{ "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-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.", "b": { "slug": "qwen3-vl-4b-instruct", "name": "Qwen3-VL-4B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-4B-Instruct is Alibaba's open-weight dense vision-language model of about 4B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json", "dns": "qwen3-vl-4b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:4b-instruct" } ], "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. Running it yourself costs the hardware, 8.89 GB for the BF16 files, 6.04 GB for Alibaba's FP8 build and 2.5 GB plus a 0.45 GB vision file for its Q4_K_M GGUF (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-VL-4B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted", "edge" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 52.6, "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": 102, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.", "bestFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "strengths": [ "About 3.5 GB of memory for Alibaba's own Q4_K_M GGUF with its vision file, with FP8, Q8_0 and F16 builds as well", "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions", "Apache 2.0 with no use policy, downloadable with no account", "262,144 tokens of native context over text, images and video", "Safetensors and GGUF only, with a chat template in the repository" ], "weaknesses": [ "No safety evaluation, agent-use guidance or security contact", "Tool calling isn't documented beyond the template, and Alibaba names no parser", "No thinking mode in this edition and no documented JSON output", "No reply from Qwen to any of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026", "The card leaves out limitations, languages, knowledge cut-off and memory needs", "Only Featherless was found serving it for agents without the hardware" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`", "In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`", "With the GGUF, load the mmproj vision file too, or images won't be read", "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`", "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text", "Validate JSON output yourself, since structured output isn't documented", "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later" ], "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": 52.6 } ], "editorialScores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-vl:4b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-4B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-4B-Instruct --tool-call-parser qwen", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:4b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-4b-instruct" }, "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-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "4.44B", "context": 262144, "goodFor": "Small vision-language model for images, documents and video with text out, fits in 8 GB at 4-bit, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-VL-4B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 6.04, "memoryGB": 8, "format": "safetensors" }, { "name": "Qwen3VL-4B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.73, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB), the vision file" }, { "name": "Qwen3VL-4B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.5 GB language file plus mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB) for image input" }, { "name": "ollama qwen3-vl:4b-instruct", "url": "https://ollama.com/library/qwen3-vl:4b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 3.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector (digest 84f7ed8042fa, updated the day before our check). qwen3-vl:4b, the library default, is the Thinking edition. Size as Ollama's tags page shows it" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 8.89, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-4B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 8.89, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-F16.gguf (0.84 GB), the vision file" } ], "run": "llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-4B-Instruct-Q4_K_M", "checked": "2026-11-04", "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-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "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 (per the first batch's RDAP check of qwen.ai; this model's files name no copyright holder)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The weights repositories have no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. The legal entity and domain date come from the first batch's check of qwen.ai on 20 October 2026; RDAP wasn't queried again.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and uploads that don't say what changed.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.json", "live": { "slug": "qwen3-vl-4b-instruct", "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": 37, "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 misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31." }, "scores": { "a": 68, "b": 31 } }, { "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": "A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60." }, "scores": { "a": 70, "b": 60 } }, { "by": 5, "category": "reach", "leads": "qwen3-vl-4b-instruct", "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": "Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95." }, "scores": { "a": 90, "b": 95 } } ], "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": "2025-11-01", "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-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.", "question": "Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3-VL-4B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 70 against 60", "Documentation and upkeep, 68 against 31" ], "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": [ "Reach, 95 against 90" ], "also": null, "goodFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "slug": "qwen3-vl-4b-instruct", "watchFor": "No safety evaluation, agent-use guidance or security contact" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct.json", "title": "Granite 4.2 30B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Granite 4.2 3B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct" }, { "json": "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-vl-4b-instruct.json", "title": "Granite 4.2 8B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Hy3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Inkling-Small vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct" }, { "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": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.json", "title": "Inkling vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-35b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kimi K3.1 Pro vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kronos vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct.json", "title": "Kronos vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-v-4-7-35b-a3b.json", "title": "Laguna M.1 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-35b-a3b.json", "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-vl-4b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct" }, { "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-vl-4b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct" }, { "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": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.json", "title": "LBS-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-1.7B vs 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}, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-v-4-7-35b-a3b.json", "title": "Ling-3.1-flash-1019 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-4b-instruct.json", "title": "Ling-3.1-flash-1019 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.json", "title": "LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-v-4-7-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-4b-instruct.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.json", "title": "MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B", "url": 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35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2.json", "title": "MiniCPM-V 4.7 35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b.json", "title": "MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct.json", "title": "Nanbeige4.2-3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct.json", "title": "North Mini Code 1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-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/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-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/qwen3-5-4b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-4B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "qwen3-vl-4b-instruct", "key": "reach", "minicpm-v-4-7-35b-a3b": 90, "name": "Reach", "qwen3-vl-4b-instruct": 95, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "minicpm-v-4-7-35b-a3b": 55, "name": "Risk", "qwen3-vl-4b-instruct": 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-vl-4b-instruct": 60, "weight": 10 }, { "by": 37, "edge": "minicpm-v-4-7-35b-a3b", "key": "upkeep", "minicpm-v-4-7-35b-a3b": 68, "name": "Documentation and upkeep", "qwen3-vl-4b-instruct": 31, "weight": 10 } ], "summary": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. 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-vl-4b-instruct": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI." } }, "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-vl-4b-instruct", "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.min.md" }, "markdown": "MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. 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-VL-4B-Instruct: grade D, 52.6/100, rank #137 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.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- Agent interface, 70 against 60\n- Documentation and upkeep, 68 against 31\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n### Qwen3-VL-4B-Instruct (D)\n\nGood for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 95 against 90\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +37\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-VL-4B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.\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-VL-4B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.\n\n### Reach: Qwen3-VL-4B-Instruct +5\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-VL-4B-Instruct (95): Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 35B-A3B | Qwen3-VL-4B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 95 | Qwen3-VL-4B-Instruct +5 |\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 | 31 | MiniCPM-V 4.7 35B-A3B +37 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.6 · D** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 35B-A3B | Qwen3-VL-4B-Instruct |\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 | 2025-11-01 |\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-VL-4B-Instruct.** Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.\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-VL-4B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`\n2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n\n## Questions\n\n### Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3-VL-4B-Instruct?\n\nMiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-35b-a3b\", \"b\": \"qwen3-vl-4b-instruct\"}`. 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"name": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct", "url": "" } ], "description": "MiniCPM-V 4.7 35B-A3B scores 61 (C) to Qwen3-VL-4B-Instruct's 52.6 (D) for open-weight models you run yourself. 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