{
  "data": {
    "a": {
      "slug": "nanbeige-4-2-3b",
      "name": "Nanbeige4.2-3B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json",
      "dns": "nanbeige-4-2-3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (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/Nanbeige/Nanbeige4.2-3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "small",
        "looped-transformer",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-09-11",
      "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": 62,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 72
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
        "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "strengths": [
          "Trained for multi-step tool use, with parsers in released SGLang",
          "A 256K context in a model that fits 8 GB",
          "Apache 2.0, ungated",
          "FP8 and 8-bit builds from Nanbeige",
          "Nanbeige replied in most of the recent discussions"
        ],
        "weaknesses": [
          "Its own architecture: Transformers loads it only with the repository's code",
          "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion",
          "The card's serve instructions still point at Nanbeige's forks",
          "No safety results on the card",
          "Training data described only in general terms",
          "Not changed since September"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`",
          "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`",
          "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat",
          "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card",
          "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's",
          "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it"
        ],
        "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": 85,
          "risk": 55,
          "upkeep": 72
        },
        "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\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-2-3b"
      },
      "sameCompany": [
        "nanbeige-4-3-33b"
      ],
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata",
        "params": "4.2B",
        "context": 262144,
        "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Nanbeige4.2-3B-FP8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's FP8 build"
          },
          {
            "name": "Nanbeige4.2-3B-GPTQ-Int8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's 8-bit GPTQ build"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 2.68,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 4.43,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026"
          },
          {
            "name": "Nanbeige4.2-3B (BF16 safetensors)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.34,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Two shards"
          }
        ],
        "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "runBuild": "Nanbeige4.2-3B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.2",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.2-3B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed",
        "domain": "zhipin.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 29 commits from 21 July to 11 September 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json"
    },
    "answer": "Nanbeige4.2-3B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk.",
    "b": {
      "slug": "nemotron-3-5-bolt-e4b-4gb",
      "name": "NVIDIA Nemotron 3.5 Bolt E4B 4GB",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3.5 Bolt E4B 4GB is NVIDIA's open-weight small language model for local voice assistants, interactive characters and device agents, post-trained from Google's Gemma 4 E4B, with 8B parameters (4B effective) and a 64K-token context.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json",
      "dns": "nemotron-3-5-bolt-e4b-4gb.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3.5-bolt:e4b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB, NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 and both DFlash drafter repositories (checked 2026-10-24).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 for NVIDIA's post-training and Apache 2.0 for the Gemma 4 base. Running it costs the hardware, 6.18 GB for NVIDIA's Q4_K_M GGUF and 16.06 GB for the BF16 repository. NVIDIA's API catalogue also serves it as nvidia/nemotron-3.5-bolt-e4b, which this listing doesn't grade (checked 2026-10-24).",
      "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 2026-10-24).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "gemma4",
        "fine-tune",
        "reasoning",
        "tool-calling",
        "conversational",
        "edge",
        "gguf",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-23",
      "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": 39,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.",
        "bestFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.",
        "strengths": [
          "NVIDIA's Q4_K_M GGUF is 6.18 GB, and NVIDIA measured 3.5 to 5 GB of VRAM in use on RTX and Jetson",
          "Released support in llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio",
          "Trained for multi-step tool use and instruction hierarchy, with the gemma4 parser named and several calls per turn",
          "Thinking switches per request and can be budgeted, and conversational_mode asks for brief spoken replies",
          "OpenMDW-1.1 on NVIDIA's part and Apache 2.0 on the Gemma 4 base, no gate, and the RL datasets published",
          "DFlash drafters for llama.cpp and vLLM, which NVIDIA says speed decoding by up to 2.7 times"
        ],
        "weaknesses": [
          "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway",
          "No safety evaluation results published",
          "One quantised build from NVIDIA, and its GGUF is text only with no vision or audio projector",
          "The card says thinking is on by default while the template defaults to off",
          "Independent results so far cover instruction following only (IFBench and MultiChallenge)"
        ],
        "agentNotes": [
          "Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K",
          "With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking",
          "With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja",
          "Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do",
          "Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says",
          "Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt",
          "Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says",
          "Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 4,
        "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": 90,
          "risk": 80,
          "upkeep": 65
        },
        "provenanceScore": 76,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3.5-bolt:e4b\n# or, with llama.cpp and NVIDIA's own GGUF (the card's recipe, without Docker):\nhf download nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --local-dir bolt\nllama-server -m bolt/Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000\n# or, with vLLM 0.31.0 or later on the BF16 weights:\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 --served-model-name nemotron-bolt --max-model-len 65536 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 \\\n  --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nemotron-bolt\", \"messages\": [{\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}],\n       \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-5-bolt-e4b-4gb"
      },
      "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-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA (post-training), on Google's Gemma 4 E4B",
        "builtOn": "google/gemma-4-E4B-it (reinforcement-learning post-training, declared in the BF16 card's metadata; base Apache 2.0)",
        "params": "8B",
        "active": "4B effective",
        "context": 65536,
        "goodFor": "A small, fast cousin for voice and device assistants, game characters and robot interfaces that interleave short replies with multi-turn tool calls on a consumer GPU or a Jetson.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 6.18,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf, 6,180,446,208 bytes, imatrix-calibrated on about 1M tokens of RL rollouts. Text only, no vision or audio projector. Replaced on 23 October 2026 to set the context metadata to 65,536 tokens"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit",
            "who": "lm-studio",
            "precision": "4bit",
            "sizeGB": 4.23,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "LM Studio"
            ],
            "note": "The smallest 4-bit build from a runtime's library, 4,231,265,397 bytes for the repository. LM Studio's catalogue page also lists its Q4_K_M, Q6_K and Q8_0 GGUFs and an 8-bit MLX build, and gives 5 GB as minimum memory"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF",
            "who": "lm-studio",
            "precision": "q4_k_m",
            "sizeGB": 6.18,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "6,180,446,208 bytes, a different file from NVIDIA's by checksum. Q6_K is 6.22 GB and Q8_0 8.03 GB"
          },
          {
            "name": "ollama nemotron-3.5-bolt:e4b-q4_K_M",
            "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 7.9,
            "memoryGB": 12,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it. The e4b tag is 7.2 to 7.9 GB by platform, with text and image input. Ollama shows a 128K context, above NVIDIA's 64K"
          },
          {
            "name": "ollama nemotron-3.5-bolt:e4b-nvfp4",
            "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 7.2,
            "memoryGB": 12,
            "format": "ollama",
            "runsOn": [
              "Ollama (MLX)"
            ],
            "note": "MLX build for Apple silicon with a 64K context, size as Ollama's tags page shows it. Ollama also lists e4b-mxfp8 (12 GB), e4b-q8_0 (9.8 GB) and e4b-bf16 (17 GB)"
          },
          {
            "name": "ggml-org NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 8.03,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation, with a BF16 GGUF (15.05 GB) and a DFlash NVFP4 drafter GGUF. No 4-bit build there"
          },
          {
            "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 16.06,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers",
              "SGLang"
            ],
            "note": "Whole repository, 15.99 GB of weights. The card intends it mainly for fine-tuning and evaluation, and its vLLM recipe sets the image, video and audio limits to 0"
          }
        ],
        "run": "llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000",
        "runBuild": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)",
        "checked": "2026-10-24",
        "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 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"
          },
          {
            "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
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-24",
        "notes": [
          "The card names NVIDIA Corporation as model developer. The files sit in the verified nvidia organisation on Hugging Face, the one developer.nvidia.com and the card's blog link to. Domain date as recorded for the other Nemotron listings.",
          "Nothing is hosted in this listing, so the licences stand in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.",
          "The changelog is the repositories' commit history. Since release only the cards changed, apart from the GGUF replaced on release day to set its context to 65,536 tokens.",
          "www.nvidia.com/.well-known/security.txt stays unknown, as recorded for the other Nemotron listings. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.json",
      "live": {
        "slug": "nemotron-3-5-bolt-e4b-4gb",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:24.848883176Z",
            "changedAt": "2026-11-10T16:23:24.848883176Z",
            "fingerprint": "7228d064ba98"
          }
        ],
        "updatedAt": "2026-11-10T16:23:24.848883176Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "risk",
        "leads": "nemotron-3-5-bolt-e4b-4gb",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "b": "File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins."
        },
        "scores": {
          "a": 55,
          "b": 80
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "nanbeige-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.",
          "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines."
        },
        "scores": {
          "a": 72,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "nemotron-3-5-bolt-e4b-4gb",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.",
          "b": "Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nanbeige LLM Lab (BOSS Zhipin)",
        "b": "NVIDIA",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
        "b": "OpenMDW-1.1",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-11",
        "b": "2026-10-23",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "4/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.2-3B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk.",
        "question": "Which is better for AI agents, Nanbeige4.2-3B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 72 against 65"
        ],
        "also": null,
        "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "slug": "nanbeige-4-2-3b",
        "watchFor": "Its own architecture: Transformers loads it only with the repository's code"
      },
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Risk, 80 against 55"
        ],
        "also": null,
        "goodFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.",
        "slug": "nemotron-3-5-bolt-e4b-4gb",
        "watchFor": "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway"
      }
    ],
    "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-nanbeige-4-2-3b.json",
        "title": "Agents-A1-4B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.json",
        "title": "Apertus v1.5 8B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.json",
        "title": "Apodex-1.1-mini vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.json",
        "title": "Arete-27B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b.json",
        "title": "BigBang-v1 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b"
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    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "nemotron-3-5-bolt-e4b-4gb",
        "key": "reach",
        "name": "Reach",
        "nanbeige-4-2-3b": 85,
        "nemotron-3-5-bolt-e4b-4gb": 90,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "nemotron-3-5-bolt-e4b-4gb",
        "key": "risk",
        "name": "Risk",
        "nanbeige-4-2-3b": 55,
        "nemotron-3-5-bolt-e4b-4gb": 80,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "nanbeige-4-2-3b": 85,
        "nemotron-3-5-bolt-e4b-4gb": 85,
        "weight": 10
      },
      {
        "by": 7,
        "edge": "nanbeige-4-2-3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nanbeige-4-2-3b": 72,
        "nemotron-3-5-bolt-e4b-4gb": 65,
        "weight": 10
      }
    ],
    "summary": "Nanbeige4.2-3B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
      "nemotron-3-5-bolt-e4b-4gb": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results."
    }
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  "markdown": "Nanbeige4.2-3B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk. Both do open-weight models you run yourself.\n\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #49 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.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### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Documentation and upkeep, 72 against 65\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)\n\nGood for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.\n\nAhead on:\n- Reach, 90 against 85\n- Risk, 80 against 55\n\nWatch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway\n\n\n## Why the edge\n\n### Risk: NVIDIA Nemotron 3.5 Bolt E4B 4GB +25\n\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (80): File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins.\n\n### Documentation and upkeep: Nanbeige4.2-3B +7\n\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (65): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines.\n\n### Reach: NVIDIA Nemotron 3.5 Bolt E4B 4GB +5\n\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB (90): Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it.\n\n## Score by category\n\n| Category | Weight | Nanbeige4.2-3B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +5 |\n| Risk | 20% (20 this run) | 55 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 72 | 65 | Nanbeige4.2-3B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nanbeige4.2-3B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-11 | 2026-10-23 |\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 | 4/5 (4) |\n\n## Verdicts\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n**NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.\n\n## Before you call either\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K\n2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking\n3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja\n4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do\n5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says\n6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt\n7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says\n8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines\n\n## Questions\n\n### Which is better for AI agents, Nanbeige4.2-3B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?\n\nNanbeige4.2-3B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanbeige-4-2-3b\", \"b\": \"nemotron-3-5-bolt-e4b-4gb\"}`. From a terminal: `anchor compare nanbeige-4-2-3b nemotron-3-5-bolt-e4b-4gb`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n\n## Other comparisons with Nanbeige4.2-3B or NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n- [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.md)\n- [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apertus v1.5 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.md)\n- [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apodex-1.1-mini vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.md)\n- [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Arete-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [BigBang-v1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b.md)\n- [BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Carbon-X vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.md)\n- [Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cola2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b.md)\n- [Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-2-3b.md)\n- [Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Darwin-36B-Opus vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-2-3b.md)\n- [Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek-V3.2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-2-3b.md)\n- [DeepSeek-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek V4.1 Flash vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek-V4-Flash-0731 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-2-3b.md)\n- [DeepSeek-V4-Flash-0731 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Devstral Small 2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-2-3b.md)\n- [Devstral Small 2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DiffusionGemma 26B A4B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-2-3b.md)\n- [DiffusionGemma 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [EXAONE 4.5 33B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-2-3b.md)\n- [EXAONE 4.5 33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 12B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-2-3b.md)\n- [Gemma 4 12B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 26B A4B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-2-3b.md)\n- [Gemma 4 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 31B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-2-3b.md)\n- [Gemma 4 31B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 E2B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-2-3b.md)\n- [Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 E4B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-2-3b.md)\n- [Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [GLM-4.5-Air vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-2-3b.md)\n- [GLM-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- 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vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-2-3b.md)\n- [gpt-oss-20b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Granite 4.2 30B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Granite 4.2 3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Granite 4.2 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-2-3b.md)\n- [Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Hy3 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-2-3b.md)\n- [Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling-Small vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-2-3b.md)\n- [Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b.md)\n- [Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-2-3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3.1 Pro vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.md)\n- [Kimi K3.1 Pro vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-2-3b.md)\n- [Kimi K3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kolibri-1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b.md)\n- [Kolibri-1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kronos vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.md)\n- [Kronos vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Laguna M.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-2-3b.md)\n- [Laguna M.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Laguna S 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Laguna XS 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna XS 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LBS-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b.md)\n- [LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM2.5-2.6B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-2-3b.md)\n- [LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-1.7B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b.md)\n- [LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-Audio-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-Omni-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-Omni-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-VL-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Ling-3.0-tiny vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nanbeige-4-2-3b.md)\n- [Ling-3.0-tiny vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Ling-3.1-flash-1019 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-2-3b.md)\n- [Ling-3.1-flash-1019 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LongCat-2.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-2-3b.md)\n- [LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 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