{ "data": { "a": { "slug": "minicpm5-2b", "name": "MiniCPM5-2B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM5-2B is OpenBMB's open-weight dense language model of 2.5B parameters for on-device and local use, with thinking, XML tool calls and a 131,072-token context, published on Hugging Face in BF16, GGUF, MLX and GPTQ under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm5-2b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json", "repo": "https://huggingface.co/openbmb/MiniCPM5-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-GGUF" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-MLX" }, { "registry": "huggingface", "name": "openbmb/MiniCPM5-2B-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm5-2b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and its GGUF, MLX and GPTQ siblings are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB sells no per-call access to these weights. Running them costs the hardware, 5.03 GB for the BF16 file and 1.56 GB for OpenBMB's Q4_K_M GGUF (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/openbmb/MiniCPM5-2B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "on-device", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 60.2, "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": 48, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 27, "interface": 75, "reach": 90, "risk": 55, "upkeep": 97 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.", "bestFor": "On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data", "strengths": [ "OpenBMB's own GGUF, MLX and GPTQ builds, the 4-bit ones under 1.6 GB", "Stock LlamaForCausalLM, so vLLM, SGLang, llama.cpp, Ollama and Transformers load it without a fork", "Tool calls documented with the minicpm5 parser in released vLLM and SGLang, several calls per turn", "131,072 tokens native", "All eight training datasets published on Hugging Face", "OpenBMB answered 16 of 18 discussions and fixed the template on 29 September" ], "weaknesses": [ "OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning", "No safety evaluation, refusal scope or prompt-injection guidance", "No security.txt, disclosure policy or SECURITY.md", "The card adds use conditions to Apache 2.0, ethical use only and no jailbreak or injection testing", "No hosted endpoint, so an agent without hardware can't call it", "No knowledge cut-off and no structured-output guidance" ], "agentNotes": [ "Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m)", "On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text", "On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls", "Tool calls are XML, `\u003cfunction name=...\u003e\u003cparam name=...\u003e`, not Hermes JSON", "Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does", "Expect a `\u003cthink\u003e` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B", "Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented", "Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix" ], "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": 60.2 } ], "editorialScores": { "capability": 27, "interface": 75, "reach": 90, "risk": 55, "upkeep": 97 }, "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": "ollama run openbmb/minicpm5-2b\n# or, with vLLM 0.29.0 or later:\nvllm serve openbmb/MiniCPM5-2B --max-model-len 131072 \\\n --enable-auto-tool-choice --tool-call-parser minicpm5", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"openbmb/MiniCPM5-2B\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": true}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm5-2b" }, "sameCompany": [ "minicpm-o-4-5" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "openbmb/MiniCPM5-2B-Base", "params": "2.5B", "context": 131072, "goodFor": "Small on-device language model with thinking and XML tool calls, text only.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM5-2B", "url": "https://huggingface.co/openbmb/MiniCPM5-2B", "who": "publisher", "precision": "bf16", "sizeGB": 5, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "MiniCPM5-2B-F16", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.7, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/blob/main/MiniCPM5-2B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ] }, { "name": "MiniCPM5-2B-MLX", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.4, "memoryGB": 8, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "MiniCPM5-2B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 2.1, "memoryGB": 8, "format": "safetensors", "note": "vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "ollama openbmb/minicpm5-2b:latest", "url": "https://ollama.com/openbmb/minicpm5-2b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run openbmb/minicpm5-2b", "runBuild": "ollama openbmb/minicpm5-2b:latest", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM5", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM5-2B-SFT", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-SFT", "params": "2B" }, { "name": "MiniCPM5-2B-Midtrain", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-Midtrain", "params": "2B" }, { "name": "MiniCPM5-2B-Base", "url": "https://huggingface.co/openbmb/MiniCPM5-2B-Base", "params": "2B" }, { "name": "MiniCPM5-1B", "url": "https://huggingface.co/openbmb/MiniCPM5-1B", "params": "1B" }, { "name": "MiniCPM5-1B-SFT", "url": "https://huggingface.co/openbmb/MiniCPM5-1B-SFT", "params": "1B" }, { "name": "MiniCPM5-1B-Base", "url": "https://huggingface.co/openbmb/MiniCPM5-1B-Base", "params": "1B" } ], "olderCount": 22, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM5-2B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE in github.com/OpenBMB/MiniCPM reads Copyright [OpenBMB]. The card links its wiki to ModelBest's Feishu space, and no legal entity is named on the card. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, whose messages say what changed (a template fix on 29 September referencing OpenBMB/MiniCPM#379, sampling advice on 12 September), and the GitHub README's dated changelog lists the release on 7 September 2026.", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and the GitHub repository has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm5-2b.json" }, "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on reach 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": "OpenBMB", "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": "Apache-2.0", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-29", "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": "3/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, MiniCPM5-2B or Nemotron 3.5 Lightning 30B A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 85", "Documentation and upkeep, 97 against 70" ], "also": null, "goodFor": "On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data", "slug": "minicpm5-2b", "watchFor": "OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning" }, { "aheadOn": [ "Capability, 34 against 27", "Risk, 75 against 55", "Agent interface, 90 against 75" ], "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-minicpm5-2b.json", "title": "Agents-A1-4B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b" }, { "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-minicpm5-2b.json", "title": "Apertus v1.5 8B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm5-2b" }, { "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": 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"json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.json", "title": "gpt-oss-20b vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm5-2b.json", "title": "Granite 4.2 3B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Granite 4.2 3B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": 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{ "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-minicpm5-2b.json", "title": "Laguna S 2.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b" }, { "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-minicpm5-2b.json", "title": "Laguna XS 2.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b" }, { "json": 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}, { "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-minimax-m2-5.json", "title": "MiniCPM5-2B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7.json", "title": "MiniCPM5-2B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b.json", "title": "MiniCPM5-2B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-30b-a3b.json", "title": 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"https://www.anchorterminal.com/compare/minicpm5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-thinkingcap-qwen3-8-27b" }, { "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": 7, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "capability", "minicpm5-2b": 27, "name": "Capability", "nemotron-3-5-lightning-30b-a3b": 34, "weight": 35 }, { "by": 5, "edge": "minicpm5-2b", "key": "reach", "minicpm5-2b": 90, "name": "Reach", "nemotron-3-5-lightning-30b-a3b": 85, "weight": 25 }, { "by": 20, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "risk", "minicpm5-2b": 55, "name": "Risk", "nemotron-3-5-lightning-30b-a3b": 75, "weight": 20 }, { "by": 15, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "interface", "minicpm5-2b": 75, "name": "Agent interface", "nemotron-3-5-lightning-30b-a3b": 90, "weight": 10 }, { "by": 27, "edge": "minicpm5-2b", "key": "upkeep", "minicpm5-2b": 97, "name": "Documentation and upkeep", "nemotron-3-5-lightning-30b-a3b": 70, "weight": 10 } ], "summary": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "minicpm5-2b": "A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.", "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/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b", "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.min.md" }, "markdown": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.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### MiniCPM5-2B (C)\n\nGood for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data\n\nAhead on:\n- Reach, 90 against 85\n- Documentation and upkeep, 97 against 70\n\nWatch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning\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- Capability, 34 against 27\n- Risk, 75 against 55\n- Agent interface, 90 against 75\n\nWatch for: No safety evaluation results published\n\n\n## Score by category\n\n| Category | Weight | MiniCPM5-2B | Nemotron 3.5 Lightning 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 27 | 34 | Nemotron 3.5 Lightning 30B A3B +7 |\n| Reach | 25% (25 this run) | 90 | 85 | MiniCPM5-2B +5 |\n| Risk | 20% (20 this run) | 55 | 75 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Agent interface | 10% (10 this run) | 75 | 90 | Nemotron 3.5 Lightning 30B A3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 97 | 70 | MiniCPM5-2B +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.2 · C** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | MiniCPM5-2B | Nemotron 3.5 Lightning 30B A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenBMB | 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 | Apache-2.0 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-29 | 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 | 3/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.\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### MiniCPM5-2B\n\n1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m)\n2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text\n3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls\n4. Tool calls are XML, `\u003cfunction name=...\u003e\u003cparam name=...\u003e`, not Hermes JSON\n5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does\n6. Expect a `\u003cthink\u003e` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B\n7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented\n8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix\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, MiniCPM5-2B or Nemotron 3.5 Lightning 30B A3B?\n\nNemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm5-2b\", \"b\": \"nemotron-3-5-lightning-30b-a3b\"}`. 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