{ "data": { "a": { "slug": "glm-5-3-flash", "name": "GLM-5.3-Flash", "vendor": "Z.ai", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json", "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash-BF16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (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-5.3-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-26", "graded": true, "anchor": { "graded": true, "score": 65.1, "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": 13, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "1,048,576 tokens of native context and several tool calls per turn in the chat template", "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it to low", "The tool-call format lives only in the chat template, with the parser named by the runtimes", "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block", "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt", "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai" ], "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": 65.1 } ], "editorialScores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "provenanceScore": 53, "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": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3-flash" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3" ], "weights": { "trainedBy": "Z.ai", "params": "320B", "active": "18B", "context": 1048576, "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3-Flash-FP4-Local", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 177.5, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash-NVFP4", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 204.4, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "who": "publisher", "precision": "fp8", "sizeGB": 328.3, "memoryGB": 512, "format": "safetensors" }, { "name": "GLM-5.3-Flash-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 642.7, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "runBuild": "GLM-5.3-Flash", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-5", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-5", "url": "https://huggingface.co/zai-org/GLM-5", "params": "744B-A40B" }, { "name": "GLM-5.1", "url": "https://huggingface.co/zai-org/GLM-5.1", "params": "744B-A40B" }, { "name": "GLM-5.2", "url": "https://huggingface.co/zai-org/GLM-5.2", "params": "744B-A40B" }, { "name": "GLM-5.3", "url": "https://huggingface.co/zai-org/GLM-5.3", "params": "744B-A40B", "listing": "https://www.anchorterminal.com/tools/glm-5-3" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)", "domain": "z.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json" }, "answer": "GLM-5.3-Flash and Nemotron 3.5 Lightning 30B A3B score within a point of each other on agent readiness, 65.1 (B) and 64.2 (B). Nemotron 3.5 Lightning 30B A3B leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "nemotron-3-5-lightning-30b-a3b", "name": "Nemotron 3.5 Lightning 30B A3B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3.5 Lightning is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 30B parameters with 3B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3.5-lightning" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 65.9 GB for the BF16 repository (one H100 80GB at 256K tokens per the card), 21.6 GB for NVIDIA's NVFP4 build or 19 GB for Ollama's smallest 4-bit build (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/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "reasoning", "tool-calling", "long-context", "safetensors", "nvfp4", "self-hosted", "new" ], "lastRelease": "2026-08-11", "graded": true, "anchor": { "graded": true, "score": 64.2, "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": 17, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.", "bestFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence", "strengths": [ "Released support in vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio", "4-bit builds of 19 GB from ggml-org and Ollama, and NVIDIA's own NVFP4 at 21.6 GB", "Safetensors only, with no repository code run to load it or parse its output", "Tool calls documented with a named parser, several per turn, and thinking off per request", "Training data listed with token counts and cut-offs, much of it published", "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's own API serves it" ], "weaknesses": [ "No safety evaluation results published", "One NVIDIA reply across 12 questions on its two repositories", "Only one quantised build from NVIDIA, and NVFP4 needs NVIDIA GPUs", "Structured output claimed but not documented, and no reasoning budget", "The card's llama.cpp recipe names a Q4_K_M file that ggml-org's repository doesn't hold" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform", "On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs", "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)", "Use `temperature=1.0` and `top_p=0.95`, as the card says", "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks", "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content", "Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.2 } ], "editorialScores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 70 }, "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 nemotron-3.5-lightning\n# or, with llama.cpp and the GGUF the card links:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072\n# or, with vLLM on NVIDIA GPUs (the card's command, abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --reasoning-parser nemotron_v3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-5-lightning-30b-a3b" }, "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-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b" ], "weights": { "trainedBy": "NVIDIA", "params": "30B", "active": "3B", "context": 1048576, "goodFor": "Small hybrid Mamba and mixture-of-experts reasoning model for always-on agents, text only, with tool calling, a thinking switch and up to 1M tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF", "who": "llama.cpp", "precision": "q4_0", "sizeGB": 18.9, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation, which NVIDIA's card calls the official GGUF weights. Also Q8_0 (33.59 GB) and BF16 (63.18 GB), and separate MTP files of 1.16 to 4.09 GB" }, { "name": "ollama nemotron-3.5-lightning:30b-a3b-nvfp4", "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama (MLX)" ], "note": "MLX build for Apple silicon, the smallest in Ollama's library. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.58, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 experts with FP8 Mamba projections. Native on Blackwell, W4A16 kernels on Hopper and Ampere, per its card. Needs NVIDIA GPUs" }, { "name": "ollama nemotron-3.5-lightning:30b-a3b-q4_K_M", "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 25, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The llama.cpp build Ollama uses on Linux. Size as Ollama's tags page shows it" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 24.52, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (33.51 GB) and Q8_0 (33.59 GB)" }, { "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 65.85, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072", "runBuild": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)", "checked": "2026-10-21", "minMemoryGB": 24 }, "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 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "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 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 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer and the LICENSE file reads Copyright 2026 NVIDIA CORPORATION \u0026 AFFILIATES. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Since release on 11 August 2026 only the cards have changed, most commits reading Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.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": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-26", "b": "2026-08-11", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3-Flash and Nemotron 3.5 Lightning 30B A3B score within a point of each other on agent readiness, 65.1 (B) and 64.2 (B). Nemotron 3.5 Lightning 30B A3B leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, GLM-5.3-Flash or Nemotron 3.5 Lightning 30B A3B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 75 against 34" ], "also": null, "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "slug": "glm-5-3-flash", "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists" }, { "aheadOn": [ "Reach, 85 against 60", "Risk, 75 against 55", "Agent interface, 90 against 70", "Documentation and upkeep, 70 against 58" ], "also": null, "goodFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence", "slug": "nemotron-3-5-lightning-30b-a3b", "watchFor": "No safety evaluation results published" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json", "title": "Agents-A1-4B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json", "title": "Apertus v1.5 8B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.json", "title": "Apodex-1.1-mini vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json", "title": "BigBang-v1 vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "BigBang-v1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.json", "title": "Cosmos-Reason2-2B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.json", "title": "Darwin-36B-Opus vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B", "url": 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"https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.json", "title": "DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.json", "title": "DiffusionGemma 26B A4B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 12B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 26B A4B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash" }, { "json": 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"https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Ling-3.0-tiny vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM5-2B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M2.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-30b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0.json", "title": "Nemotron 3.5 Lightning 30B A3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-4b-instruct-2507.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-2b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 41, "edge": "glm-5-3-flash", "glm-5-3-flash": 75, "key": "capability", "name": "Capability", "nemotron-3-5-lightning-30b-a3b": 34, "weight": 35 }, { "by": 25, "edge": "nemotron-3-5-lightning-30b-a3b", "glm-5-3-flash": 60, "key": "reach", "name": "Reach", "nemotron-3-5-lightning-30b-a3b": 85, "weight": 25 }, { "by": 20, "edge": "nemotron-3-5-lightning-30b-a3b", "glm-5-3-flash": 55, "key": "risk", "name": "Risk", "nemotron-3-5-lightning-30b-a3b": 75, "weight": 20 }, { "by": 20, "edge": "nemotron-3-5-lightning-30b-a3b", "glm-5-3-flash": 70, "key": "interface", "name": "Agent interface", "nemotron-3-5-lightning-30b-a3b": 90, "weight": 10 }, { "by": 12, "edge": "nemotron-3-5-lightning-30b-a3b", "glm-5-3-flash": 58, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-lightning-30b-a3b": 70, "weight": 10 } ], "summary": "GLM-5.3-Flash and Nemotron 3.5 Lightning 30B A3B score within a point of each other on agent readiness, 65.1 (B) and 64.2 (B). Nemotron 3.5 Lightning 30B A3B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "nemotron-3-5-lightning-30b-a3b": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b", "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.min.md" }, "markdown": "GLM-5.3-Flash and Nemotron 3.5 Lightning 30B A3B score within a point of each other on agent readiness, 65.1 (B) and 64.2 (B). Nemotron 3.5 Lightning 30B A3B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json\n- Nemotron 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.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\n## Which one, for what\n\n### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nAhead on:\n- Capability, 75 against 34\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n### Nemotron 3.5 Lightning 30B A3B (B)\n\nGood for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence\n\nAhead on:\n- Reach, 85 against 60\n- Risk, 75 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 70 against 58\n\nWatch for: No safety evaluation results published\n\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Nemotron 3.5 Lightning 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 75 | 34 | GLM-5.3-Flash +41 |\n| Reach | 25% (25 this run) | 60 | 85 | Nemotron 3.5 Lightning 30B A3B +25 |\n| Risk | 20% (20 this run) | 55 | 75 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 70 | Nemotron 3.5 Lightning 30B A3B +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Nemotron 3.5 Lightning 30B A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-26 | 2026-08-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\n\n**Nemotron 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.\n\n## Before you call either\n\n### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n### Nemotron 3.5 Lightning 30B A3B\n\n1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform\n2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95`, as the card says\n5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content\n7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Nemotron 3.5 Lightning 30B A3B?\n\nGLM-5.3-Flash and Nemotron 3.5 Lightning 30B A3B score within a point of each other on agent readiness, 65.1 (B) and 64.2 (B). Nemotron 3.5 Lightning 30B A3B 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-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"nemotron-3-5-lightning-30b-a3b\"}`. From a terminal: `anchor compare glm-5-3-flash nemotron-3-5-lightning-30b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json\n\n## Other comparisons with GLM-5.3-Flash or Nemotron 3.5 Lightning 30B A3B\n\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs Nemotron 3.5 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