{
  "data": {
    "a": {
      "slug": "mimo-v2-6-flash-rl",
      "name": "MiMo-V2.6-Flash-RL",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json",
      "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "license": "MIT (metadata only, no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "audio",
        "video",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "mit",
        "safetensors",
        "fp4",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-10-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 59,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
        "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "strengths": [
          "MIT, ungated, with hosted options",
          "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang",
          "A 1M context and four input types",
          "Trained with reinforcement learning across agent harnesses",
          "A GGUF from the llama.cpp project's own organisation"
        ],
        "weaknesses": [
          "178 GB at 4 bits, so it needs about 200 GB of memory",
          "The card's serve commands pass trust-remote-code",
          "A pickle file sits in the draft model's folder",
          "No safety results, limitations or data sources on the card",
          "Xiaomi replied in a few of the discussions only"
        ],
        "agentNotes": [
          "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita",
          "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`",
          "Use temperature 1.0 and top_p 0.95, as the card recommends",
          "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in",
          "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle",
          "The Use Restrictions section asks for human oversight of high-risk actions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 45,
          "upkeep": 59
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags",
        "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-flash-rl"
      },
      "sameCompany": [
        "mimo-v2-6-distill-qwen-9b"
      ],
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "params": "309B",
        "active": "15B",
        "context": 1048576,
        "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiMo-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "who": "publisher",
            "precision": "fp4",
            "sizeGB": 177.74,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build Xiaomi publishes, with most weights at 4 bits"
          },
          {
            "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF",
            "who": "llama.cpp",
            "precision": "mxfp4",
            "sizeGB": 168.93,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026"
          }
        ],
        "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo",
        "runBuild": "MiMo-V2.6-Flash-RL",
        "checked": "2026-11-10",
        "minMemoryGB": 256
      },
      "family": {
        "lab": "Xiaomi (MiMo)",
        "family": "MiMo-V2 (V2, V2.5, V2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#xiaomi",
        "others": [
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "params": "9B (Hub safetensors count 9.41B)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b"
          },
          {
            "name": "MiMo-V2.6-Pro-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Pro-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-ASR",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR",
            "params": "7.62B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base",
            "params": "309.77B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash",
            "params": "309.77B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 12
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)",
        "domain": "mimo.xiaomi.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.",
          "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 13 commits from 21 September to 28 October 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json"
    },
    "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
    "b": {
      "slug": "minimax-m2-7",
      "name": "MiniMax-M2.7",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.7 is MiniMax's open-weight mixture-of-experts model for coding, agent harnesses and office work, about 229B parameters in FP8 with thinking always on, published on Hugging Face on 9 April 2026 under a non-commercial licence.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-7",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json",
      "dns": "minimax-m2-7.grade.anchorterminal.com",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "license": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.7"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and to use for personal, research and non-profit purposes. Since 11 April 2026 the licence prohibits commercial use without MiniMax's prior written authorisation, and commercial users must display \"Built with MiniMax M2.7\". Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.7 to new users on 19 October 2026 and retires it on 18 November 2026 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "non-commercial",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-04-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.6,
        "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": 80,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.",
        "bestFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio, and in Ollama through its pinned llama.cpp",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and about 200K tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by three inference providers on Hugging Face"
        ],
        "weaknesses": [
          "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note",
          "230 GB in FP8, and about 166 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact, and every loading route runs repository code",
          "No MiniMax reply in discussions since June 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7",
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence",
          "On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn",
          "Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer",
          "Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.6
          }
        ],
        "editorialScores": {
          "capability": 71,
          "interface": 75,
          "reach": 60,
          "risk": 30,
          "upkeep": 42
        },
        "provenanceScore": 60,
        "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": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.7\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-7"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m3-1-flash"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 204800,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses and office documents, with parallel tool calls and always-on thinking, text only, for non-commercial use unless MiniMax authorises otherwise.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LM Studio minimax/minimax-m2.7 (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/MiniMax-M2.7-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 138.3,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "MiniMax-M2.7-Q4_K_M split GGUF, 138.34 GB summed from the Hub's file list. The catalogue entry also lists Q6_K (187.8 GB) and Q8_0 (243.1 GB). LM Studio states 138 GB minimum memory"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.2,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8 E4M3 with 128 by 128 blocks; the only build MiniMax publishes. Size summed from the Hub's file list"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.7",
        "checked": "2026-11-04",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-5"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "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": "2026-10-21",
        "notes": [
          "The current licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api listing.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. Commit messages are uploads and README or LICENSE updates; the licence change on 11 April 2026 reads only \"Delete LICENSE-MODEL\" and \"Upload LICENSE with huggingface_hub\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, and MiniMax-AI/MiniMax-M2.7 has no SECURITY.md, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links the GitHub repository, so the repository is the publisher's own."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-7.json",
      "live": {
        "slug": "minimax-m2-7",
        "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"
      }
    },
    "edges": [
      {
        "by": 17,
        "category": "upkeep",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.",
          "b": "The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply."
        },
        "scores": {
          "a": 59,
          "b": 42
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Risk",
        "reasons": {
          "a": "File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.",
          "b": "File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. 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, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories."
        },
        "scores": {
          "a": 45,
          "b": 30
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.",
          "b": "Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Xiaomi (MiMo team)",
        "b": "MiniMax",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (metadata only, no LICENSE file)",
        "b": "MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-28",
        "b": "2026-04-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or MiniMax-M2.7?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 65 against 60",
          "Risk, 45 against 30",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 59 against 42"
        ],
        "also": null,
        "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context",
        "slug": "mimo-v2-6-flash-rl",
        "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls",
        "slug": "minimax-m2-7",
        "watchFor": "Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note"
      }
    ],
    "history": {
      "mimo-v2-6-flash-rl": null,
      "minimax-m2-7": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-04",
          "delta": 1.5,
          "from": 56.1,
          "fromGrade": "C",
          "to": 57.6,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.json",
        "title": "Agents-A1-4B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json",
        "title": "Agents-A1-4B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.json",
        "title": "Apertus v1.5 8B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.json",
        "title": "Apodex-1.1-mini vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.json",
        "title": "Arete-27B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.json",
        "title": "Arete-27B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json",
        "title": "BigBang-v1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.json",
        "title": "BigBang-v1 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl.json",
        "title": "Carbon-X vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7.json",
        "title": "Carbon-X vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json",
        "title": "Cola2 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7.json",
        "title": "Cola2 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "Cosmos-Reason2-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7.json",
        "title": "Cosmos-Reason2-2B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl.json",
        "title": "Darwin-36B-Opus vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7.json",
        "title": "Darwin-36B-Opus vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl.json",
        "title": "DeepSeek-V3.2 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7.json",
        "title": "DeepSeek-V3.2 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.json",
        "title": "DeepSeek V4.1 Flash vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.json",
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      },
      {
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        "title": "Devstral Small 2 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl"
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      {
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.json",
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-7.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.json",
        "title": "EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL",
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.json",
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl.json",
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl.json",
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      {
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      },
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7"
      },
      {
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-7.json",
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.json",
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      {
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 8B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.json",
        "title": "Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.json",
        "title": "Hy3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl.json",
        "title": "Inkling-Small vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7"
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        "title": "LFM2.5-2.6B vs MiMo-V2.6-Flash-RL",
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        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl"
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7"
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-minimax-m2-7.json",
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      {
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl"
      },
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minimax-m2-7.json",
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      },
      {
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      {
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        "title": "MiniMax-M2.7 vs Nemotron 3 Nano 4B",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M2.7 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.json",
        "title": "MiniMax-M2.7 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.json",
        "title": "MiniMax-M2.7 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.json",
        "title": "MiniMax-M2.7 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.json",
        "title": "MiniMax-M2.7 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b"
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        "title": "MiniMax-M2.7 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.json",
        "title": "MiniMax-M2.7 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b.json",
        "title": "MiniMax-M2.7 vs ZAYA1-8B",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "mimo-v2-6-flash-rl",
        "key": "reach",
        "mimo-v2-6-flash-rl": 65,
        "minimax-m2-7": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "edge": "mimo-v2-6-flash-rl",
        "key": "risk",
        "mimo-v2-6-flash-rl": 45,
        "minimax-m2-7": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "mimo-v2-6-flash-rl",
        "key": "interface",
        "mimo-v2-6-flash-rl": 85,
        "minimax-m2-7": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 17,
        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "mimo-v2-6-flash-rl": 59,
        "minimax-m2-7": 42,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.",
      "minimax-m2-7": "A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release."
    }
  },
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  "links": {
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  "markdown": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n- MiniMax-M2.7: grade C, 57.6/100, rank #106 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Reach, 65 against 60\n- Risk, 45 against 30\n- Agent interface, 85 against 75\n- Documentation and upkeep, 59 against 42\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### MiniMax-M2.7 (C)\n\nGood for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls\n\nWatch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note\n\n\n## Why the edge\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +17\n\n- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.\n- MiniMax-M2.7 (42): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns nothing, since only the training process is described. The last change earns 20, a card commit on 26 October 2026 that renames a MiniMax Agent link to MiniMax Code, 9 days before this check; the files, template and code last changed on 20 April 2026. Responsiveness earns 10, with a MiniMax reply in 8 of 36 discussions, none since June. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns 5, because a report that it doesn't load in Transformers v5 waits without a reply.\n\n### Risk: MiMo-V2.6-Flash-RL +15\n\n- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.\n- MiniMax-M2.7 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. 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, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses appendix goes beyond the law, MiniMax swapped a commercial licence for a non-commercial one two days after release, and neither that swap nor the amendment three days later carries a dated note. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories.\n\n### Agent interface: MiMo-V2.6-Flash-RL +10\n\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- MiniMax-M2.7 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 204,800 tokens in the config with no position scaling.\n\n### Reach: MiMo-V2.6-Flash-RL +5\n\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n- MiniMax-M2.7 (60): Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's Q4_K_M GGUF at 138.3 GB, about 166 GB with the fifth added, so it needs more than our 107.4 GB machine and fits within 256 GB. Runtimes earn the full 25, with model code in released vLLM, SGLang, llama.cpp and Transformers, an LM Studio catalogue entry, and Ollama 0.40.14, whose pinned llama.cpp b11963 has the minimax-m2 architecture (capped at 25). Builds earn 10 for one publisher precision, the FP8 release. Terms earn 5, since the licence today allows only personal, research and non-profit use without MiniMax's written authorisation. Download earns 10, with no gate or account. Hosted earns 5, since Novita, Featherless AI and DeepInfra serve it through Hugging Face, while MiniMax's own API is closed to new users and retires it on 18 November 2026.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | MiniMax-M2.7 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 60 | MiMo-V2.6-Flash-RL +5 |\n| Risk | 20% (20 this run) | 45 | 30 | MiMo-V2.6-Flash-RL +15 |\n| Agent interface | 10% (10 this run) | 85 | 75 | MiMo-V2.6-Flash-RL +10 |\n| Documentation and upkeep | 10% (10 this run) | 59 | 42 | MiMo-V2.6-Flash-RL +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | MiniMax-M2.7 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | MiniMax |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (metadata only, no LICENSE file) | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-04-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 1.8/5 (4) |\n\n## How the scores have moved\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n### MiniMax-M2.7\n\n- +1.5 on 4 November 2026: product\n\n## Verdicts\n\n**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\n\n**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.\n\n## Before you call either\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\n\n### MiniMax-M2.7\n\n1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7\n2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence\n5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory\n6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn\n7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer\n8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or MiniMax-M2.7?\n\nMiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.7's 57.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"minimax-m2-7\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl minimax-m2-7`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or MiniMax-M2.7\n\n- [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.md)\n- [Agents-A1-4B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.md)\n- [Apertus v1.5 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.md)\n- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.md)\n- [Apodex-1.1-mini vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Apodex-1.1-mini vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-7.md)\n- [Arete-27B 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