build from Alibaba and no inference provider found", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any discussion, and a template fix waits since April", "Tool format only in the template, structured output undocumented, and no word on where reasoning comes back", "No knowledge cut-off or training data sources on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB", "Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out", "There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking", "Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests", "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": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 46.7 } ], "editorialScores": { "capability": 17, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "provenanceScore": 58, "checked": { "categories": { "capability": "2026-11-05", "interface": "2026-11-05", "reach": "2026-11-05", "risk": "2026-11-05", "upkeep": "2026-11-05" }, "last": "2026-11-05", "oldest": "2026-11-05", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:0.8b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:0.8b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-0-8b" }, "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", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "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)", "builtOn": "Qwen/Qwen3.5-0.8B-Base", "params": "0.8B", "context": 262144, "goodFor": "Tiny vision-language model for prototyping, fine-tuning and light local steps: text, images and video in, tool calling, thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "who": "publisher", "precision": "bf16", "sizeGB": 1.8, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "ollama qwen3.5:0.8b-q8_0", "url": "https://ollama.com/library/qwen3.5:0.8b-q8_0", "who": "ollama", "precision": "q8_0", "sizeGB": 1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q8_0 language model (812 MB) with a BF16 vision projector (207 MB), digest 7c00f78aed0f. The default qwen3.5:0.8b is 1.2 to 1.3 GB across platform variants. Size as Ollama's pages show it on 21 October 2026, not re-read on 5 November" }, { "name": "LM Studio qwen/qwen3.5-0.8b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-0.8B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 0.7, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Qwen3.5-0.8B-Q4_K_M.gguf (0.53 GB) plus mmproj-Qwen3.5-0.8B-BF16.gguf (0.21 GB), from the Hub's model information on 5 November 2026. LM Studio's catalogue entry also lists MLX 4-bit and 8-bit builds (21 October)" } ], "run": "vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-0.8B", "checked": "2026-11-05", "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": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B-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": "2026-11-05", "notes": [ "The LICENSE file reads Copyright Alibaba Cloud. The domain's registration date is carried from the 20 October 2026 check for qwen3-8-27b, which took it from a registry data service this run doesn't use.", "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 16 messages are uploads, README edits and one template and tokenizer config update on 1 March 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 2 March 2026.", "qwen.ai answers every path with its web app, so there is no security.txt, and QwenLM/Qwen3.5 has no SECURITY.md (clone, 5 November 2026)." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.json", "live": { "slug": "qwen3-5-0-8b", "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": 31, "category": "upkeep", "leads": "minicpm-v-4-7-35b-a3b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.", "b": "The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 248 days before this check, within a year (5). No Qwen member has answered any of the 15 discussions that aren't pull requests (0). Qwen3-0.6B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37." }, "scores": { "a": 68, "b": 37 } }, { "by": 10, "category": "reach", "leads": "minicpm-v-4-7-35b-a3b", "name": "Reach", "reasons": { "a": "Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.", "b": "Memory earns the full 30, since LM Studio's Q4_K_M build with its projector is 0.73 GB (0.88 GB with the fifth added) and Ollama's Q8_0 build was 1.0 GB, both far within 16 GB. Alibaba publishes no quantised 0.8B, so builds scores 0. Runtimes reach the 25 cap on llama.cpp, Ollama (through its pinned llama.cpp), vLLM, SGLang, LM Studio and Transformers. Apache 2.0 with nothing added gives terms 15 and the ungated repository download 10. Hosted too scores 0, as the Hub maps no inference provider to this size. 30+25+0+15+10+0 = 80." }, "scores": { "a": 90, "b": 80 } }, { "by": 10, "category": "interface", "leads": "minicpm-v-4-7-35b-a3b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.", "b": "The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60." }, "scores": { "a": 70, "b": 60 } } ], "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", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-03", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3.5-0.8B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 80", "Agent interface, 70 against 60", "Documentation and upkeep, 68 against 37" ], "also": null, "goodFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence", "slug": "minicpm-v-4-7-35b-a3b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" }, { "aheadOn": null, "also": null, "goodFor": "Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU", "slug": "qwen3-5-0-8b", "watchFor": "No quantised build from Alibaba and no inference provider found" } ], "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-minicpm-v-4-7-35b-a3b.json", "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b.json", "title": "Agents-A1-4B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b.json", "title": "Apertus v1.5 8B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b" }, { 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MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b.json", "title": "GLM-4.7-Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-35b-a3b.json", "title": "GLM-5.3-Flash vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b.json", "title": "GLM-5.3-Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-35b-a3b.json", "title": "GLM-5.3 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-35b-a3b" }, { "json": 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"https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Granite 4.2 8B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-0-8b.json", "title": "Granite 4.2 8B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Hy-Memory-30B-A3B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b.json", "title": "Hy-Memory-30B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-v-4-7-35b-a3b.json", "title": "Hy3 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b.json", "title": "Hy3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling-Small vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-0-8b.json", "title": "Inkling-Small vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b" }, { "json": 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"json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b.json", "title": "Kimi K3.1 Pro vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kimi K3 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.json", "title": "Kimi K3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kolibri-1 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-0-8b.json", "title": "Kolibri-1 vs Qwen3.5-0.8B", "url": 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"title": "Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b.json", "title": "Laguna S 2.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-35b-a3b.json", "title": "Laguna XS 2.1 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b.json", "title": "Laguna XS 2.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LBS-27B vs MiniCPM-V 4.7 35B-A3B", "url": 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"https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b.json", "title": "LFM3-1.7B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-0-8b.json", "title": "LFM3-Audio-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-Omni-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-0-8b.json", "title": "LFM3-Omni-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b.json", "title": "LFM3-VL-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-v-4-7-35b-a3b.json", "title": "Ling-3.0-tiny vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.json", "title": "Ling-3.0-tiny vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b" }, { "json": 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"https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-0-8b.json", "title": "North Mini Code 1.0 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b.json", "title": "occamy-1.0 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b.json", "title": "Olmo 3 7B Think vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-122B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-397B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-9B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-0-8b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-4b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next.json", "title": "Qwen3.5-0.8B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint.json", "title": "Qwen3.5-0.8B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview.json", "title": "Qwen3.5-0.8B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.json", "title": "Qwen3.5-0.8B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-0.8B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-step-3-7-flash.json", "title": "Qwen3.5-0.8B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-0.8B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-0.8B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-0.8B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b.json", "title": "Qwen3.5-0.8B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "minicpm-v-4-7-35b-a3b", "key": "reach", "minicpm-v-4-7-35b-a3b": 90, "name": "Reach", "qwen3-5-0-8b": 80, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "minicpm-v-4-7-35b-a3b": 55, "name": "Risk", "qwen3-5-0-8b": 55, "weight": 20 }, { "by": 10, "edge": "minicpm-v-4-7-35b-a3b", "key": "interface", "minicpm-v-4-7-35b-a3b": 70, "name": "Agent interface", "qwen3-5-0-8b": 60, "weight": 10 }, { "by": 31, "edge": "minicpm-v-4-7-35b-a3b", "key": "upkeep", "minicpm-v-4-7-35b-a3b": 68, "name": "Documentation and upkeep", "qwen3-5-0-8b": 37, "weight": 10 } ], "summary": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "minicpm-v-4-7-35b-a3b": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.", "qwen3-5-0-8b": "A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b", "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.min.md" }, "markdown": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n- Qwen3.5-0.8B: grade D, 46.7/100, rank #156 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-0-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.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### MiniCPM-V 4.7 35B-A3B (C)\n\nGood for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence\n\nAhead on:\n- Reach, 90 against 80\n- Agent interface, 70 against 60\n- Documentation and upkeep, 68 against 37\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### Qwen3.5-0.8B (D)\n\nGood for: Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU\n\nWatch for: No quantised build from Alibaba and no inference provider found\n\n\n## Why the edge\n\n### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +31\n\n- MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.\n- Qwen3.5-0.8B (37): The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 248 days before this check, within a year (5). No Qwen member has answered any of the 15 discussions that aren't pull requests (0). Qwen3-0.6B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37.\n\n### Reach: MiniCPM-V 4.7 35B-A3B +10\n\n- MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- Qwen3.5-0.8B (80): Memory earns the full 30, since LM Studio's Q4_K_M build with its projector is 0.73 GB (0.88 GB with the fifth added) and Ollama's Q8_0 build was 1.0 GB, both far within 16 GB. Alibaba publishes no quantised 0.8B, so builds scores 0. Runtimes reach the 25 cap on llama.cpp, Ollama (through its pinned llama.cpp), vLLM, SGLang, LM Studio and Transformers. Apache 2.0 with nothing added gives terms 15 and the ungated repository download 10. Hosted too scores 0, as the Hub maps no inference provider to this size. 30+25+0+15+10+0 = 80.\n\n### Agent interface: MiniCPM-V 4.7 35B-A3B +10\n\n- MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.\n- Qwen3.5-0.8B (60): The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 35B-A3B | Qwen3.5-0.8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 80 | MiniCPM-V 4.7 35B-A3B +10 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | MiniCPM-V 4.7 35B-A3B +10 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 37 | MiniCPM-V 4.7 35B-A3B +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.7 · D** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 35B-A3B | Qwen3.5-0.8B |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-03 | 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 | 2.8/5 (4) |\n\n## Verdicts\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\n\n**Qwen3.5-0.8B.** A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.\n\n## Before you call either\n\n### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Qwen3.5-0.8B\n\n1. Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB\n2. Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out\n4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking\n5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests\n6. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3.5-0.8B?\n\nMiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-0-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-35b-a3b\", \"b\": \"qwen3-5-0-8b\"}`. 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