{
  "data": {
    "a": {
      "slug": "mimo-v2-6-flash-rl",
      "name": "MiMo-V2.6-Flash-RL",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json",
      "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "license": "MIT (metadata only, no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "audio",
        "video",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "mit",
        "safetensors",
        "fp4",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-10-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 59,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
        "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "strengths": [
          "MIT, ungated, with hosted options",
          "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang",
          "A 1M context and four input types",
          "Trained with reinforcement learning across agent harnesses",
          "A GGUF from the llama.cpp project's own organisation"
        ],
        "weaknesses": [
          "178 GB at 4 bits, so it needs about 200 GB of memory",
          "The card's serve commands pass trust-remote-code",
          "A pickle file sits in the draft model's folder",
          "No safety results, limitations or data sources on the card",
          "Xiaomi replied in a few of the discussions only"
        ],
        "agentNotes": [
          "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita",
          "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`",
          "Use temperature 1.0 and top_p 0.95, as the card recommends",
          "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in",
          "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle",
          "The Use Restrictions section asks for human oversight of high-risk actions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags",
        "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-flash-rl"
      },
      "sameCompany": [
        "mimo-v2-6-distill-qwen-9b"
      ],
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "params": "309B",
        "active": "15B",
        "context": 1048576,
        "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiMo-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "who": "publisher",
            "precision": "fp4",
            "sizeGB": 177.74,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build Xiaomi publishes, with most weights at 4 bits"
          },
          {
            "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF",
            "who": "llama.cpp",
            "precision": "mxfp4",
            "sizeGB": 168.93,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo",
        "runBuild": "MiMo-V2.6-Flash-RL",
        "checked": "2026-11-10",
        "minMemoryGB": 256
      },
      "family": {
        "lab": "Xiaomi (MiMo)",
        "family": "MiMo-V2 (V2, V2.5, V2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#xiaomi",
        "others": [
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "params": "9B (Hub safetensors count 9.41B)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b"
          },
          {
            "name": "MiMo-V2.6-Pro-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Pro-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-ASR",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR",
            "params": "7.62B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base",
            "params": "309.77B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash",
            "params": "309.77B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 12
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)",
        "domain": "mimo.xiaomi.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.",
          "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 13 commits from 21 September to 28 October 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json"
    },
    "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.",
    "b": {
      "slug": "nemotron-3-nano-4b",
      "name": "Nemotron 3 Nano 4B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json",
      "dns": "nemotron-3-nano-4b.grade.anchorterminal.com",
      "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": 1518,
        "categoryRank": 82,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 25,
          "interface": 70,
          "reach": 90,
          "risk": 65,
          "upkeep": 55
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.",
        "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": "0.7",
            "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": 76,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "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",
        "nemotron-3-5-transcribe-30b-a4b",
        "parakeet-tdt-0-6b-v3",
        "parakeet-unified-en-0-6b",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-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 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          },
          {
            "name": "Nemotron 3.5 Parse",
            "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse",
            "params": "0.631B (630,800,496 trainable parameters per the card)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse"
          },
          {
            "name": "Nemotron 3.5 Transcribe 30B-A4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B",
            "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b"
          }
        ],
        "olderCount": 47,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-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": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.json",
      "live": {
        "slug": "nemotron-3-nano-4b",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:18.735571066Z",
            "changedAt": "2026-11-10T16:23:18.735571066Z",
            "fingerprint": "3b3d07e6bba4"
          }
        ],
        "updatedAt": "2026-11-10T16:23:18.735571066Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "reach",
        "leads": "nemotron-3-nano-4b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.",
          "b": "Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found."
        },
        "scores": {
          "a": 65,
          "b": 90
        }
      },
      {
        "by": 20,
        "category": "risk",
        "leads": "nemotron-3-nano-4b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.",
          "b": "File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins."
        },
        "scores": {
          "a": 45,
          "b": 65
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Xiaomi (MiMo team)",
        "b": "NVIDIA",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (metadata only, no LICENSE file)",
        "b": "NVIDIA Nemotron Open Model License",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-28",
        "b": "2026-03-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.",
        "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or Nemotron 3 Nano 4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "slug": "mimo-v2-6-flash-rl",
        "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory"
      },
      {
        "aheadOn": [
          "Reach, 90 against 65",
          "Risk, 65 against 45"
        ],
        "also": null,
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.json",
        "title": "Agents-A1-4B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.json",
        "title": "Apertus v1.5 8B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl"
      },
      {
        "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/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.json",
        "title": "Apodex-1.1-mini vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.json",
        "title": "Arete-27B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl"
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      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl"
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      {
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-4b"
      },
      {
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      {
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        "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-4b-vs-unlimited-ocr-2.json",
        "title": "Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b.json",
        "title": "Nemotron 3 Nano 4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "nemotron-3-nano-4b",
        "key": "reach",
        "mimo-v2-6-flash-rl": 65,
        "name": "Reach",
        "nemotron-3-nano-4b": 90,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "nemotron-3-nano-4b",
        "key": "risk",
        "mimo-v2-6-flash-rl": 45,
        "name": "Risk",
        "nemotron-3-nano-4b": 65,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "mimo-v2-6-flash-rl",
        "key": "interface",
        "mimo-v2-6-flash-rl": 85,
        "name": "Agent interface",
        "nemotron-3-nano-4b": 70,
        "weight": 10
      },
      {
        "by": 4,
        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "mimo-v2-6-flash-rl": 59,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-4b": 55,
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
      "nemotron-3-nano-4b": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b",
    "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.min.md"
  },
  "markdown": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself.\n\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #110 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\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 45\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository\n\n\n## Why the edge\n\n### Reach: Nemotron 3 Nano 4B +25\n\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n- Nemotron 3 Nano 4B (90): Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.\n\n### Risk: Nemotron 3 Nano 4B +20\n\n- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.\n- Nemotron 3 Nano 4B (65): File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins.\n\n### Agent interface: MiMo-V2.6-Flash-RL +15\n\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Nemotron 3 Nano 4B (70): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 tokens.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | Nemotron 3 Nano 4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | Nemotron 3 Nano 4B +25 |\n| Risk | 20% (20 this run) | 45 | 65 | Nemotron 3 Nano 4B +20 |\n| Agent interface | 10% (10 this run) | 85 | 70 | MiMo-V2.6-Flash-RL +15 |\n| Documentation and upkeep | 10% (10 this run) | 59 | 55 | MiMo-V2.6-Flash-RL +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | Nemotron 3 Nano 4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (metadata only, no LICENSE file) | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-03-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.3/5 (4) |\n\n## Verdicts\n\n**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\n\n**Nemotron 3 Nano 4B.** A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.\n\n## Before you call either\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\n\n### Nemotron 3 Nano 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or Nemotron 3 Nano 4B?\n\nMiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 4 scored categories. Nemotron 3 Nano 4B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"nemotron-3-nano-4b\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl nemotron-3-nano-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or Nemotron 3 Nano 4B\n\n- [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.md)\n- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.md)\n- [Apertus v1.5 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.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- [Apodex-1.1-mini vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano 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