{ "data": { "a": { "slug": "minicpm-v-4-7-35b-a3b", "name": "MiniCPM-V 4.7 35B-A3B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 35B-A3B is OpenBMB's open-weight vision-language model, a 35B mixture of experts with about 3B active per token that reads images and video, calls tools, and ships in BF16, FP8, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json", "dns": "minicpm-v-4-7-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-FP8" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-35B-A3B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:35b-a3b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, FP8, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, about 70 GB for the BF16 files, 22.7 GB for the 4-bit safetensors builds or 18.7 GB for the IQ4_XS GGUF (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "video", "on-device", "tool-calling", "apache-2.0", "safetensors", "gguf", "fp8", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-11-03", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 61, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.", "bestFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence", "strengths": [ "Fits about 24 GB from OpenBMB's IQ4_XS GGUF and 32 GB from its Q4_K_M, 4-bit safetensors or Ollama builds", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions, with llama.cpp's own guide", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "OpenBMB answered 8 of 12 discussions and dates its releases", "Images, several images and video in one model, with thinking off by default" ], "weaknesses": [ "No limitations section, safety results, agent-use guidance or security contact", "Training data isn't described, and the card gives no context length, languages or knowledge cut-off", "The card shows a tool-calling request but not the reply, and names no parser", "OpenBMB's cookbook is behind the runtimes on SGLang and Ollama support", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`", "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost", "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace", "With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`", "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends", "Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 68 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-v4.7:35b-a3b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-35B-A3B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:35b-a3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-35b-a3b" }, "sameCompany": [ "minicpm-v-4-7-1b", "minicpm-o-4-5", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)", "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_moe_text with 40 layers and 256 experts", "params": "35.2B (3B active)", "context": 262144, "goodFor": "Vision-language mixture-of-experts model for images, several images and video, with tool calling and thinking off by default, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4_7-35B-A3B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "The smallest file at 4 bits. IQ3_M and IQ3_XS (15.4 and 14.5 GB) are below the 4-bit line and are not listed" }, { "name": "MiniCPM-V-4.7-35B-A3B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-35B-A3B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-35B-A3B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-int4", "who": "publisher", "precision": "int4", "sizeGB": 22.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "Listed on the card with the FP8 build under llm-compressor's FP8 and INT4 guide" }, { "name": "MiniCPM-V-4_7-35B-A3B-Q5_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 24.7, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-model-f16.gguf (1.1 GB) or mmproj-model-f16-4x.gguf (0.9 GB) for image input. The same file with thinking on is in the thinking/ folder" }, { "name": "ollama openbmb/minicpm-v4.7:35b-a3b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. Q4_K_M, 22 GB as the tags page shows it, with q8_0 (38 GB) and fp16 (70 GB) tags beside it" }, { "name": "MiniCPM-V-4.7-35B-A3B-FP8", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 38, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 28.5, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "MiniCPM-V-4.7-35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 70.4, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4_7-35B-A3B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B-gguf/blob/main/MiniCPM-V-4_7-35B-A3B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 69.4, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-35B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "params": "1.55B (1,554,476,272 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/OpenBMB/MiniCPM-V", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.", "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 GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.", "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.", "unchecked: the domain's registration date." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.json" }, "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on reach.", "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", "dns": "nemotron-3-5-lightning-30b-a3b.grade.anchorterminal.com", "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": "2026-11-02" }, "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": 64.7, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 15, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "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 14 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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.7 } ], "editorialScores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 75 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "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", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "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 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "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" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47, "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": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.json", "live": { "slug": "nemotron-3-5-lightning-30b-a3b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:26.657997393Z", "changedAt": "2026-11-10T16:23:26.657997393Z", "fingerprint": "b181720663ba" } ], "updatedAt": "2026-11-10T16:23:26.657997393Z" } }, "edges": [ { "by": 20, "category": "risk", "leads": "nemotron-3-5-lightning-30b-a3b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.", "b": "File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins." }, "scores": { "a": 55, "b": 75 } }, { "by": 20, "category": "interface", "leads": "nemotron-3-5-lightning-30b-a3b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.", "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens." }, "scores": { "a": 70, "b": 90 } }, { "by": 7, "category": "upkeep", "leads": "nemotron-3-5-lightning-30b-a3b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.", "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code as for the other Nemotron listings. The repositories last changed on 10 September, 53 days ago, and TensorRT-LLM, which the card's serving section runs, changed its Nemotron-H model code on 14 October, 19 days ago. Responsiveness earns 5, one NVIDIA reply across 14 questions. Version policy earns 5 as a first release. File health earns 5, since reports about the card's recipes wait without a reply and the llama.cpp recipe names a quantisation the linked repository doesn't hold." }, "scores": { "a": 68, "b": 75 } }, { "by": 5, "category": "reach", "leads": "minicpm-v-4-7-35b-a3b", "name": "Reach", "reasons": { "a": "Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.", "b": "Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it." }, "scores": { "a": 90, "b": 85 } } ], "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": "2026-11-03", "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": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on reach.", "question": "Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Nemotron 3.5 Lightning 30B A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 85" ], "also": null, "goodFor": "Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence", "slug": "minicpm-v-4-7-35b-a3b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" }, { "aheadOn": [ "Risk, 75 against 55", "Agent interface, 90 against 70", "Documentation and upkeep, 75 against 68" ], "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" } ], "history": { "minicpm-v-4-7-35b-a3b": null, "nemotron-3-5-lightning-30b-a3b": [ { "causes": [ "correction" ], "date": "2026-11-02", "delta": 0.5, "from": 64.2, "fromGrade": "B", "to": 64.7, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Agents-A1-4B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-35b-a3b" }, { "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-minicpm-v-4-7-35b-a3b.json", "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-35b-a3b" }, { "json": 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"json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Arete-27B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b.json", "title": "BigBang-v1 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-35b-a3b" }, { "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/carbon-x-vs-minicpm-v-4-7-35b-a3b.json", "title": "Carbon-X vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Carbon-X vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json", "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Cola2 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Cosmos-Reason2-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": 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"https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "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-minicpm-v-4-7-35b-a3b.json", "title": 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"https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b" }, { "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/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b.json", "title": "EXAONE 4.5 33B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "EXAONE 4.5 33B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-35b-a3b.json", "title": "Gemma 4 12B IT vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-35b-a3b" }, { "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-minicpm-v-4-7-35b-a3b.json", "title": "Gemma 4 26B A4B IT vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-v-4-7-35b-a3b.json", "title": "Gemma 4 31B IT vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 31B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-35b-a3b.json", "title": "Gemma 4 E2B IT vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": 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"title": "GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-35b-a3b.json", "title": "GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-35b-a3b.json", "title": "GLM-5.3-Flash vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "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-vs-minicpm-v-4-7-35b-a3b.json", "title": "GLM-5.3 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b.json", "title": "gpt-oss-120b vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "gpt-oss-120b vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b.json", "title": "gpt-oss-20b vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b" }, { "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-4-2-30b-vs-minicpm-v-4-7-35b-a3b.json", "title": "Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Granite 4.2 30B vs Nemotron 3.5 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Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling-Small vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling-Small vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling vs Nemotron 3.5 Lightning 30B A3B", "url": 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{ "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Ling-3.1-flash-1019 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.json", "title": "LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-v-4-7-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 35B-A3B", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-north-mini-code-1-0.json", "title": "Nemotron 3.5 Lightning 30B A3B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-north-mini-code-1-0" }, { "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-1-5-122b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-122b" }, { "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-otel-2-0-llm-31b-it.json", "title": "Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b" }, { "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-recursive-flint.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ripple-65b-preview.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-sensenova-loop-9b-preview" }, { "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-step-3-7-flash.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-step-3-7-flash" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-zaya1-8b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "minicpm-v-4-7-35b-a3b", "key": "reach", "minicpm-v-4-7-35b-a3b": 90, "name": "Reach", "nemotron-3-5-lightning-30b-a3b": 85, "weight": 25 }, { "by": 20, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "risk", "minicpm-v-4-7-35b-a3b": 55, "name": "Risk", "nemotron-3-5-lightning-30b-a3b": 75, "weight": 20 }, { "by": 20, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "interface", "minicpm-v-4-7-35b-a3b": 70, "name": "Agent interface", "nemotron-3-5-lightning-30b-a3b": 90, "weight": 10 }, { "by": 7, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "upkeep", "minicpm-v-4-7-35b-a3b": 68, "name": "Documentation and upkeep", "nemotron-3-5-lightning-30b-a3b": 75, "weight": 10 } ], "summary": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "minicpm-v-4-7-35b-a3b": "A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.", "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/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b", "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.min.md" }, "markdown": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on reach. Both do open-weight models you run yourself.\n\n- MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json\n- Nemotron 3.5 Lightning 30B A3B: grade B, 64.7/100, rank #20 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniCPM-V 4.7 35B-A3B (C)\n\nGood for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence\n\nAhead on:\n- Reach, 90 against 85\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\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- Risk, 75 against 55\n- Agent interface, 90 against 70\n- Documentation and upkeep, 75 against 68\n\nWatch for: No safety evaluation results published\n\n\n## Why the edge\n\n### Risk: Nemotron 3.5 Lightning 30B A3B +20\n\n- MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.\n- Nemotron 3.5 Lightning 30B A3B (75): File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.\n\n### Agent interface: Nemotron 3.5 Lightning 30B A3B +20\n\n- MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.\n- Nemotron 3.5 Lightning 30B A3B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.\n\n### Documentation and upkeep: Nemotron 3.5 Lightning 30B A3B +7\n\n- MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68.\n- Nemotron 3.5 Lightning 30B A3B (75): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts, cut-offs and filtering, and the corpora published. Last change earns 20, counting NVIDIA's own inference code as for the other Nemotron listings. The repositories last changed on 10 September, 53 days ago, and TensorRT-LLM, which the card's serving section runs, changed its Nemotron-H model code on 14 October, 19 days ago. Responsiveness earns 5, one NVIDIA reply across 14 questions. Version policy earns 5 as a first release. File health earns 5, since reports about the card's recipes wait without a reply and the llama.cpp recipe names a quantisation the linked repository doesn't hold.\n\n### Reach: MiniCPM-V 4.7 35B-A3B +5\n\n- MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- Nemotron 3.5 Lightning 30B A3B (85): Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it.\n\n## Score by category\n\n| Category | Weight | MiniCPM-V 4.7 35B-A3B | Nemotron 3.5 Lightning 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | MiniCPM-V 4.7 35B-A3B +5 |\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) | 68 | 75 | Nemotron 3.5 Lightning 30B A3B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **64.7 · B** | |\n\n## Facts side by side\n\n| Fact | MiniCPM-V 4.7 35B-A3B | 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 | 2026-11-03 | 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| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### MiniCPM-V 4.7 35B-A3B\n\nNo score change since it was listed.\n\n### Nemotron 3.5 Lightning 30B A3B\n\n- +0.5 on 2 November 2026: correction\n\n## Verdicts\n\n**MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.\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### MiniCPM-V 4.7 35B-A3B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate`\n5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format\n7. Validate JSON output yourself, since structured output isn't documented\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, MiniCPM-V 4.7 35B-A3B or Nemotron 3.5 Lightning 30B A3B?\n\nNemotron 3.5 Lightning 30B A3B scores 64.7 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-5-lightning-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minicpm-v-4-7-35b-a3b\", \"b\": \"nemotron-3-5-lightning-30b-a3b\"}`. 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ZAYA1-8B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3.5 Lightning 30B A3B", "url": "" } ], "description": "Nemotron 3.5 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