{ "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", "dns": "glm-4-7-flash.grade.anchorterminal.com", "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": "2026-11-02" }, "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": 1518, "categoryRank": 20, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 59, "interface": 85, "reach": 80, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "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": "0.7", "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": 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": "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\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-4.7-Flash-GGUF:Q4_K --jinja", "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": "ggml-org GLM-4.7-Flash-GGUF (Q4_K)", "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF", "who": "llama.cpp", "precision": "q4_k", "sizeGB": 18.24, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 18.24 GB of Q4_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "ggml-org GLM-4.7-Flash-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 31.84, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 31.84 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "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": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json", "live": { "slug": "glm-4-7-flash", "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-4.7-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:24:00.649563516Z", "changedAt": "2026-11-10T16:24:00.649563516Z", "fingerprint": "7db42760a7b7" } ], "updatedAt": "2026-11-10T16:24:00.649563516Z" } }, "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep.", "b": { "slug": "lfm3-omni-2b", "name": "LFM3-Omni-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-Omni-2B is Liquid AI's open-weight 2.17B omni model for on-device use, taking text, images, video and audio and answering in text or speech, with tool calling and a 32K-token context, released on 27 October 2026 with GGUF and MLX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-omni-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json", "dns": "lfm3-omni-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-omni" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.29 GB projector file and 4.49 GB in BF16. Liquid's API also serves it, at prices shown in its console (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://docs.liquid.ai/lfm/models/lfm3-omni-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "omni", "vision", "video", "audio", "speech", "small", "edge", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "onnx", "self-hosted" ], "lastRelease": "2026-11-03", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 57, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.", "bestFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "strengths": [ "One model for text, images, video, audio and speech on an 8 GB machine", "Speech output and a realtime speech server in released llama.cpp", "Tool calls from spoken requests, in a documented format", "Updated a week before this check", "Liquid's own API serves it" ], "weaknesses": [ "Only llama.cpp and Ollama run it in a release", "A 32K-token context, which video and audio fill quickly", "It doesn't reason, and structured output isn't documented", "None of 12 questions on the repository has a reply from Liquid", "Loading in Transformers runs 14 Python files from the repository", "No safety results, security contact or training data description" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`", "Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card", "The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames", "Parse tool calls yourself from the XML it returns", "It doesn't reason, so don't pass enable_thinking", "Validate JSON output yourself" ], "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": 80, "risk": 40, "upkeep": 49 }, "provenanceScore": 73, "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": "llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-omni", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm3-omni-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)", "params": "2.17B", "context": 32000, "goodFor": "One small on-device model in place of separate speech recognition, vision, language and speech models, for voice chat, questions about video with its soundtrack and tool calls from spoken requests. It doesn't reason.", "runtimes": [ "llama.cpp", "Ollama" ], "builds": [ { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.49, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model" }, { "name": "LFM3-Omni-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.36, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Omni-2B-Q8_0.gguf (0.51 GB)" }, { "name": "LFM3-Omni-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.39, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Omni-2B-Q4_K_M.gguf (0.29 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-Omni-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 2.11, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-omni", "url": "https://ollama.com/library/lfm3-omni", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags" } ], "run": "llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-Omni-2B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM3", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM3-1.7B-Base", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base", "params": "1.73B" }, { "name": "LFM3-1.7B", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B", "params": "1.73B", "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b" }, { "name": "LFM3-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "params": "1.92B", "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b" }, { "name": "LFM3-Audio-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "params": "2.00B", "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b" } ] }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LiquidAI/LFM3-Omni-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026", "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026" ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.json" }, "edges": [ { "by": 21, "category": "upkeep", "leads": "lfm3-omni-2b", "name": "Documentation and upkeep", "reasons": { "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.", "b": "Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 14 + 5 + 20 + 0 + 5 + 5 = 49." }, "scores": { "a": 28, "b": 49 } }, { "by": 15, "category": "risk", "leads": "glm-4-7-flash", "name": "Risk", "reasons": { "a": "File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40." }, "scores": { "a": 55, "b": 40 } }, { "by": 15, "category": "interface", "leads": "glm-4-7-flash", "name": "Agent interface", "reasons": { "a": "A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.", "b": "Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70." }, "scores": { "a": 85, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai (Zhipu AI)", "b": "Liquid AI", "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-01-29", "b": "2026-11-03", "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-4.7-Flash scores 63 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep.", "question": "Which is better for AI agents, GLM-4.7-Flash or LFM3-Omni-2B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 40", "Agent interface, 85 against 70" ], "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": [ "Documentation and upkeep, 49 against 28" ], "also": null, "goodFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "slug": "lfm3-omni-2b", "watchFor": "Only llama.cpp and Ollama run it in a release" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.json", "title": "LFM3-Omni-2B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-35b-a3b.json", "title": "LFM3-Omni-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next.json", "title": "LFM3-Omni-2B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next.json", "title": "LFM3-Omni-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-32b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM3-Omni-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint.json", "title": "LFM3-Omni-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ripple-65b-preview.json", "title": "LFM3-Omni-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview.json", "title": "LFM3-Omni-2B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-smollm3-3b.json", "title": "LFM3-Omni-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b.json", "title": "LFM3-Omni-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.json", "title": "LFM3-Omni-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM3-Omni-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.json", "title": "LFM3-Omni-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b.json", "title": "LFM3-Omni-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "glm-4-7-flash": 80, "key": "reach", "lfm3-omni-2b": 80, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "glm-4-7-flash", "glm-4-7-flash": 55, "key": "risk", "lfm3-omni-2b": 40, "name": "Risk", "weight": 20 }, { "by": 15, "edge": "glm-4-7-flash", "glm-4-7-flash": 85, "key": "interface", "lfm3-omni-2b": 70, "name": "Agent interface", "weight": 10 }, { "by": 21, "edge": "lfm3-omni-2b", "glm-4-7-flash": 28, "key": "upkeep", "lfm3-omni-2b": 49, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "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.", "lfm3-omni-2b": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b", "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b.min.md" }, "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.7-Flash: grade B, 63/100, rank #26 of 160 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- LFM3-Omni-2B: grade C, 61/100, rank #71 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-omni-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.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-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 70\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n### LFM3-Omni-2B (C)\n\nGood for: Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools\n\nAhead on:\n- Documentation and upkeep, 49 against 28\n\nWatch for: Only llama.cpp and Ollama run it in a release\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-Omni-2B +21\n\n- GLM-4.7-Flash (28): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.\n- LFM3-Omni-2B (49): Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 14 + 5 + 20 + 0 + 5 + 5 = 49.\n\n### Risk: GLM-4.7-Flash +15\n\n- GLM-4.7-Flash (55): File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.\n- LFM3-Omni-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n\n### Agent interface: GLM-4.7-Flash +15\n\n- GLM-4.7-Flash (85): A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.\n- LFM3-Omni-2B (70): Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.\n\n## Score by category\n\n| Category | Weight | GLM-4.7-Flash | LFM3-Omni-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 80 | even |\n| Risk | 20% (20 this run) | 55 | 40 | GLM-4.7-Flash +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.7-Flash +15 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 49 | LFM3-Omni-2B +21 |\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 | LFM3-Omni-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | Liquid AI |\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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-01-29 | 2026-11-03 |\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-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**LFM3-Omni-2B.** One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.\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### LFM3-Omni-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`\n2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card\n3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames\n4. Parse tool calls yourself from the XML it returns\n5. It doesn't reason, so don't pass enable_thinking\n6. Validate JSON output yourself\n\n## Questions\n\n### Which is better for AI agents, GLM-4.7-Flash or LFM3-Omni-2B?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-omni-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"lfm3-omni-2b\"}`. From a terminal: `anchor compare glm-4-7-flash lfm3-omni-2b`\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/lfm3-omni-2b.json\n\n## Other comparisons with GLM-4.7-Flash or LFM3-Omni-2B\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 LFM3-Omni-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-omni-2b.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 LFM3-Omni-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-omni-2b.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 LFM3-Omni-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-omni-2b.md)\n- [Arete-27B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.md)\n- [Arete-27B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.md)\n- [BigBang-v1 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.md)\n- [BigBang-v1 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-omni-2b.md)\n- [Carbon-X vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.md)\n- [Carbon-X vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-omni-2b.md)\n- [Cola2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.md)\n- [Cola2 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.md)\n- [Cosmos-Reason2-2B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.md)\n- [Cosmos-Reason2-2B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-omni-2b.md)\n- [Darwin-36B-Opus vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.md)\n- [Darwin-36B-Opus vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-omni-2b.md)\n- [DeepSeek-V3.2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.md)\n- [DeepSeek-V3.2 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-omni-2b.md)\n- [DeepSeek V4.1 Flash vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.md)\n- [DeepSeek V4.1 Flash vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-omni-2b.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.md)\n- [DeepSeek-V4-Flash-0731 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-omni-2b.md)\n- [Devstral Small 2 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-4-7-flash.md)\n- [Devstral Small 2 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-omni-2b.md)\n- [DiffusionGemma 26B A4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.md)\n- [DiffusionGemma 26B A4B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-omni-2b.md)\n- [EXAONE 4.5 33B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-7-flash.md)\n- [EXAONE 4.5 33B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-omni-2b.md)\n- [Gemma 4 12B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 12B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-omni-2b.md)\n- [Gemma 4 26B A4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 26B A4B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-omni-2b.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-omni-2b.md)\n- [Gemma 4 E2B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E2B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-omni-2b.md)\n- [Gemma 4 E4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E4B IT vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-omni-2b.md)\n- [GLM-4.5-Air vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.md)\n- [GLM-4.5-Air vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-omni-2b.md)\n- [GLM-4.7-Flash vs GLM-5.3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.md)\n- [GLM-4.7-Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.md)\n- [GLM-4.7-Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-120b.md)\n- [GLM-4.7-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-20b.md)\n- [GLM-4.7-Flash vs Granite 4.2 30B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-30b.md)\n- [GLM-4.7-Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.md)\n- [GLM-4.7-Flash vs Granite 4.2 8B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-8b.md)\n- [GLM-4.7-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-4.7-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.md)\n- [GLM-4.7-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling.md)\n- [GLM-4.7-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small.md)\n- [GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-4.7-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.md)\n- [GLM-4.7-Flash vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3-1-pro.md)\n- [GLM-4.7-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.md)\n- [GLM-4.7-Flash vs Kronos](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos.md)\n- [GLM-4.7-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1.md)\n- [GLM-4.7-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.md)\n- [GLM-4.7-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.md)\n- [GLM-4.7-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.md)\n- [GLM-4.7-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.md)\n- [GLM-4.7-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-1-7b.md)\n- [GLM-4.7-Flash vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-audio-2b.md)\n- [GLM-4.7-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b.md)\n- [GLM-4.7-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.md)\n- [GLM-4.7-Flash vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-1-flash-1019.md)\n- [GLM-4.7-Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-longcat-2-5.md)\n- [GLM-4.7-Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [GLM-4.7-Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.md)\n- [GLM-4.7-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-o-4-5.md)\n- [GLM-4.7-Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-1b.md)\n- [GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-35b-a3b.md)\n- [GLM-4.7-Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm5-2b.md)\n- [GLM-4.7-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5.md)\n- [GLM-4.7-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.md)\n- [GLM-4.7-Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m3-1-flash.md)\n- [GLM-4.7-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.md)\n- [GLM-4.7-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-muse-glimmer-30b.md)\n- [GLM-4.7-Flash vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-2-3b.md)\n- [GLM-4.7-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-4.7-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-4.7-Flash vs North Mini Code 1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-north-mini-code-1-0.md)\n- [GLM-4.7-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.md)\n- [GLM-4.7-Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-olmo-3-7b-think.md)\n- [GLM-4.7-Flash vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.md)\n- [GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it.md)\n- [GLM-4.7-Flash vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.md)\n- [GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [GLM-4.7-Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507.md)\n- [GLM-4.7-Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b.md)\n- [GLM-4.7-Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b.md)\n- [GLM-4.7-Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.md)\n- [GLM-4.7-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b.md)\n- [GLM-4.7-Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b.md)\n- [GLM-4.7-Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.md)\n- [GLM-4.7-Flash vs 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