{
  "data": {
    "a": {
      "slug": "minimax-m3-1-flash",
      "name": "MiniMax-M3.1-Flash",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M3.1-Flash is MiniMax's open-weight mixture-of-experts model for coding, agent work, computer use and cybersecurity, about 428B parameters with 23B active, taking text, images and video with up to 1M tokens of context.",
      "url": "https://www.anchorterminal.com/tools/minimax-m3-1-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json",
      "dns": "minimax-m3-1-flash.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
      "license": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M3.1-Flash"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for MiniMaxAI/MiniMax-M3.1-Flash (checked 2026-10-24).",
      "pricing": "free",
      "pricingNotes": "Free to download. The MiniMax Community License allows commercial use if the product shows \"Built with MiniMax M3.1-Flash\" and MiniMax gets a one-time notice by email; products earning more than $20M a year need MiniMax's prior written authorisation. Running it yourself costs the hardware, 250.1 GB of NVFP4 and MXFP8 weights plus a 2.3 GB DSpark draft. MiniMax's API sells the same-named model at $0.15 in and $0.60 out per million tokens (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": "2026-10-24"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "reasoning",
        "tool-calling",
        "coding",
        "nvfp4",
        "mxfp8",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.3,
        "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": 97,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 64,
          "interface": 70,
          "reach": 55,
          "risk": 35,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.",
        "strengths": [
          "Quantisation-aware NVFP4 and MXFP8 release, about 250 GB for 428B parameters, with a DSpark draft included",
          "1,048,576 tokens of context in the config, with sparse attention in every layer",
          "Several tool calls in one block, and reasoning_effort from low to max per request",
          "Text, image and video input in one model",
          "No gate or account, files in MiniMax's own organisation with a SHA-256 for every shard"
        ],
        "weaknesses": [
          "SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code",
          "About 300 GB with a fifth added, more than any 128 GB or 256 GB machine",
          "Commercial use needs a Built with line and a notice to MiniMax, and authorisation above $20M yearly revenue",
          "No safety evaluation, agent guidance or security contact, alongside strong claimed offensive security capability",
          "The card omits limitations, languages, training data and a knowledge cut-off, and none of nine discussions has a MiniMax reply"
        ],
        "agentNotes": [
          "Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue",
          "On 8 H100 or H200 GPUs use vLLM 0.33.0 or later, or the recipe's image vllm/vllm-openai:minimax-m31-flash, with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144",
          "Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code",
          "--block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128",
          "Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95",
          "On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off",
          "For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage",
          "Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.3
          }
        ],
        "editorialScores": {
          "capability": 64,
          "interface": 70,
          "reach": 55,
          "risk": 35,
          "upkeep": 41
        },
        "provenanceScore": 62,
        "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",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM's recipe, 8 x H100 or H200 (the recipe's image; vLLM v0.33.0 of 1 November 2026 meets its minimum version)\ndocker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash \\\n  --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager \\\n  --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin \\\n  --max-model-len 262144 --reasoning-parser minimax_m3 \\\n  --enable-auto-tool-choice --tool-call-parser minimax_m3\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M3.1-Flash\", \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"reasoning_effort\": \"medium\"}, \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m3-1-flash"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m2-7"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "builtOn": "MiniMaxAI/MiniMax-M3 (the card says M3.1-Flash builds on M3 and keeps its mixture-of-experts architecture; not declared as base_model in the metadata; M3 carries the same MiniMax Community License)",
        "params": "428B",
        "context": 1048576,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses, computer use and security work, with images and video in, about 1M tokens of context and parallel tool calls, on a 4 to 8 GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "MiniMax-M3.1-Flash",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 250.12,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Quantisation-aware trained release, routed experts in NVFP4 (group 16) and dense and attention layers in MXFP8, 250,116,419,880 bytes in 48 shards summed from the Hub's file list, plus a 2.30 GB DSpark draft. The only build MiniMax publishes. vLLM v0.33.0 (1 November 2026) meets its recipe's minimum version, and SGLang v0.5.23 (29 October) registers the architecture though its cookbook still points to a pull request or a dev image"
          },
          {
            "name": "unsloth MiniMax-M3.1-Flash-GGUF (UD-Q4_K_XL)",
            "url": "https://huggingface.co/unsloth/MiniMax-M3.1-Flash-GGUF",
            "who": "unsloth",
            "precision": "q4_k_xl",
            "sizeGB": 251.14,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not MiniMax's. 249.40 GB of UD-Q4_K_XL weights plus the 1.74 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "unsloth MiniMax-M3.1-Flash-GGUF (UD-Q8_K_XL)",
            "url": "https://huggingface.co/unsloth/MiniMax-M3.1-Flash-GGUF",
            "who": "unsloth",
            "precision": "q8_k_xl",
            "sizeGB": 260.82,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not MiniMax's. 259.08 GB of UD-Q8_K_XL weights plus the 1.74 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          }
        ],
        "run": "docker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin --max-model-len 262144 --reasoning-parser minimax_m3 --enable-auto-tool-choice --tool-call-parser minimax_m3",
        "runBuild": "MiniMax-M3.1-Flash",
        "checked": "2026-11-04",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M3",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M3",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3",
            "params": "~428B total, ~23B activated"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-24",
        "notes": [
          "The licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api and minimax-m2-7 listings.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The repository's history is one commit, \"Init\", on 23 October 2026.",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 24 October 2026, and MiniMax has no public GitHub repository for M3.1 Flash, so there is no security.txt or SECURITY.md.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links MiniMax's site, API and GitHub organisation, so the repository is the publisher's own."
        ],
        "score": 62
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.json",
      "live": {
        "slug": "minimax-m3-1-flash",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:11.844974518Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-11-08T14:54:44.54388672Z"
        },
        "updatedAt": "2026-11-10T16:08:11.844974518Z"
      }
    },
    "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in every scored category.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 31,
        "category": "upkeep",
        "leads": "nanbeige-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 11, missing limitations, languages and a knowledge cut-off. Training data earns nothing, as it isn't described. The last change earns 20, an evaluation-results commit of 30 October 2026 after the release commit of 23 October. Responsiveness earns nothing, with no MiniMax reply in nine discussions opened from 23 to 31 October. Version policy earns 5 for a first release in its own repository. File health earns 5, for the tokenizer's 40,960,000-token model_max_length against the 1M context, the Transformers library tag against the card, and an unanswered vLLM streaming report. 11 + 0 + 20 + 0 + 5 + 5 = 41.",
          "b": "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."
        },
        "scores": {
          "a": 41,
          "b": 72
        }
      },
      {
        "by": 30,
        "category": "reach",
        "leads": "nanbeige-4-2-3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns nothing. MiniMax's only build is its 250 GB NVFP4 and MXFP8 release, about 300 GB with the fifth added, above 256 GB, and no runtime library has a local build. Runtimes earn 20. llama.cpp from b11861 runs it, Ollama v0.40.14 pins llama.cpp b11963 with the minimax-m3 architecture, vLLM v0.33.0 (tagged 1 November 2026) registers the architecture and meets its recipe's minimum version, and SGLang v0.5.23 (tagged 29 October) registers it with M3.1 processor code and tests, though its cookbook still points to a pull request. Transformers support is in progress per the card and LM Studio has no entry. Builds earn 10 for one publisher precision. Terms earn 10, since commercial use is free with attribution and a notice below $20M of yearly revenue. Download earns 10, with no gate. Hosted earns 5, since MiniMax's API and Novita serve it. 0 + 20 + 10 + 10 + 10 + 5 = 55.",
          "b": "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."
        },
        "scores": {
          "a": 55,
          "b": 85
        }
      },
      {
        "by": 20,
        "category": "risk",
        "leads": "nanbeige-4-2-3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processor code. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing; the launch post's security testing measured offensive capability, and the template's root role isn't documented as a trained hierarchy. Licence risk earns 5, after the military-use ban and the $20M threshold. The security programme earns nothing, with no security.txt, disclosure policy or advisories.",
          "b": "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."
        },
        "scores": {
          "a": 35,
          "b": 55
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "nanbeige-4-2-3b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template shows the format and the parsers come from vLLM's and SGLang's own docs. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, as it isn't documented. Reasoning control earns 10, with reasoning_effort per request on the card and the reasoning in mm:think tags. Context earns 10 for 1,048,576 tokens with no position scaling.",
          "b": "Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85."
        },
        "scores": {
          "a": 70,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "MiniMax",
        "b": "Nanbeige LLM Lab (BOSS Zhipin)",
        "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": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)",
        "b": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-23",
        "b": "2026-09-11",
        "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": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, MiniMax-M3.1-Flash or Nanbeige4.2-3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.",
        "slug": "minimax-m3-1-flash",
        "watchFor": "SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code"
      },
      {
        "aheadOn": [
          "Reach, 85 against 55",
          "Risk, 55 against 35",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 72 against 41"
        ],
        "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"
      }
    ],
    "history": {
      "minimax-m3-1-flash": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 3.8,
          "from": 50.5,
          "fromGrade": "D",
          "to": 54.3,
          "toGrade": "C"
        }
      ],
      "nanbeige-4-2-3b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "title": "Muse Glimmer 30B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.json",
        "title": "Nanbeige4.2-3B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Nanbeige4.2-3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-4b.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Nanbeige4.2-3B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-ultra-550b-a55b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-north-mini-code-1-0.json",
        "title": "Nanbeige4.2-3B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-occamy-1-0.json",
        "title": "Nanbeige4.2-3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think.json",
        "title": "Nanbeige4.2-3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-122b.json",
        "title": "Nanbeige4.2-3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nanbeige4.2-3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.json",
        "title": "Nanbeige4.2-3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-9b.json",
        "title": "Nanbeige4.2-3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it.json",
        "title": "Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Nanbeige4.2-3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Nanbeige4.2-3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-0-8b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-2b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-397b-a17b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-4b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-27b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-35b-a3b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-27b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next.json",
        "title": "Nanbeige4.2-3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-next.json",
        "title": "Nanbeige4.2-3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nanbeige4.2-3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.json",
        "title": "Nanbeige4.2-3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview.json",
        "title": "Nanbeige4.2-3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview.json",
        "title": "Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b.json",
        "title": "Nanbeige4.2-3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b.json",
        "title": "Nanbeige4.2-3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash.json",
        "title": "Nanbeige4.2-3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nanbeige4.2-3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nanbeige4.2-3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2.json",
        "title": "Nanbeige4.2-3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json",
        "title": "Nanbeige4.2-3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json",
        "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b.json",
        "title": "Nanbeige4.2-3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "nanbeige-4-2-3b",
        "key": "reach",
        "minimax-m3-1-flash": 55,
        "name": "Reach",
        "nanbeige-4-2-3b": 85,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "nanbeige-4-2-3b",
        "key": "risk",
        "minimax-m3-1-flash": 35,
        "name": "Risk",
        "nanbeige-4-2-3b": 55,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "nanbeige-4-2-3b",
        "key": "interface",
        "minimax-m3-1-flash": 70,
        "name": "Agent interface",
        "nanbeige-4-2-3b": 85,
        "weight": 10
      },
      {
        "by": 31,
        "edge": "nanbeige-4-2-3b",
        "key": "upkeep",
        "minimax-m3-1-flash": 41,
        "name": "Documentation and upkeep",
        "nanbeige-4-2-3b": 72,
        "weight": 10
      }
    ],
    "summary": "Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "minimax-m3-1-flash": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b",
    "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.min.md"
  },
  "markdown": "Nanbeige4.2-3B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- MiniMax-M3.1-Flash: grade C, 54.3/100, rank #131 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json\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- 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### MiniMax-M3.1-Flash (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.\n\nWatch for: SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code\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- Reach, 85 against 55\n- Risk, 55 against 35\n- Agent interface, 85 against 70\n- Documentation and upkeep, 72 against 41\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n\n## Why the edge\n\n### Documentation and upkeep: Nanbeige4.2-3B +31\n\n- MiniMax-M3.1-Flash (41): The model card earns 11, missing limitations, languages and a knowledge cut-off. Training data earns nothing, as it isn't described. The last change earns 20, an evaluation-results commit of 30 October 2026 after the release commit of 23 October. Responsiveness earns nothing, with no MiniMax reply in nine discussions opened from 23 to 31 October. Version policy earns 5 for a first release in its own repository. File health earns 5, for the tokenizer's 40,960,000-token model_max_length against the 1M context, the Transformers library tag against the card, and an unanswered vLLM streaming report. 11 + 0 + 20 + 0 + 5 + 5 = 41.\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\n### Reach: Nanbeige4.2-3B +30\n\n- MiniMax-M3.1-Flash (55): Memory earns nothing. MiniMax's only build is its 250 GB NVFP4 and MXFP8 release, about 300 GB with the fifth added, above 256 GB, and no runtime library has a local build. Runtimes earn 20. llama.cpp from b11861 runs it, Ollama v0.40.14 pins llama.cpp b11963 with the minimax-m3 architecture, vLLM v0.33.0 (tagged 1 November 2026) registers the architecture and meets its recipe's minimum version, and SGLang v0.5.23 (tagged 29 October) registers it with M3.1 processor code and tests, though its cookbook still points to a pull request. Transformers support is in progress per the card and LM Studio has no entry. Builds earn 10 for one publisher precision. Terms earn 10, since commercial use is free with attribution and a notice below $20M of yearly revenue. Download earns 10, with no gate. Hosted earns 5, since MiniMax's API and Novita serve it. 0 + 20 + 10 + 10 + 10 + 5 = 55.\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\n### Risk: Nanbeige4.2-3B +20\n\n- MiniMax-M3.1-Flash (35): File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processor code. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing; the launch post's security testing measured offensive capability, and the template's root role isn't documented as a trained hierarchy. Licence risk earns 5, after the military-use ban and the $20M threshold. The security programme earns nothing, with no security.txt, disclosure policy or advisories.\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\n### Agent interface: Nanbeige4.2-3B +15\n\n- MiniMax-M3.1-Flash (70): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template shows the format and the parsers come from vLLM's and SGLang's own docs. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, as it isn't documented. Reasoning control earns 10, with reasoning_effort per request on the card and the reasoning in mm:think tags. Context earns 10 for 1,048,576 tokens with no position scaling.\n- Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n## Score by category\n\n| Category | Weight | MiniMax-M3.1-Flash | Nanbeige4.2-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 85 | Nanbeige4.2-3B +30 |\n| Risk | 20% (20 this run) | 35 | 55 | Nanbeige4.2-3B +20 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Nanbeige4.2-3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 72 | Nanbeige4.2-3B +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M3.1-Flash | Nanbeige4.2-3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | Nanbeige LLM Lab (BOSS Zhipin) |\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 | MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-23 | 2026-09-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### MiniMax-M3.1-Flash\n\n- +3.8 on 4 November 2026 (D to C): product\n\n### Nanbeige4.2-3B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.\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## Before you call either\n\n### MiniMax-M3.1-Flash\n\n1. Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue\n2. On 8 H100 or H200 GPUs use vLLM 0.33.0 or later, or the recipe's image vllm/vllm-openai:minimax-m31-flash, with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144\n3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code\n4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128\n5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95\n6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off\n7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage\n8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort\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## Questions\n\n### Which is better for AI agents, MiniMax-M3.1-Flash or Nanbeige4.2-3B?\n\nNanbeige4.2-3B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m3-1-flash\", \"b\": \"nanbeige-4-2-3b\"}`. From a terminal: `anchor compare minimax-m3-1-flash nanbeige-4-2-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n\n## Other comparisons with MiniMax-M3.1-Flash or Nanbeige4.2-3B\n\n- [Agents-A1-4B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m3-1-flash.md)\n- [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.md)\n- [Apertus v1.5 8B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m3-1-flash.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- [Apodex-1.1-mini vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m3-1-flash.md)\n- [Apodex-1.1-mini vs 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