{ "data": { "a": { "slug": "nemotron-3-nano-30b-a3b", "name": "Nemotron 3 Nano 30B A3B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3-nano:30b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 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, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "reasoning", "tool-calling", "commercial-licence", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-24", "graded": true, "anchor": { "graded": true, "score": 67.1, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1201, "categoryRank": 10, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.", "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers", "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard", "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted", "1M tokens of input with no position-encoding scaling", "Full model card and a detailed data card with cut-offs, with much of the training data released" ], "weaknesses": [ "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository", "No safety or bias results published, though the subcards say both were run", "No NVIDIA reply on any of the 10 discussions opened since 1 March 2026, including a chat-template report", "Licence replaced eight days after release with no dated note, and the subcards still link the old one", "Structured output isn't documented, and the custom licence isn't OSI-approved" ], "agentNotes": [ "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)", "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151", "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text", "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning", "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks", "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens", "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3", "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution", "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67.1 } ], "editorialScores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 75 }, "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:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n --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:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-30b-a3b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "nemotron-3-5-parse", "cosmos-reason2-2b", "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": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B", "active": "3.5B", "context": 1048576, "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "ollama nemotron-3-nano:30b", "url": "https://ollama.com/library/nemotron-3-nano:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 24, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 32.7, "memoryGB": 48, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 63.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "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" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" } ], "olderCount": 47, "unrestrictedCopies": 5 }, "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-nemotron-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.", "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json", "live": { "slug": "nemotron-3-nano-30b-a3b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-23T15:23:07.231483397Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-23T01:49:46.545840859Z", "changedAt": "2026-10-23T01:49:46.545840859Z", "fingerprint": "130c55e2f52c" }, { "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/", "kind": "terms", "status": 304, "checkedAt": "2026-10-23T02:04:47.215582353Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "8461faf946d9" } ], "updatedAt": "2026-10-23T15:23:07.231483397Z" } }, "answer": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories.", "b": { "slug": "recursive-flint", "name": "Recursive Flint", "vendor": "Recursive", "vendorUrl": "https://www.recursive.com", "kind": "weights", "category": "open-weights", "summary": "Recursive Flint is Recursive's open-weight mixture-of-experts coding model for agents, 39.8B total and about 2.5B active parameters, with reasoning, tool calling and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/recursive-flint", "markdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/recursive-flint.json", "repo": "https://huggingface.co/RecursiveSI/flint", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "RecursiveSI/flint" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4-gguf" }, { "registry": "huggingface", "name": "RecursiveSI/flint-nvfp4-gguf" }, { "registry": "ollama", "name": "flint" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and all three quantised builds (checked 2026-10-22).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 79.6 GB for the BF16 files and 22.6 GB for Recursive's smallest 4-bit build (MLX INT4). The card mentions hosted API terms separate from the weight licence, but no Recursive API or inference provider serving Flint was confirmed (checked 2026-10-22).", "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 2026-10-22).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-22" }, "docsUrl": "https://huggingface.co/RecursiveSI/flint", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "mlx", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-21", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1201, "categoryRank": 41, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-22" }, "negative": 0, "verdict": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.", "bestFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Recursive's own MLX INT4, Q4_1 GGUF and NVFP4 GGUF builds, the smallest 22.6 GB", "Tool format, parser, parallel calls and JSON Schema output documented by Recursive", "262,144 tokens of context with no position scaling", "Safetensors and GGUF only, with a checksum on every file in Recursive's organisation", "llama.cpp, Ollama and LM Studio support on release day" ], "weaknesses": [ "No independent benchmark results yet, every published score is Recursive's", "No reasoning switch or budget, so hard prompts can spend tens of thousands of tokens thinking", "The BF16 route needs one 141 GB GPU and Recursive's SGLang plugin, and vLLM and released Transformers don't run it", "No safety results and no trained instruction hierarchy", "No inference provider or confirmed Recursive API for agents without the hardware" ], "agentNotes": [ "Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp", "With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on", "Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out", "Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask", "Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call", "Message content must be a string, flatten content-part arrays first", "Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-22", "reach": "2026-10-22", "risk": "2026-10-22", "upkeep": "2026-10-22" }, "last": "2026-10-22", "oldest": "2026-10-22", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run flint\n# or, with llama.cpp b11803 or later:\nllama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 262144 -np 1 -fa on --jinja \\\n --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"flint\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/recursive-flint" }, "weights": { "trainedBy": "Recursive", "params": "39.8B total, about 2.5B active", "context": 262144, "goodFor": "Agentic coding on a 32 to 48 GB machine, investigating codebases, fixing bugs and writing tests with multi-turn tool calls and reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "flint-int4", "url": "https://huggingface.co/RecursiveSI/flint-int4", "who": "publisher", "precision": "int4 (group-64 affine, INT8 embeddings)", "sizeGB": 22.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "rec", "Ollama" ], "note": "For Apple Silicon. Recursive's rec engine asks for an M4 Pro or newer with 48 GB" }, { "name": "flint-int4-gguf Q4_1", "url": "https://huggingface.co/RecursiveSI/flint-int4-gguf", "who": "publisher", "precision": "q4_1", "sizeGB": 26, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-int4-q4_1.gguf, 25,992,966,976 bytes, the INT4 export's trained codes stored as Q4_1. The card targets Metal and asks for a Mac with at least 48 GB at full context" }, { "name": "flint-nvfp4-gguf", "url": "https://huggingface.co/RecursiveSI/flint-nvfp4-gguf", "who": "publisher", "precision": "nvfp4", "sizeGB": 22.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-nvfp4.gguf, 22,863,084,160 bytes, for NVIDIA GPUs with CUDA. The card recommends at least 48 GB at full context" }, { "name": "ollama flint:latest", "url": "https://ollama.com/library/flint:latest", "who": "ollama", "precision": "4-bit", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest 905c74dfd56b, shown as 23 GB (Ollama rounds to whole GB). The library also has qat-int4 (23 GB), q4_1 (26 GB), gguf-nvfp4 (23 GB) and mlx-bf16 (80 GB) tags. Ollama 0.40.8 runs Flint in its MLX runner; its bundled llama.cpp (b11738) predates Flint, so the GGUF tags on Linux weren't confirmed in a release" }, { "name": "lmstudio-community flint-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/flint-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 24.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "flint-Q4_K_M.gguf, 24,166,841,184 bytes, the build behind LM Studio's recursivesi/flint catalogue page. Also Q6_K (32.7 GB, 48 GB of memory) and Q8_0 (42.4 GB, 64 GB)" }, { "name": "flint", "url": "https://huggingface.co/RecursiveSI/flint", "who": "publisher", "precision": "bf16", "sizeGB": 79.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "SGLang 0.5.16 with Recursive's flint_sglang kit" ], "note": "Recursive's SGLang kit needs one GPU with at least 141 GB (B300, B200 or H200). Transformers support isn't in a release yet" } ], "run": "llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "runBuild": "flint-int4-gguf Q4_1", "checked": "2026-10-22", "minMemoryGB": 32 }, "family": { "lab": "Recursive", "family": "Flint", "url": "https://www.anchorterminal.com/open-weights/#recursive" }, "area": "models", "provenance": { "legalEntity": "Recursive Superintelligence, Inc. (the Hugging Face organisation's full name)", "domain": "recursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "unknown", "checked": "2026-10-22", "notes": [ "The Hugging Face organisation RecursiveSI is named Recursive Superintelligence, Inc. and isn't verified by Hugging Face. Recursive's rec repository, which carries Recursive's logo and links recursive.com, downloads the INT4 build from RecursiveSI/flint-int4 at a pinned commit.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The card links www.recursive.com, docs.recursive.com and github.com/recursive-oss/rec. Neither recursive.com host could be read on 22 October 2026, so security.txt and the domain's registration weren't checked.", "The card names a security reporting address at recursive.com, and rec's SECURITY.md asks for private reports through GitHub's vulnerability reporting." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/recursive-flint.json", "live": { "slug": "recursive-flint", "securityTxt": { "url": "https://recursive.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-23T15:23:07.053004679Z" }, "updatedAt": "2026-10-23T15:23:07.053004679Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "Recursive", "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 (custom, Apache-style, commercial use allowed, not an OSI licence)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-24", "b": "2026-10-21", "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": "3.3/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories.", "question": "Which is better for AI agents, Nemotron 3 Nano 30B A3B or Recursive Flint?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 75", "Documentation and upkeep, 75 against 67" ], "also": null, "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "slug": "nemotron-3-nano-30b-a3b", "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository" }, { "aheadOn": null, "also": null, "goodFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "slug": "recursive-flint", "watchFor": "No independent benchmark results yet, every published score is Recursive's" } ], "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-30b-a3b.json", "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.json", "title": "Agents-A1-4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.json", "title": "Apertus v1.5 8B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.json", "title": "GLM-5.3-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-30b-a3b.json", "title": "GLM-5.3 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.json", "title": "GLM-5.3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.json", "title": "gpt-oss-120b vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.json", "title": "gpt-oss-120b vs 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"title": "Nemotron 3 Nano 30B A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": 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"Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint.json", "title": "Nemotron 3 Nano 4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json", "title": "Nemotron 3 Super 120B A12B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-recursive-flint.json", "title": "Nemotron 3 Ultra 550B A55B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json", "title": "occamy-1.0 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint.json", "title": "Olmo 3 7B Think vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint.json", "title": "Ornith-1.5-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint.json", "title": "Ornith-1.5-397B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint.json", "title": "Ornith-1.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint.json", "title": "Qwen3-4B-Instruct-2507 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint.json", "title": "Qwen3.5-0.8B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint.json", "title": "Qwen3.5-122B-A10B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint.json", "title": "Qwen3.5-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint.json", "title": "Qwen3.5-397B-A17B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint.json", "title": "Qwen3.5-4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json", "title": "Qwen3.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.json", "title": "Qwen3.6-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint.json", "title": "Qwen3.6-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint.json", "title": "Qwen3.8-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.json", "title": "Qwen3.8-Flash-Next vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint.json", "title": "Qwen3-Coder-Next vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-2B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-32B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-4B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-8B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.json", "title": "Qwopus3.8-27B-Flash-V2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json", "title": "Recursive Flint vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json", "title": "Recursive Flint vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json", "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "nemotron-3-nano-30b-a3b", "key": "reach", "name": "Reach", "nemotron-3-nano-30b-a3b": 90, "recursive-flint": 75, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "nemotron-3-nano-30b-a3b": 60, "recursive-flint": 60, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "nemotron-3-nano-30b-a3b": 90, "recursive-flint": 90, "weight": 10 }, { "by": 8, "edge": "nemotron-3-nano-30b-a3b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-30b-a3b": 75, "recursive-flint": 67, "weight": 10 } ], "summary": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.", "recursive-flint": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint", "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.min.md" }, "markdown": "Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json\n- Recursive Flint: grade C, 61/100, rank #49 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.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 3023 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Reach, 90 against 75\n- Documentation and upkeep, 75 against 67\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n### Recursive Flint (C)\n\nGood for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano 30B A3B | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 75 | Nemotron 3 Nano 30B A3B +15 |\n| Risk | 20% (20 this run) | 60 | 60 | even |\n| Agent interface | 10% (10 this run) | 90 | 90 | even |\n| Documentation and upkeep | 10% (10 this run) | 75 | 67 | Nemotron 3 Nano 30B A3B +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **67.1 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 30B A3B | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Recursive |\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 (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-24 | 2026-10-21 |\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 | 3.3/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.\n\n## Before you call either\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n### Recursive Flint\n\n1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp\n2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on\n3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out\n4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask\n5. Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call\n6. Message content must be a string, flatten content-part arrays first\n7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano 30B A3B or Recursive Flint?\n\nNemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Recursive Flint's 61 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-30b-a3b\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare nemotron-3-nano-30b-a3b recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with Nemotron 3 Nano 30B A3B or Recursive Flint\n\n- [Agents-A1-4B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.md)\n- [Agents-A1-4B vs Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.md)\n- [Apertus v1.5 8B vs Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.md)\n- [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md)\n- [BigBang-v1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.md)\n- [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md)\n- [Carbon-X vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b.md)\n- [Carbon-X vs Recursive Flint](https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [Cosmos-Reason2-2B vs Recursive Flint](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.md)\n- [Darwin-36B-Opus vs Recursive Flint](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.md)\n- [DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek-V3.2 vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.md)\n- [DiffusionGemma 26B A4B IT vs Nemotron 3 Nano 30B 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Flint](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3-Flash vs Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.md)\n- [GLM-5.3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3 vs Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.md)\n- [gpt-oss-120b vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-30b-a3b.md)\n- [gpt-oss-120b vs Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.md)\n- [gpt-oss-20b vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-30b-a3b.md)\n- [gpt-oss-20b vs Recursive 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Flint](https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.md)\n- [Laguna S 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna S 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna XS 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.md)\n- [Ling-3.0-tiny vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-nano-30b-a3b.md)\n- [Ling-3.0-tiny vs Recursive 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