{
  "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",
      "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": 50.5,
        "grade": "D",
        "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": 1211,
        "categoryRank": 78,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 64,
          "interface": 70,
          "reach": 40,
          "risk": 35,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-24"
        },
        "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, and vLLM and SGLang support was pre-release on 24 October 2026. 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": [
          "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did",
          "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 no discussion 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's nightly 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": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "capability": 64,
          "interface": 70,
          "reach": 40,
          "risk": 35,
          "upkeep": 41
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-24",
            "interface": "2026-10-24",
            "reach": "2026-10-24",
            "risk": "2026-10-24",
            "upkeep": "2026-10-24"
          },
          "last": "2026-10-24",
          "oldest": "2026-10-24",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM's recipe, 8 x H100 or H200 (nightly image; no pip release supports it on 24 October 2026)\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",
        "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-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"
        ],
        "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 and SGLang ran it only from pre-release images on 24 October 2026"
          }
        ],
        "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": "2026-10-24",
        "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": 55
      },
      "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": "2026-10-26T15:30:49.178284153Z"
        },
        "updatedAt": "2026-10-26T15:30:49.178284153Z"
      }
    },
    "answer": "Nanbeige4.3-33B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category.",
    "b": {
      "slug": "nanbeige-4-3-33b",
      "name": "Nanbeige4.3-33B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.3-33B is the Nanbeige LLM Lab's open-weight dense model for agentic coding and office work, 33B parameters on the Llama architecture with a 512K-token context, thinking that switches per request and tool calls in an XML format.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.3-33B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for Nanbeige/Nanbeige4.3-33B and Nanbeige/Nanbeige4.3-33B-Base (checked 2026-10-25).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata names it. Neither repository holds a LICENSE file. Running it costs the hardware, 66.26 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-25).",
      "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 2026-10-25).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-25"
      },
      "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "llama",
        "reasoning",
        "tool-calling",
        "coding",
        "agentic",
        "long-context",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-24",
      "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": 1211,
        "categoryRank": 48,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 55,
          "risk": 60,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-25"
        },
        "negative": 0,
        "verdict": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.",
        "bestFor": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.",
        "strengths": [
          "A plain LlamaForCausalLM with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it",
          "Apache 2.0 by its metadata, ungated, safetensors only",
          "524,288 tokens of context set in config.json with no rope scaling",
          "Thinking switches per request, and preserve_thinking keeps earlier reasoning for agent loops",
          "Tool calls in an XML format the qwen3_coder parser reads, several per turn",
          "A Nanbeige member answered four of the first ten discussions on release day"
        ],
        "weaknesses": [
          "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run",
          "BF16 only from Nanbeige, about 80 GB to load, and no Ollama or LM Studio build",
          "No LICENSE file in either repository; Apache 2.0 rests on the metadata tag",
          "No safety evaluation, agent-use guidance, training data description or knowledge cut-off",
          "No hosted API or inference provider serves it"
        ],
        "agentNotes": [
          "Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers",
          "Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes",
          "Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends",
          "Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card",
          "Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed",
          "Send one system message at the start only, as the template raises an error for a later one",
          "In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 55,
          "risk": 60,
          "upkeep": 56
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-25",
            "reach": "2026-10-25",
            "risk": "2026-10-25",
            "upkeep": "2026-10-25"
          },
          "last": "2026-10-25",
          "oldest": "2026-10-25",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM 0.32.0 or SGLang 0.5.22, as the card's commands (the card uses four GPUs with tensor parallelism)\nhf download Nanbeige/Nanbeige4.3-33B --local-dir Nanbeige4.3-33B\nvllm serve ./Nanbeige4.3-33B --served-model-name nanbeige --tensor-parallel-size 4 \\\n  --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or\npython -m sglang.launch_server --model-path ./Nanbeige4.3-33B --tp-size 4 \\\n  --reasoning-parser qwen3 --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nanbeige\", \"messages\": [{\"role\": \"user\", \"content\": \"Which number is bigger, 9.11 or 9.8?\"}],\n       \"temperature\": 0.6, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-3-33b"
      },
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige/Nanbeige4.3-33B-Base (fine-tune, declared in the card's metadata; Apache 2.0 by its tag, no LICENSE file)",
        "params": "33B",
        "context": 524288,
        "goodFor": "A cousin for long agentic coding and office tasks on a machine with about 96 GB of memory, if Nanbeige's own results hold up, with a 512K context and per-request thinking.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Nanbeige4.3-33B (BF16)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.26,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Whole repository, 66.24 GB of weights in 14 shards. 66.26 GB × 1.2 = 79.5 GB. The only build from Nanbeige; no Ollama, LM Studio or ggml-org build exists, and llama.cpp needs a GGUF converted with its own script or taken from someone else"
          }
        ],
        "run": "vllm serve Nanbeige/Nanbeige4.3-33B --tensor-parallel-size 4 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder",
        "runBuild": "Nanbeige4.3-33B (BF16)",
        "checked": "2026-10-25",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.3",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.3-33B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base",
            "params": "33B"
          }
        ],
        "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/Nanbeige4.3-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-25",
        "notes": [
          "The card names the Nanbeige Team and nothing else. BOSS Zhipin is identified as the maker by the lab's Nanbeige4.2-3B report on arXiv (Nanbeige LLM Lab, Boss Zhipin), which links huggingface.co/Nanbeige, and by the chat template's default system prompt naming BOSS Zhipin as developer and trainer. The card's contact address is on kanzhun.com. Kanzhun Limited as BOSS Zhipin's operator wasn't confirmed first-hand, since ir.zhipin.com refused our reader. The domain's registration date wasn't checked.",
          "The Hugging Face organisation Nanbeige (Nanbeige LLM Lab) isn't verified by the Hub. The arXiv report's link to it is the publisher's own documentation pointing there.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history.",
          "www.zhipin.com/.well-known/security.txt answers 404 and kanzhun.com's redirects to the home page. BOSS Zhipin runs a security response centre at security.zhipin.com for reporting flaws in its products; nothing links it to the model."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.json"
    },
    "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": "2026-10-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.3-33B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, MiniMax-M3.1-Flash or Nanbeige4.3-33B?"
      }
    ],
    "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": "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did"
      },
      {
        "aheadOn": [
          "Reach, 55 against 40",
          "Risk, 60 against 35",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 56 against 41"
        ],
        "also": null,
        "goodFor": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.",
        "slug": "nanbeige-4-3-33b",
        "watchFor": "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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      },
      {
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        "title": "Apertus v1.5 8B vs MiniMax-M3.1-Flash",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b"
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Nanbeige4.3-33B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Nanbeige4.3-33B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0.json",
        "title": "Nanbeige4.3-33B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-olmo-3-7b-think.json",
        "title": "Nanbeige4.3-33B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nanbeige4.3-33B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-397b.json",
        "title": "Nanbeige4.3-33B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.json",
        "title": "Nanbeige4.3-33B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Nanbeige4.3-33B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507.json",
        "title": "Nanbeige4.3-33B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint.json",
        "title": "Nanbeige4.3-33B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.json",
        "title": "Nanbeige4.3-33B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.json",
        "title": "Nanbeige4.3-33B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "nanbeige-4-3-33b",
        "key": "reach",
        "minimax-m3-1-flash": 40,
        "name": "Reach",
        "nanbeige-4-3-33b": 55,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "nanbeige-4-3-33b",
        "key": "risk",
        "minimax-m3-1-flash": 35,
        "name": "Risk",
        "nanbeige-4-3-33b": 60,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "nanbeige-4-3-33b",
        "key": "interface",
        "minimax-m3-1-flash": 70,
        "name": "Agent interface",
        "nanbeige-4-3-33b": 85,
        "weight": 10
      },
      {
        "by": 15,
        "edge": "nanbeige-4-3-33b",
        "key": "upkeep",
        "minimax-m3-1-flash": 41,
        "name": "Documentation and upkeep",
        "nanbeige-4-3-33b": 56,
        "weight": 10
      }
    ],
    "summary": "Nanbeige4.3-33B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "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, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.",
      "nanbeige-4-3-33b": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file."
    }
  },
  "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-4-3-33b",
    "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.min.md"
  },
  "markdown": "Nanbeige4.3-33B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #100 of 111 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- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.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 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniMax-M3.1-Flash (D)\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: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did\n\n### Nanbeige4.3-33B (C)\n\nGood for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.\n\nAhead on:\n- Reach, 55 against 40\n- Risk, 60 against 35\n- Agent interface, 85 against 70\n- Documentation and upkeep, 56 against 41\n\nWatch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M3.1-Flash | Nanbeige4.3-33B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 55 | Nanbeige4.3-33B +15 |\n| Risk | 20% (20 this run) | 35 | 60 | Nanbeige4.3-33B +25 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Nanbeige4.3-33B +15 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 56 | Nanbeige4.3-33B +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M3.1-Flash | Nanbeige4.3-33B |\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 | 2026-10-24 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.3/5 (4) | none |\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, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.\n\n**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.\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's nightly 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### Nanbeige4.3-33B\n\n1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers\n2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes\n3. Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends\n4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card\n5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed\n6. Send one system message at the start only, as the template raises an error for a later one\n7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M3.1-Flash or Nanbeige4.3-33B?\n\nNanbeige4.3-33B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), 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-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m3-1-flash\", \"b\": \"nanbeige-4-3-33b\"}`. From a terminal: `anchor compare minimax-m3-1-flash nanbeige-4-3-33b`\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-4-3-33b.json\n\n## Other comparisons with MiniMax-M3.1-Flash or Nanbeige4.3-33B\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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.md)\n- [BigBang-v1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m3-1-flash.md)\n- [BigBang-v1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.md)\n- [Carbon-X vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m3-1-flash.md)\n- [Carbon-X vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.md)\n- [Cosmos-Reason2-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m3-1-flash.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.md)\n- [Darwin-36B-Opus vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m3-1-flash.md)\n- [Darwin-36B-Opus vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V3.2 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m3-1-flash.md)\n- [DeepSeek-V3.2 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m3-1-flash.md)\n- [DeepSeek V4.1 Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m3-1-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b.md)\n- [DiffusionGemma 26B A4B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m3-1-flash.md)\n- [DiffusionGemma 26B A4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 12B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 12B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 26B A4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 31B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 31B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E2B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 E2B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E4B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 E4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.md)\n- [GLM-4.5-Air vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m3-1-flash.md)\n- [GLM-4.5-Air vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-3-33b.md)\n- [GLM-4.7-Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m3-1-flash.md)\n- [GLM-4.7-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3-Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m3-1-flash.md)\n- [GLM-5.3-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m3-1-flash.md)\n- [GLM-5.3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-3-33b.md)\n- [gpt-oss-120b vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m3-1-flash.md)\n- [gpt-oss-120b vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nanbeige-4-3-33b.md)\n- [gpt-oss-20b vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m3-1-flash.md)\n- [gpt-oss-20b vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-3-33b.md)\n- [Granite 4.2 3B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m3-1-flash.md)\n- [Granite 4.2 3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-3-33b.md)\n- [Hy3 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.md)\n- [Hy3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-3-33b.md)\n- [Inkling-Small vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m3-1-flash.md)\n- [Inkling-Small vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.md)\n- [Inkling vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/inkling-vs-minimax-m3-1-flash.md)\n- [Inkling vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m3-1-flash.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-3-33b.md)\n- [Kimi K3 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m3-1-flash.md)\n- [Kimi K3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-3-33b.md)\n- [Kolibri-1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m3-1-flash.md)\n- [Kolibri-1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.md)\n- [Kronos vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kronos-vs-minimax-m3-1-flash.md)\n- [Kronos vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-3-33b.md)\n- [Laguna M.1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m3-1-flash.md)\n- [Laguna M.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-3-33b.md)\n- [Laguna S 2.1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m3-1-flash.md)\n- [Laguna S 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-3-33b.md)\n- [Laguna XS 2.1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m3-1-flash.md)\n- [Laguna XS 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-3-33b.md)\n- [LFM2.5-2.6B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.md)\n- [LFM2.5-2.6B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-3-33b.md)\n- [Ling-3.0-tiny vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m3-1-flash.md)\n- [Ling-3.0-tiny vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nanbeige-4-3-33b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m3-1-flash.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nanbeige-4-3-33b.md)\n- [MiniCPM-o 4.5 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m3-1-flash.md)\n- [MiniCPM-o 4.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nanbeige-4-3-33b.md)\n- [MiniCPM5-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minicpm5-2b-vs-minimax-m3-1-flash.md)\n- [MiniCPM5-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M2.5 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minimax-m2-5-vs-minimax-m3-1-flash.md)\n- [MiniMax-M2.5 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M2.7 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/minimax-m2-7-vs-minimax-m3-1-flash.md)\n- [MiniMax-M2.7 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-3-33b.md)\n- [MiniMax-M3.1-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-muse-glimmer-30b.md)\n- [MiniMax-M3.1-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-4b.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [MiniMax-M3.1-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiniMax-M3.1-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-occamy-1-0.md)\n- [MiniMax-M3.1-Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-olmo-3-7b-think.md)\n- [MiniMax-M3.1-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [MiniMax-M3.1-Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-397b.md)\n- [MiniMax-M3.1-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-9b.md)\n- [MiniMax-M3.1-Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiniMax-M3.1-Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-4b-instruct-2507.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-0-8b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-122b-a10b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-2b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-4b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-9b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-27b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-6-35b-a3b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-27b.md)\n- [MiniMax-M3.1-Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-flash-next.md)\n- [MiniMax-M3.1-Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-coder-next.md)\n- [MiniMax-M3.1-Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-2b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-32b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct.md)\n- [MiniMax-M3.1-Flash vs 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