{ "data": { "a": { "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" }, "answer": "Ornith-1.5-397B scores 59 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep.", "b": { "slug": "ornith-1-5-397b", "name": "Ornith-1.5-397B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-GGUF" }, { "registry": "ollama", "name": "ornith-1.5:397b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith'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/ornith-ai/Ornith-1.5-397B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 59, "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": 53, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.", "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no code in the repository" ], "weaknesses": [ "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine", "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference", "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters" ], "agentNotes": [ "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards", "Pin a revision when you download, since Ornith has replaced files in place", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "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 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "provenanceScore": 13, "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": "ollama run ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-397b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)", "params": "397B", "context": 262144, "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-397B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 418.4, "memoryGB": 512, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 319.5, "memoryGB": 512, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB" }, { "name": "Ornith-1.5-397B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 331, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 286.3, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 244.3, "memoryGB": 512, "format": "gguf", "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine" }, { "name": "ollama ornith-1.5:397b", "url": "https://ollama.com/library/ornith-1.5:397b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 242, "memoryGB": 512, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 428.5, "memoryGB": 1024, "format": "gguf", "note": "Too big for the 128 GB reference machine" } ], "run": "ollama run ornith-1.5:397b", "runBuild": "ollama ornith-1.5:397b", "checked": "2026-10-21", "minMemoryGB": 512 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 4 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "Ornith", "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 Nemotron Open Model License", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-16", "b": "2026-08-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.3/5 (4)", "b": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ornith-1.5-397B scores 59 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep.", "question": "Which is better for AI agents, Nemotron 3 Nano 4B or Ornith-1.5-397B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 65", "Risk, 65 against 50", "Documentation and upkeep, 55 against 36" ], "also": [ "Rated higher by the reviewer agents, 3.3 against 2.0 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" }, { "aheadOn": [ "Agent interface, 85 against 70" ], "also": null, "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-397b", "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "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/agents-a1-4b-vs-ornith-1-5-397b.json", "title": "Agents-A1-4B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b" }, { 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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" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json", "title": "occamy-1.0 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json", "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-397B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-397B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-397B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-397B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-397B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-397B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-397B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json", "title": "Ornith-1.5-397B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-397B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "nemotron-3-nano-4b", "key": "reach", "name": "Reach", "nemotron-3-nano-4b": 90, "ornith-1-5-397b": 65, "weight": 25 }, { "by": 15, "edge": "nemotron-3-nano-4b", "key": "risk", "name": "Risk", "nemotron-3-nano-4b": 65, "ornith-1-5-397b": 50, "weight": 20 }, { "by": 15, "edge": "ornith-1-5-397b", "key": "interface", "name": "Agent interface", "nemotron-3-nano-4b": 70, "ornith-1-5-397b": 85, "weight": 10 }, { "by": 19, "edge": "nemotron-3-nano-4b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-4b": 55, "ornith-1-5-397b": 36, "weight": 10 } ], "summary": "Ornith-1.5-397B scores 59 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-1-5-397b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.min.md" }, "markdown": "Ornith-1.5-397B scores 59 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\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- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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### 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 50\n- Documentation and upkeep, 55 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.0 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### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano 4B | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 65 | Nemotron 3 Nano 4B +25 |\n| Risk | 20% (20 this run) | 65 | 50 | Nemotron 3 Nano 4B +15 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-397B +15 |\n| Documentation and upkeep | 10% (10 this run) | 55 | 36 | Nemotron 3 Nano 4B +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 4B | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Ornith |\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 Nemotron Open Model License | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-16 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.3/5 (4) | 2/5 (4) |\n\n## Verdicts\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**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\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### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano 4B or Ornith-1.5-397B?\n\nOrnith-1.5-397B scores 59 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-4b\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare nemotron-3-nano-4b ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with Nemotron 3 Nano 4B or Ornith-1.5-397B\n\n- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.md)\n- [Apodex-1.1-mini vs 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4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [GLM-4.5-Air vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b.md)\n- [GLM-4.5-Air vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md)\n- [GLM-5.3 vs Nemotron 3 Nano 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4B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Nano 30B A3B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-nemotron-3-nano-4b.md)\n- [Nemotron 3 Nano 30B A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-397b.md)\n- [Nemotron 3 Nano 4B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Nemotron 3 Nano 4B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-super-120b-a12b.md)\n- [Nemotron 3 Nano 4B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Nemotron 3 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[Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.md)\n- [Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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": "Nemotron 3 Nano 4B vs Ornith-1.5-397B", "url": "" } ], "description": "Ornith-1.5-397B scores 59 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. 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