{ "data": { "a": { "slug": "glm-5-3", "name": "GLM-5.3", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3 is Z.ai's open-weight mixture-of-experts model for coding and long-horizon agent work, 744B parameters with 40B active and a 1M-token context, published on Hugging Face in FP8 and BF16 under its own licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3.json", "dns": "glm-5-3.grade.anchorterminal.com", "repo": "https://huggingface.co/zai-org/GLM-5.3", "license": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-BF16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the GLM-5.3 License. A licensee running a model-as-a-service business with more than US$10 billion of revenue in 12 months must pass Z.AI's security review before commercial use. Running it yourself costs the hardware, 755.7 GB of FP8 files (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": "2026-11-02" }, "docsUrl": "https://huggingface.co/zai-org/GLM-5.3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "coding", "long-context", "custom-licence", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 64.6, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 80, "interface": 70, "reach": 55, "risk": 50, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.", "bestFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider", "strengths": [ "Model code in released llama.cpp b11768, vLLM 0.28.0, SGLang 0.5.22 and Transformers 5.12.0", "1,048,576 tokens of native context and three reasoning budgets per request", "Template renders several tool calls per turn, parsed by glm47 in vLLM and SGLang", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "Downloadable with no account, and hosted by Z.ai and eight live providers on Hugging Face" ], "weaknesses": [ "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it", "Commercial use by model-as-a-service operators above US$10 billion revenue needs Z.AI's security review", "The card leaves out limitations, architecture, parameters and cut-off, and zai-org answered 2 of 16 discussions, none since 8 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default", "Don't send `enable_thinking: false`; the template ignores it and always opens a think block", "Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API", "No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.6 } ], "editorialScores": { "capability": 80, "interface": 70, "reach": 55, "risk": 50, "upkeep": 58 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM's recipe, FP8 on one 8xH200 node\nuv pip install \"vllm\u003e=0.28.0\" --torch-backend=auto\nvllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 \\\n --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 \\\n --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice \\\n --served-model-name glm-5.3\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/GLM-5.3-GGUF:UD-Q4_K_XL --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-5.3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"reasoning_effort\": \"low\", \"clear_thinking\": true}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "builtOn": "GLM-5.2's base model (same base, new post-training)", "params": "744B", "active": "40B", "context": 1048576, "goodFor": "Large model for coding and long-horizon agent work with tool calling and always-on reasoning, text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3", "url": "https://huggingface.co/zai-org/GLM-5.3", "who": "publisher", "precision": "fp8", "sizeGB": 755.6, "memoryGB": 1024, "format": "safetensors" }, { "name": "unsloth GLM-5.3-GGUF (UD-Q4_K_XL)", "url": "https://huggingface.co/unsloth/GLM-5.3-GGUF", "who": "unsloth", "precision": "q4_k_xl", "sizeGB": 467.29, "memoryGB": 1024, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Z.ai's. 467.29 GB of UD-Q4_K_XL weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "GLM-5.3-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 1506.7, "memoryGB": 2048, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3", "runBuild": "GLM-5.3", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-5", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-5", "url": "https://huggingface.co/zai-org/GLM-5", "params": "744B-A40B" }, { "name": "GLM-5.1", "url": "https://huggingface.co/zai-org/GLM-5.1", "params": "744B-A40B" }, { "name": "GLM-5.2", "url": "https://huggingface.co/zai-org/GLM-5.2", "params": "744B-A40B" }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "params": "320B-A18B", "listing": "https://www.anchorterminal.com/tools/glm-5-3-flash" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai). The licence names Z.AI", "domain": "z.ai", "domainRegistered": "2017-12-15", "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-5.3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Z.ai's terms of use name JINGSHENG HENGXING TECHNOLOGY PTE.LTD as the operator of Z.ai, and RDAP names the same company as the registrant of z.ai. The weights' LICENSE names Z.AI as the copyright holder.", "Nothing is hosted for this listing, so the GLM-5.3 License stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 11 commits from 27 August to 14 October 2026, whose messages say what changed (two chat template fixes, on 4 September and 14 October, and evaluation results).", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3.json", "live": { "slug": "glm-5-3", "securityTxt": { "url": "https://z.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:36.154640571Z" }, "domain": { "domain": "z.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/z.ai", "checkedAt": "2026-11-08T15:05:18.801433245Z" }, "pages": [ { "url": "https://huggingface.co/zai-org/GLM-5.3/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:24:04.914285738Z", "changedAt": "2026-11-10T16:24:04.914285738Z", "fingerprint": "ff4e45ec78e4" } ], "updatedAt": "2026-11-10T16:24:04.914285738Z" } }, "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk.", "b": { "slug": "minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json", "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:1b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, 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, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (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-4.7-1B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "video", "on-device", "small", "tool-calling", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-30", "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": 60, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.", "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "strengths": [ "Runs in about 2 GB from OpenBMB's own GGUF with its projector", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "Images, several images and video in one small model, with thinking off by default", "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself" ], "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", "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`", "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector", "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace", "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", "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": 95, "risk": 55, "upkeep": 48 }, "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-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-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-4.7-1B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-1b" }, "sameCompany": [ "minicpm-v-4-7-35b-a3b", "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_text with 24 layers", "params": "1.55B", "context": 262144, "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4.7-1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "who": "publisher", "precision": "bf16", "sizeGB": 3.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4.7-1B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 2.37, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 1.87, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4", "who": "publisher", "precision": "int4", "sizeGB": 1.57, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "MiniCPM-V-4_7-1B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 3.13, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.02 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "1.08 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 1.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.84 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.78, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder" }, { "name": "MiniCPM-V-4_7-1B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 1.54, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed" }, { "name": "ollama openbmb/minicpm-v4.7:1b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it" } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b" } ], "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-4-7-1b.json" }, "edges": [ { "by": 40, "category": "reach", "leads": "minicpm-v-4-7-1b", "name": "Reach", "reasons": { "a": "Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5.", "b": "Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95." }, "scores": { "a": 55, "b": 95 } }, { "by": 10, "category": "upkeep", "leads": "glm-5-3", "name": "Documentation and upkeep", "reasons": { "a": "The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of the GLM-5 corpus in the cited report with no cut-off. The last change, a chat template speed-up on 14 October in the FP8 repository and on 15 October in the BF16 one, is within 30 days on 2 November, for 20. Responsiveness is 5, as zai-org answered 2 of the 16 discussions that aren't pull requests, the last on 8 September, which is a few, and no discussion has been opened since 10 October. Version policy is the full 10, with each GLM-5 release in its own repository and a dated blog saying what changed from GLM-5.2. File health is the full 10, with files and card agreeing and no broken-file report waiting by title.", "b": "Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48." }, "scores": { "a": 58, "b": 48 } }, { "by": 5, "category": "risk", "leads": "minicpm-v-4-7-1b", "name": "Risk", "reasons": { "a": "File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later. Provenance is full, files in the zai-org organisation Z.ai links to with a SHA-256 for every shard. Safety evaluation is 0, because the blog mentions safety evaluation and hardening but publishes no results, and agent guidance is 0, with nothing on refusals, prompt injection or cautions. Licence risk is 10, losing 5 for the security review that model-as-a-service operators above US$10 billion of revenue must pass. The security programme is 0, with no security.txt, no SECURITY.md and no advisories.", "b": "File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai (Zhipu AI)", "b": "OpenBMB", "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": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-10-30", "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": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk.", "question": "Which is better for AI agents, GLM-5.3 or MiniCPM-V 4.7 1B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 58 against 48" ], "also": null, "goodFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider", "slug": "glm-5-3", "watchFor": "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build" }, { "aheadOn": [ "Reach, 95 against 55", "Risk, 55 against 50" ], "also": null, "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "slug": "minicpm-v-4-7-1b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" } ], "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-glm-5-3.json", "title": "Agents-A1-4B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.json", "title": "Agents-A1-4B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json", "title": "Apertus v1.5 8B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.json", "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.json", "title": "Apodex-1.1-mini vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.json", "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.json", "title": "Arete-27B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.json", "title": "Arete-27B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.json", "title": "BigBang-v1 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.json", "title": "BigBang-v1 vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3.json", "title": "Carbon-X vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b.json", "title": "Carbon-X vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3.json", "title": "Cola2 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.json", "title": "Cola2 vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3.json", "title": "Cosmos-Reason2-2B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b.json", "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.json", "title": "Darwin-36B-Opus vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b.json", "title": "Darwin-36B-Opus vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.json", "title": "DeepSeek-V3.2 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.json", "title": "DeepSeek-V3.2 vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.json", "title": "DeepSeek V4.1 Flash vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b.json", "title": "DeepSeek V4.1 Flash vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3.json", "title": "DeepSeek-V4-Flash-0731 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-1b.json", "title": 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{ "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b.json", "title": "Granite 4.2 30B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-1b.json", "title": "Granite 4.2 3B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-1b.json", "title": "Granite 4.2 8B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b.json", "title": "Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-v-4-7-1b.json", 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Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.json", "title": "MiniCPM-V 4.7 1B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.json", "title": "MiniCPM-V 4.7 1B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.json", "title": "MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.json", "title": "MiniCPM-V 4.7 1B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.json", "title": "MiniCPM-V 4.7 1B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.json", "title": "MiniCPM-V 4.7 1B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.json", "title": "MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.json", "title": "MiniCPM-V 4.7 1B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "minicpm-v-4-7-1b", "glm-5-3": 55, "key": "reach", "minicpm-v-4-7-1b": 95, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "minicpm-v-4-7-1b", "glm-5-3": 50, "key": "risk", "minicpm-v-4-7-1b": 55, "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "glm-5-3": 70, "key": "interface", "minicpm-v-4-7-1b": 70, "name": "Agent interface", "weight": 10 }, { "by": 10, "edge": "glm-5-3", "glm-5-3": 58, "key": "upkeep", "minicpm-v-4-7-1b": 48, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "glm-5-3": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.", "minicpm-v-4-7-1b": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b", "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.min.md" }, "markdown": "GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk. Both do open-weight models you run yourself.\n\n- GLM-5.3: grade B, 64.6/100, rank #21 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.json\n- MiniCPM-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.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### GLM-5.3 (B)\n\nGood for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider\n\nAhead on:\n- Documentation and upkeep, 58 against 48\n\nWatch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build\n\n### MiniCPM-V 4.7 1B (C)\n\nGood for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model\n\nAhead on:\n- Reach, 95 against 55\n- Risk, 55 against 50\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Reach: MiniCPM-V 4.7 1B +40\n\n- GLM-5.3 (55): Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5.\n- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n\n### Documentation and upkeep: GLM-5.3 +10\n\n- GLM-5.3 (58): The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of the GLM-5 corpus in the cited report with no cut-off. The last change, a chat template speed-up on 14 October in the FP8 repository and on 15 October in the BF16 one, is within 30 days on 2 November, for 20. Responsiveness is 5, as zai-org answered 2 of the 16 discussions that aren't pull requests, the last on 8 September, which is a few, and no discussion has been opened since 10 October. Version policy is the full 10, with each GLM-5 release in its own repository and a dated blog saying what changed from GLM-5.2. File health is the full 10, with files and card agreeing and no broken-file report waiting by title.\n- MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.\n\n### Risk: MiniCPM-V 4.7 1B +5\n\n- GLM-5.3 (50): File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later. Provenance is full, files in the zai-org organisation Z.ai links to with a SHA-256 for every shard. Safety evaluation is 0, because the blog mentions safety evaluation and hardening but publishes no results, and agent guidance is 0, with nothing on refusals, prompt injection or cautions. Licence risk is 10, losing 5 for the security review that model-as-a-service operators above US$10 billion of revenue must pass. The security programme is 0, with no security.txt, no SECURITY.md and no advisories.\n- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | GLM-5.3 | MiniCPM-V 4.7 1B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 95 | MiniCPM-V 4.7 1B +40 |\n| Risk | 20% (20 this run) | 50 | 55 | MiniCPM-V 4.7 1B +5 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 58 | 48 | GLM-5.3 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.6 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3 | MiniCPM-V 4.7 1B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | OpenBMB |\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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-15 | 2026-10-30 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.\n\n**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.\n\n## Before you call either\n\n### GLM-5.3\n\n1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default\n5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block\n6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API\n7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe\n\n### MiniCPM-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3 or MiniCPM-V 4.7 1B?\n\nGLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3\", \"b\": \"minicpm-v-4-7-1b\"}`. From a terminal: `anchor compare glm-5-3 minicpm-v-4-7-1b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3.json and https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json\n\n## Other comparisons with GLM-5.3 or MiniCPM-V 4.7 1B\n\n- [Agents-A1-4B vs GLM-5.3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.md)\n- [Agents-A1-4B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.md)\n- [Apertus v1.5 8B vs GLM-5.3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.md)\n- [Apertus v1.5 8B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.md)\n- [Apodex-1.1-mini vs GLM-5.3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.md)\n- [Apodex-1.1-mini vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs 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run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "GLM-5.3 vs MiniCPM-V 4.7 1B", "url": "" } ], "description": "GLM-5.3 scores 64.6 (B) to MiniCPM-V 4.7 1B's 61 (C) for open-weight models you run yourself. 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