{ "data": { "a": { "slug": "glm-4-7-flash", "name": "GLM-4.7-Flash", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-4.7-Flash is Z.ai's open-weight mixture-of-experts reasoning model, 30B parameters with about 3B active per token, for English and Chinese, published on Hugging Face in BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-4-7-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json", "repo": "https://huggingface.co/zai-org/GLM-4.7-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-4.7-Flash" }, { "registry": "ollama", "name": "glm-4.7-flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 62.5 GB for the BF16 files and 19 GB for Ollama's default build. Z.ai's own API also serves GLM-4.7-Flash, which its docs describe as completely free (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/zai-org/GLM-4.7-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "chinese", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-01-29", "graded": true, "anchor": { "graded": true, "score": 63, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 21, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 59, "interface": 85, "reach": 80, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.", "bestFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released vLLM, SGLang, Transformers and llama.cpp, and a local Ollama build", "Ollama's 19 GB build fits a 24 GB GPU, and BF16 fits our reference machine", "Z.ai documents the glm47 tool parser, OpenAI-style tool declarations and turning thinking off per request", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard" ], "weaknesses": [ "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January", "No quantised build from Z.ai and no LICENSE file in the repository", "No safety evaluation, agent-use guidance or security contact", "The card leaves out intended use, limitations, context length, knowledge cut-off and training data", "config.json and tokenizer_config.json disagree on context length" ], "agentNotes": [ "On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`", "Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build", "Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text", "Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026", "Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default", "Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench", "Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63 } ], "editorialScores": { "capability": 59, "interface": 85, "reach": 80, "risk": 55, "upkeep": 28 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run glm-4.7-flash\n# or, per Z.ai's card:\nvllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 \\\n --enable-auto-tool-choice --served-model-name glm-4.7-flash", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-4.7-flash\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-4-7-flash" }, "sameCompany": [ "glm-4-5-air", "glm-5-3", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "params": "30B", "active": "3B", "context": 202752, "goodFor": "Small mixture-of-experts reasoning model for English and Chinese with tool calling, a local coding and agent model.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama glm-4.7-flash:latest", "url": "https://ollama.com/library/glm-4.7-flash:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "GLM-4.7-Flash", "url": "https://huggingface.co/zai-org/GLM-4.7-Flash", "who": "publisher", "precision": "bf16", "sizeGB": 62.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash", "runBuild": "GLM-4.7-Flash", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-4", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-4-9B", "url": "https://huggingface.co/zai-org/glm-4-9b", "params": "9B" }, { "name": "GLM-4-9B-Chat", "url": "https://huggingface.co/zai-org/glm-4-9b-chat", "params": "9B" }, { "name": "GLM-4-9B-Chat-1M", "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m", "params": "9B" }, { "name": "GLM-4V-9B", "url": "https://huggingface.co/zai-org/glm-4v-9b", "params": "9B" }, { "name": "GLM-4-9B-0414", "url": "https://huggingface.co/zai-org/GLM-4-9B-0414", "params": "9B" }, { "name": "GLM-4-32B-Base-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414", "params": "32B" }, { "name": "GLM-4-32B-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-0414", "params": "32B" }, { "name": "GLM-Z1-9B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414", "params": "9B" }, { "name": "GLM-Z1-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414", "params": "32B" }, { "name": "GLM-Z1-Rumination-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414", "params": "32B" }, { "name": "GLM-4.1V-9B-Base", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base", "params": "9B" }, { "name": "GLM-4.1V-9B-Thinking", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking", "params": "9B" }, { "name": "GLM-4.5-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Base", "params": "355B-A32B" }, { "name": "GLM-4.5", "url": "https://huggingface.co/zai-org/GLM-4.5", "params": "355B-A32B" }, { "name": "GLM-4.5-Air-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base", "params": "106B-A12B" }, { "name": "GLM-4.5-Air", "url": "https://huggingface.co/zai-org/GLM-4.5-Air", "params": "106B-A12B", "listing": "https://www.anchorterminal.com/tools/glm-4-5-air" }, { "name": "GLM-4.5V", "url": "https://huggingface.co/zai-org/GLM-4.5V", "params": "106B-A12B" }, { "name": "GLM-4.6", "url": "https://huggingface.co/zai-org/GLM-4.6", "params": "355B-A32B" }, { "name": "GLM-4.6V", "url": "https://huggingface.co/zai-org/GLM-4.6V", "params": "106B" }, { "name": "GLM-4.6V-Flash", "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash", "params": "9B" }, { "name": "GLM-4.7", "url": "https://huggingface.co/zai-org/GLM-4.7", "params": "355B-A32B" } ], "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)", "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-4.7-Flash/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' card names no legal entity and the repository has no LICENSE file.", "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 12 commits from 19 to 29 January 2026, mostly card and benchmark updates.", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json" }, "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "b": { "slug": "minicpm-o-4-5", "name": "MiniCPM-o 4.5", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json", "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-awq" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm-o4.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/OpenSQZ/MiniCPM-V-CookBook", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "omni", "vision", "speech", "voice-cloning", "full-duplex", "reasoning", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 35, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.", "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "strengths": [ "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB", "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation", "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too", "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds", "Repository changed 5 days ago and earlier versions stay in their own repositories" ], "weaknesses": [ "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni", "Two pickle files (flow.pt and hift.pt) in the speech stage", "No safety results or guidance, though it clones voices from a short clip", "Tool calling, structured output and where the reasoning comes back aren't documented", "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions" ], "agentNotes": [ "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB", "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does", "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni", "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off", "Keep prompts within 40,960 tokens", "Don't rely on tool calls or JSON output, neither is documented; validate any you get" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "provenanceScore": 53, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n --max-model-len 4096 --max-num-batched-tokens 2048", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-o-4-5" }, "sameCompany": [ "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)", "params": "9.4B", "context": 40960, "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.", "runtimes": [ "vLLM", "llama.cpp", "Ollama" ], "builds": [ { "name": "MiniCPM-o-4_5-gguf Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni" }, { "name": "MiniCPM-o-4_5-gguf Q4_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 5.87, "memoryGB": 8, "format": "gguf", "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair" }, { "name": "ollama openbmb/minicpm-o4.5:latest", "url": "https://ollama.com/openbmb/minicpm-o4.5/tags", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it" }, { "name": "MiniCPM-o-4_5-gguf Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "note": "8.71 GB language file plus the 1.10 GB vision file" }, { "name": "MiniCPM-o-4_5-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 9.77, "memoryGB": 12, "format": "safetensors", "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "MiniCPM-o-4_5-awq", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq", "who": "publisher", "precision": "awq-int4", "sizeGB": 13.07, "memoryGB": 16, "format": "safetensors", "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code" }, { "name": "MiniCPM-o-4_5-gguf F16", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "f16", "sizeGB": 17.48, "memoryGB": 24, "format": "gguf", "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input" }, { "name": "MiniCPM-o-4_5", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "who": "publisher", "precision": "bf16", "sizeGB": 20.05, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "Transformers (remote code)" ], "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0" } ], "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048", "runBuild": "MiniCPM-o-4_5", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-o 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "olderCount": 22 }, "alsoIn": [ "voice-agents" ], "area": "models", "provenance": { "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.", "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json" }, "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": "MIT", "b": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-01-29", "b": "2026-10-16", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, GLM-4.7-Flash or MiniCPM-o 4.5?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 40", "Agent interface, 85 against 35" ], "also": null, "goodFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime", "slug": "glm-4-7-flash", "watchFor": "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January" }, { "aheadOn": [ "Reach, 90 against 80", "Documentation and upkeep, 66 against 28" ], "also": null, "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "slug": "minicpm-o-4-5", "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, 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{ "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.json", "title": "Inkling-Small vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.json", "title": "Inkling vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.json", "title": "Kimi K3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.json", "title": "Kolibri-1 vs MiniCPM-o 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"https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.json", "title": "MiniCPM-o 4.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json", "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "minicpm-o-4-5", "glm-4-7-flash": 80, "key": "reach", "minicpm-o-4-5": 90, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "glm-4-7-flash", "glm-4-7-flash": 55, "key": "risk", "minicpm-o-4-5": 40, "name": "Risk", "weight": 20 }, { "by": 50, "edge": "glm-4-7-flash", "glm-4-7-flash": 85, "key": "interface", "minicpm-o-4-5": 35, "name": "Agent interface", "weight": 10 }, { "by": 38, "edge": "minicpm-o-4-5", "glm-4-7-flash": 28, "key": "upkeep", "minicpm-o-4-5": 66, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "glm-4-7-flash": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.", "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5", "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.min.md" }, "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json\n- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### GLM-4.7-Flash (B)\n\nGood for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime\n\nAhead on:\n- Risk, 55 against 40\n- Agent interface, 85 against 35\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Reach, 90 against 80\n- Documentation and upkeep, 66 against 28\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n\n## Score by category\n\n| Category | Weight | GLM-4.7-Flash | MiniCPM-o 4.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 90 | MiniCPM-o 4.5 +10 |\n| Risk | 20% (20 this run) | 55 | 40 | GLM-4.7-Flash +15 |\n| Agent interface | 10% (10 this run) | 85 | 35 | GLM-4.7-Flash +50 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 66 | MiniCPM-o 4.5 +38 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.7-Flash | MiniCPM-o 4.5 |\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 | MIT | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-01-29 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.\n\n**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\n\n## Before you call either\n\n### GLM-4.7-Flash\n\n1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`\n2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build\n3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text\n4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026\n5. Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default\n6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench\n7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000\n\n### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n## Questions\n\n### Which is better for AI agents, GLM-4.7-Flash or MiniCPM-o 4.5?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"minicpm-o-4-5\"}`. From a terminal: `anchor compare glm-4-7-flash minicpm-o-4-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json\n\n## Other comparisons with GLM-4.7-Flash or MiniCPM-o 4.5\n\n- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.md)\n- [Agents-A1-4B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.md)\n- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)\n- [Apertus v1.5 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.md)\n- [Apodex-1.1-mini vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.md)\n- [Apodex-1.1-mini vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [BigBang-v1 vs 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