{
  "data": {
    "a": {
      "slug": "laguna-xs-2-1",
      "name": "Laguna XS 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json",
      "dns": "laguna-xs-2-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/poolside/Laguna-XS-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-xs-2.1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found (checked 2026-11-03).",
      "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/poolside/Laguna-XS-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "openmdw-1.1",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-19",
      "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": 36,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.",
        "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "Tool calling and the poolside_v1 parser documented on the card, several calls a turn",
          "Thinking on or off per request, with the reasoning returned in reasoning_content"
        ],
        "weaknesses": [
          "No reply from Poolside to any discussion, including reports about the tokenizer and remote code",
          "No safety results and only general cautions for agent use",
          "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code",
          "The template defaults thinking off while generation_config.json turns it on",
          "No structured output documentation, and no limitations, languages or cut-off on the card",
          "No hosted endpoint since Poolside's OpenRouter endpoints expired on 31 October 2026, so an agent without the hardware can't call it"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF",
          "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)",
          "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds",
          "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding",
          "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped",
          "Sampling per the card is temperature 1.0 and top_k 20",
          "The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "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": 80,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run laguna-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n  --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-xs-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-s-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "33.4B total, 3B active",
        "context": 262144,
        "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-XS-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "Laguna-XS-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ],
            "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)"
          },
          {
            "name": "ollama laguna-xs-2.1:latest",
            "url": "https://ollama.com/library/laguna-xs-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships"
          },
          {
            "name": "Laguna-XS-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.3,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000",
        "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "params": "117.56B (Hub safetensors count; the name gives only the size class S)",
            "listing": "https://www.anchorterminal.com/tools/laguna-s-2-1"
          },
          {
            "name": "Laguna-S-2.1-base",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-base",
            "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)"
          },
          {
            "name": "Laguna-XS.2",
            "url": "https://huggingface.co/poolside/Laguna-XS.2",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)"
          }
        ],
        "unrestrictedCopies": 1
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's blog post for this release links its collection there.",
          "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.json",
      "live": {
        "slug": "laguna-xs-2-1",
        "securityTxt": {
          "url": "https://poolside.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2028-04-01T12:00:00.000Z",
          "checkedAt": "2026-11-10T16:09:25.614312659Z"
        },
        "domain": {
          "domain": "poolside.ai",
          "registered": "2020-07-07",
          "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai",
          "checkedAt": "2026-11-08T14:58:52.089559761Z"
        },
        "updatedAt": "2026-11-10T16:09:25.614312659Z"
      }
    },
    "answer": "Laguna XS 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 15,
        "category": "reach",
        "leads": "laguna-xs-2-1",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.",
          "b": "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."
        },
        "scores": {
          "a": 80,
          "b": 65
        }
      },
      {
        "by": 13,
        "category": "upkeep",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 10, the linked report describes sources and filtering and Poolside's S 2.1 post gives the shared November 2025 cut-off. Last change 15, 19 August 2026, 76 days back. Responsiveness 0, Poolside answered none of the 6 discussions. Version policy 5, XS.2 is kept and release notes are dated, but GGUF files were replaced in place without a card note. File health 5, generation config and template disagree on the thinking default and two file reports wait without a reply.",
          "b": "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."
        },
        "scores": {
          "a": 46,
          "b": 59
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "laguna-xs-2-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation that Poolside's blog links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.",
          "b": "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."
        },
        "scores": {
          "a": 55,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "b": "Xiaomi (MiMo team)",
        "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": "OpenMDW-1.1",
        "b": "MIT (metadata only, no LICENSE file)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-19",
        "b": "2026-10-28",
        "name": "Last release"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna XS 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Laguna XS 2.1 or MiMo-V2.6-Flash-RL?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 65",
          "Risk, 55 against 45"
        ],
        "also": null,
        "goodFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "slug": "laguna-xs-2-1",
        "watchFor": "No reply from Poolside to any discussion, including reports about the tokenizer and remote code"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 59 against 46"
        ],
        "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"
      }
    ],
    "history": {
      "laguna-xs-2-1": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "mimo-v2-6-flash-rl": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1"
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      {
        "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/apertus-v1-5-8b-vs-laguna-xs-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1"
      },
      {
        "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"
      },
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl"
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      {
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl"
      },
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1"
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl"
      },
      {
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        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-super-120b-a12b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.json",
        "title": "MiMo-V2.6-Flash-RL vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.json",
        "title": "MiMo-V2.6-Flash-RL vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-olmo-3-7b-think.json",
        "title": "MiMo-V2.6-Flash-RL vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b.json",
        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-397b.json",
        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-9b.json",
        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it.json",
        "title": "MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-4b-instruct-2507.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-0-8b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-122b-a10b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-2b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-397b-a17b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-35b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-27b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.json",
        "title": "MiMo-V2.6-Flash-RL vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview.json",
        "title": "MiMo-V2.6-Flash-RL vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.json",
        "title": "MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b.json",
        "title": "MiMo-V2.6-Flash-RL vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.json",
        "title": "MiMo-V2.6-Flash-RL vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.json",
        "title": "MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.json",
        "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.json",
        "title": "MiMo-V2.6-Flash-RL vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "laguna-xs-2-1",
        "key": "reach",
        "laguna-xs-2-1": 80,
        "mimo-v2-6-flash-rl": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "laguna-xs-2-1",
        "key": "risk",
        "laguna-xs-2-1": 55,
        "mimo-v2-6-flash-rl": 45,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "laguna-xs-2-1": 85,
        "mimo-v2-6-flash-rl": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 13,
        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "laguna-xs-2-1": 46,
        "mimo-v2-6-flash-rl": 59,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Laguna XS 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl",
    "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.min.md"
  },
  "markdown": "Laguna XS 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna XS 2.1: grade C, 61/100, rank #46 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\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- 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### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nAhead on:\n- Reach, 80 against 65\n- Risk, 55 against 45\n\nWatch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code\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- Documentation and upkeep, 59 against 46\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n\n## Why the edge\n\n### Reach: Laguna XS 2.1 +15\n\n- Laguna XS 2.1 (80): Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.\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\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +13\n\n- Laguna XS 2.1 (46): Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 10, the linked report describes sources and filtering and Poolside's S 2.1 post gives the shared November 2025 cut-off. Last change 15, 19 August 2026, 76 days back. Responsiveness 0, Poolside answered none of the 6 discussions. Version policy 5, XS.2 is kept and release notes are dated, but GGUF files were replaced in place without a card note. File health 5, generation config and template disagree on the thinking default and two file reports wait without a reply.\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\n### Risk: Laguna XS 2.1 +10\n\n- Laguna XS 2.1 (55): File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation that Poolside's blog links, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.\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\n## Score by category\n\n| Category | Weight | Laguna XS 2.1 | MiMo-V2.6-Flash-RL | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 65 | Laguna XS 2.1 +15 |\n| Risk | 20% (20 this run) | 55 | 45 | Laguna XS 2.1 +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 46 | 59 | MiMo-V2.6-Flash-RL +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna XS 2.1 | MiMo-V2.6-Flash-RL |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | Xiaomi (MiMo team) |\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 | OpenMDW-1.1 | MIT (metadata only, no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-10-28 |\n| Terms last updated | 2026-07-21 | no document linked |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### Laguna XS 2.1\n\n- no change on 3 November 2026: product\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\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## Before you call either\n\n### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Laguna XS 2.1 or MiMo-V2.6-Flash-RL?\n\nLaguna XS 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-xs-2-1\", \"b\": \"mimo-v2-6-flash-rl\"}`. From a terminal: `anchor compare laguna-xs-2-1 mimo-v2-6-flash-rl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n\n## Other comparisons with Laguna XS 2.1 or MiMo-V2.6-Flash-RL\n\n- [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md)\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- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.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- [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.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- 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