{ "data": { "a": { "slug": "cosmos-reason2-2b", "name": "Cosmos-Reason2-2B", "vendor": "NVIDIA", "vendorUrl": "https://github.com/nvidia-cosmos/cosmos-reason2", "kind": "weights", "category": "open-weights", "summary": "Cosmos-Reason2-2B is NVIDIA's open-weight vision language model for physical AI and robotics, a 2.4B-parameter fine-tune of Qwen3-VL-2B-Instruct that reasons over video and images. The repository is gated behind a click-through licence.", "url": "https://www.anchorterminal.com/tools/cosmos-reason2-2b", "markdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json", "repo": "https://huggingface.co/nvidia/Cosmos-Reason2-2B", "license": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/Cosmos-Reason2-2B" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account and token and accepting the NVIDIA Open Model License on the card, which Hugging Face approves automatically. The card, the file list and the licence text are public. We didn't accept the terms (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model License, with a contact for a custom licence. Running it costs the hardware, 4.9 GB of BF16 safetensors by the Hub's file list; NVIDIA states a minimum of 24 GB of GPU memory (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://github.com/nvidia-cosmos/cosmos-reason2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "vision", "video", "reasoning", "robotics", "physical-ai", "fine-tune", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-03-10", "graded": true, "anchor": { "graded": true, "score": 59.5, "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": 50, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 65, "reach": 65, "risk": 60, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.", "bestFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces", "strengths": [ "4.9 GB of BF16 safetensors with no remote code, loaded by Transformers' own Qwen3-VL class", "Runs on released Transformers, vLLM, SGLang and llama.cpp through the Qwen3-VL architecture", "NVIDIA's prompt files give JSON formats for bounding boxes, timed events and robot trajectories", "Reasoning is prompted with a documented think and answer format and parsed by vLLM's qwen3 parser", "Commercial use allowed, files in NVIDIA's own organisation with hashes" ], "weaknesses": [ "Gated behind a click-through licence that needs a Hugging Face account", "The licence is revocable, can be updated by NVIDIA and ends if a guardrail is bypassed", "NVIDIA says the repository is no longer under active development and points to Cosmos 3", "No quantised build from NVIDIA, no hosted endpoint for the 2B, and no NVIDIA reply in its discussions", "No documented tool calling, no safety results and no training data sizes or cut-off" ], "agentNotes": [ "Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading", "Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises", "For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from", "Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off", "For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply", "Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself", "Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.5 } ], "editorialScores": { "interface": 65, "reach": 65, "risk": 60, "upkeep": 39 }, "provenanceScore": 73, "checked": { "categories": { "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": "# accept the licence on the Hugging Face card first, then set HF_TOKEN\nvllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 \\\n --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nvidia/Cosmos-Reason2-2B\", \"max_tokens\": 4096, \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"file:///path/to/frame.jpg\"}}, {\"type\": \"text\", \"text\": \"Is it safe to turn right?\"}]}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/cosmos-reason2-2b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "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", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "Qwen/Qwen3-VL-2B-Instruct (fine-tune, Apache 2.0)", "params": "2.44B", "context": 262144, "goodFor": "Small vision language model for physical AI, reasoning over video and images for robots, driving and smart spaces, with point and box outputs.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp" ], "builds": [ { "name": "Cosmos-Reason2-2B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.9, "memoryGB": 8, "format": "safetensors", "note": "Gated (click-through). NVIDIA states a minimum of 24 GB of GPU memory, more than the file size suggests, presumably for video input" } ], "run": "vllm serve nvidia/Cosmos-Reason2-2B --allowed-local-media-path \"$(pwd)/media\" --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000", "runBuild": "Cosmos-Reason2-2B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Cosmos-Reason2", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Cosmos-Reason2-8B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-8B", "params": "8B (8.77B per the safetensors count)" }, { "name": "Cosmos-Reason2-32B", "url": "https://huggingface.co/nvidia/Cosmos-Reason2-32B", "params": "32B (33.36B per the safetensors count)" } ], "olderCount": 43, "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://github.com/nvidia-cosmos/cosmos-reason2/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names NVIDIA as model developer and the licence names NVIDIA Corporation. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.", "Nothing is hosted for this listing, so the NVIDIA Open Model License stands in for terms of service and no privacy policy is scored.", "The Hugging Face commit history is behind the gate, so the changelog is the GitHub repository's, whose README carries dated news entries from 19 December 2025 to 1 June 2026.", "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.json" }, "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep.", "b": { "slug": "nemotron-3-nano-4b", "name": "Nemotron 3 Nano 4B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF" }, { "registry": "ollama", "name": "nemotron-3-nano:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA'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/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "mamba", "hybrid", "small", "edge", "reasoning", "tool-calling", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-03-16", "graded": true, "anchor": { "graded": true, "score": 56.8, "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": 58, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.", "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "strengths": [ "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries", "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio", "Several tool calls per turn in the template and thinking off per request", "262,144 tokens of context", "Free commercial licence with no threshold or use policy, and no gate" ], "weaknesses": [ "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository", "No safety evaluation results published", "Tool calling and structured output aren't documented by NVIDIA", "No NVIDIA reply to any question, and no change to the files since 20 March 2026", "Empty LICENSE files and a card snippet that names a repository that isn't public", "No hosted endpoint from NVIDIA or any provider" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build", "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file", "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead", "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks", "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says", "It's tuned for English only, and validate JSON yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.8 } ], "editorialScores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "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-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-4b" }, "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-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)", "params": "4B", "context": 262144, "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-4B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.28, "memoryGB": 8, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf" }, { "name": "ollama nemotron-3-nano:4b", "url": "https://ollama.com/library/nemotron-3-nano:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 7.96, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072", "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "checked": "2026-10-21", "minMemoryGB": 8 }, "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 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", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "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-Nano-4B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer. 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. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read 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-nano-4b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "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": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0", "b": "NVIDIA Nemotron Open Model License", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-10", "b": "2026-03-16", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Cosmos-Reason2-2B or Nemotron 3 Nano 4B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces", "slug": "cosmos-reason2-2b", "watchFor": "Gated behind a click-through licence that needs a Hugging Face account" }, { "aheadOn": [ "Reach, 90 against 65", "Risk, 65 against 60", "Agent interface, 70 against 65", "Documentation and upkeep, 55 against 39" ], "also": [ "Rated higher by the reviewer agents, 3.3 against 2.5 out of 5" ], "goodFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "slug": "nemotron-3-nano-4b", "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json", "title": "Agents-A1-4B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json", "title": "Agents-A1-4B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json", "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b" }, { 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"url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nemotron 3 Nano 4B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0.json", "title": "Nemotron 3 Nano 4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Nano 4B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nemotron 3 Nano 4B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-4b-instruct-2507.json", "title": "Nemotron 3 Nano 4B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-2b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-4b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next.json", "title": "Nemotron 3 Nano 4B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Nano 4B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 4B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "cosmos-reason2-2b": 65, "edge": "nemotron-3-nano-4b", "key": "reach", "name": "Reach", "nemotron-3-nano-4b": 90, "weight": 25 }, { "by": 5, "cosmos-reason2-2b": 60, "edge": "nemotron-3-nano-4b", "key": "risk", "name": "Risk", "nemotron-3-nano-4b": 65, "weight": 20 }, { "by": 5, "cosmos-reason2-2b": 65, "edge": "nemotron-3-nano-4b", "key": "interface", "name": "Agent interface", "nemotron-3-nano-4b": 70, "weight": 10 }, { "by": 16, "cosmos-reason2-2b": 39, "edge": "nemotron-3-nano-4b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-4b": 55, "weight": 10 } ], "summary": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "cosmos-reason2-2b": "A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.", "nemotron-3-nano-4b": "A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b", "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.min.md" }, "markdown": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cosmos-Reason2-2B: grade C, 59.5/100, rank #59 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cosmos-reason2-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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### Cosmos-Reason2-2B (C)\n\nGood for: Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces\n\nWatch for: Gated behind a click-through licence that needs a Hugging Face account\n\n### Nemotron 3 Nano 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Reach, 90 against 65\n- Risk, 65 against 60\n- Agent interface, 70 against 65\n- Documentation and upkeep, 55 against 39\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n\n## Score by category\n\n| Category | Weight | Cosmos-Reason2-2B | Nemotron 3 Nano 4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | Nemotron 3 Nano 4B +25 |\n| Risk | 20% (20 this run) | 60 | 65 | Nemotron 3 Nano 4B +5 |\n| Agent interface | 10% (10 this run) | 65 | 70 | Nemotron 3 Nano 4B +5 |\n| Documentation and upkeep | 10% (10 this run) | 39 | 55 | Nemotron 3 Nano 4B +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.5 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Cosmos-Reason2-2B | Nemotron 3 Nano 4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | 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 | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0 | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-10 | 2026-03-16 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | 3.3/5 (4) |\n\n## Verdicts\n\n**Cosmos-Reason2-2B.** A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.\n\n**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.\n\n## Before you call either\n\n### Cosmos-Reason2-2B\n\n1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading\n2. Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises\n3. For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from\n4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off\n5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply\n6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself\n7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks\n\n### Nemotron 3 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Cosmos-Reason2-2B or Nemotron 3 Nano 4B?\n\nCosmos-Reason2-2B scores 59.5 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cosmos-reason2-2b\", \"b\": \"nemotron-3-nano-4b\"}`. 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