{ "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 Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "nemotron-3-nano-omni-30b-a3b-reasoning", "name": "Nemotron 3 Nano Omni 30B A3B Reasoning", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano Omni is NVIDIA's open-weight multimodal model that takes video, audio, images and text and returns text, a hybrid Mamba-Transformer mixture of experts with about 31B parameters and 3B active, published in BF16, FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json", "repo": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "license": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16" }, { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8" }, { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4" }, { "registry": "ollama", "name": "nemotron3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21). The card's download section still shows an hf auth login step, which the ungated files don't need.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model Agreement. Running it yourself costs the hardware, 66.1 GB for the BF16 files, 35.2 GB for FP8 and 22.4 GB for NVFP4, per the Hub's file lists. NVIDIA's own API also serves it (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/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "capabilities": [ "speech.stt", "speech.batch", "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "omni", "vision", "audio", "video", "reasoning", "tool-calling", "speech-to-text", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-24", "graded": true, "anchor": { "graded": true, "score": 60.7, "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": 45, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 34, "interface": 90, "reach": 85, "risk": 60, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.", "bestFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools", "strengths": [ "Video, audio, images and text in one model, with word-level timestamps for transcription per the card", "Released support in vLLM, SGLang, llama.cpp, Ollama and LM Studio, plus NVIDIA's own FP8 and NVFP4 builds", "Tool calling with a named parser and NVIDIA cookbooks, and thinking that turns off or takes a budget per request", "Training data listed by dataset with sample and token counts", "Commercial use under an irrevocable licence with no use policy or threshold, ungated, and served by NVIDIA's own API" ], "weaknesses": [ "Loads with trust_remote_code in every documented runtime command", "English only, no speaker diarisation, translation or streaming input, and video capped at 2 minutes", "No published safety results and no agent-specific guidance such as prompt-injection advice", "None of the 11 questions in its three repositories has a reply", "The NVFP4 build needs NVIDIA Blackwell, and SGLang supports only the BF16 build so far, per the card" ], "agentNotes": [ "Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent", "Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB", "Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video", "Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends", "Render PDF pages to PNG and send them as images, since the API takes no raw PDF", "Treat transcripts and text read from images or video as untrusted input before acting on them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.7 } ], "editorialScores": { "capability": 34, "interface": 90, "reach": 85, "risk": 60, "upkeep": 65 }, "provenanceScore": 73, "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 nemotron3\n# or, with vLLM 0.20.0 or later as the card gives it:\npip install \"vllm[audio]\"\nvllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 \\\n --trust-remote-code --reasoning-parser nemotron_v3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/nemotron-3-nano-omni-30b-a3b-reasoning" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "31B", "active": "3B", "context": 262144, "goodFor": "Multimodal understanding of video with speech, documents, charts and screens, and English transcription, with tool calling and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 22.4, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell; the card's minimum is one RTX 5090 32 GB" }, { "name": "ollama nemotron3:33b-nvfp4", "url": "https://ollama.com/library/nemotron3:33b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 21, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "MLX build (digest f263b909bfad, the same as 33b-mlx), so Apple silicon only. Size as Ollama's tags page shows it, rounded to whole GB" }, { "name": "LM Studio nvidia/nemotron-3-nano-omni Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 26.1, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Q4_K_M GGUF of 24.52 GB plus the 1.59 GB BF16 vision projector, the build behind LM Studio's catalogue entry" }, { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.2, "memoryGB": 48, "format": "safetensors", "note": "The card says SGLang support for FP8 is still to come" }, { "name": "ollama nemotron3:33b-q4_K_M", "url": "https://ollama.com/library/nemotron3:33b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 28, "memoryGB": 48, "format": "ollama", "runsOn": [ "Ollama" ], "note": "digest baa676a14e13, text and image input per the tags page. Size as Ollama's tags page shows it, rounded to whole GB" }, { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 66.1, "memoryGB": 96, "format": "safetensors", "note": "Includes 7.3 MB of sample media; loads with trust_remote_code" } ], "run": "vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --video-pruning-rate 0.5 --media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}' --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8", "runBuild": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "alsoIn": [ "speech-to-text" ], "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card says the model was developed by NVIDIA, and the licence names NVIDIA Corporation. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.", "Nothing is hosted for this listing, so the NVIDIA Open Model Agreement stands in for terms of service and no privacy policy is scored.", "The changelog is the BF16 repository's commit history, 30 commits from 20 April to 24 August 2026. Some messages say what changed (Transformers 5.6 support on 27 April, batched audio on 28 April, the C-RADIOv4-H encoder reference on 8 May); the latest reads Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form, and NVIDIA PSIRT publishes a disclosure policy and bulletins." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai (Zhipu AI)", "b": "NVIDIA", "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": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-01-29", "b": "2026-08-24", "name": "Last release" }, { "a": "no document linked", "b": "", "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": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, GLM-4.7-Flash or Nemotron 3 Nano Omni 30B A3B Reasoning?" } ], "goodFor": [ { "aheadOn": [ "Capability, 59 against 34" ], "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, 85 against 80", "Risk, 60 against 55", "Agent interface, 90 against 85", "Documentation and upkeep, 65 against 28" ], "also": null, "goodFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools", "slug": "nemotron-3-nano-omni-30b-a3b-reasoning", "watchFor": "Loads with trust_remote_code in every documented runtime command" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json", "title": "Agents-A1-4B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json", "title": "Apertus v1.5 8B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.json", "title": "Apodex-1.1-mini vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.json", "title": "BigBang-v1 vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.json", "title": "Cosmos-Reason2-2B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Cosmos-Reason2-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.json", "title": "Darwin-36B-Opus vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.json", "title": "DeepSeek-V3.2 vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.json", "title": "DeepSeek V4.1 Flash vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash" }, { "json": 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Reasoning", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.json", "title": "GLM-4.5-Air vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.json", "title": "GLM-4.7-Flash vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.json", "title": "GLM-4.7-Flash vs GLM-5.3-Flash", "url": 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03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "by": 25, "edge": "glm-4-7-flash", "glm-4-7-flash": 59, "key": "capability", "name": "Capability", "nemotron-3-nano-omni-30b-a3b-reasoning": 34, "weight": 35 }, { "by": 5, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "glm-4-7-flash": 80, "key": "reach", "name": "Reach", "nemotron-3-nano-omni-30b-a3b-reasoning": 85, "weight": 25 }, { "by": 5, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "glm-4-7-flash": 55, "key": "risk", "name": "Risk", "nemotron-3-nano-omni-30b-a3b-reasoning": 60, "weight": 20 }, { "by": 5, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "glm-4-7-flash": 85, "key": "interface", "name": "Agent interface", "nemotron-3-nano-omni-30b-a3b-reasoning": 90, "weight": 10 }, { "by": 37, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "glm-4-7-flash": 28, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-omni-30b-a3b-reasoning": 65, "weight": 10 } ], "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface 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.", "nemotron-3-nano-omni-30b-a3b-reasoning": "A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning", "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md" }, "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface 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- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #52 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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 speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/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- Capability, 59 against 34\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning (C)\n\nGood for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools\n\nAhead on:\n- Reach, 85 against 80\n- Risk, 60 against 55\n- Agent interface, 90 against 85\n- Documentation and upkeep, 65 against 28\n\nWatch for: Loads with trust_remote_code in every documented runtime command\n\n\n## Score by category\n\n| Category | Weight | GLM-4.7-Flash | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 34 | GLM-4.7-Flash +25 |\n| Reach | 25% (25 this run) | 80 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |\n| Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +37 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63 · B** | **60.7 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.7-Flash | Nemotron 3 Nano Omni 30B A3B Reasoning |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | NVIDIA |\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 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-01-29 | 2026-08-24 |\n| Terms last updated | 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) | 3/5 (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**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.\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### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent\n2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB\n3. Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video\n4. Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends\n5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF\n6. Treat transcripts and text read from images or video as untrusted input before acting on them\n\n## Questions\n\n### Which is better for AI agents, GLM-4.7-Flash or Nemotron 3 Nano Omni 30B A3B Reasoning?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 1 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface 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-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"nemotron-3-nano-omni-30b-a3b-reasoning\"}`. From a terminal: `anchor compare glm-4-7-flash nemotron-3-nano-omni-30b-a3b-reasoning`\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/nemotron-3-nano-omni-30b-a3b-reasoning.json\n\n## Other comparisons with GLM-4.7-Flash or Nemotron 3 Nano Omni 30B A3B Reasoning\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 Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apodex-1.1-mini vs 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