{
  "data": {
    "a": {
      "slug": "ling-3-0-tiny",
      "name": "Ling-3.0-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.0-tiny is inclusionAI's open-weight hybrid reasoning mixture of experts with 7.9B parameters and 1.3B active, mixing Kimi Delta Attention and MLA layers 3 to 1, with switchable thinking and a 131,072-token context, in BF16, FP8, INT4 and GGUF under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-0-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json",
      "dns": "ling-3-0-tiny.grade.anchorterminal.com",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-fp8"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-int4"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repositories hold no LICENSE file). Running it yourself costs the hardware, 15.8 GB for the BF16 files, 8.4 GB for the FP8 build, 5.8 GB for INT4 and 4.8 GB for the Q4_K_M GGUF (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/inclusionAI/Ling-3.0-tiny",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "int4",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-08-31",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.2,
        "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": 88,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 31,
          "interface": 60,
          "reach": 95,
          "risk": 50,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.",
        "bestFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT",
        "strengths": [
          "About 6 GB of memory for inclusionAI's own Q4_K_M GGUF, with FP8, INT4 and three more GGUF precisions",
          "Runs in released Transformers, vLLM, SGLang and llama.cpp, and in Ollama through its pinned llama.cpp",
          "MIT with no gate, no account and no use policy, with checksummed files in inclusionAI's own organisation",
          "Thinking switchable per request, several tool calls a turn and 131,072 tokens of native context",
          "A training-content summary with sources, size and an October 2025 cut-off, and a guardrail probe made for this model"
        ],
        "weaknesses": [
          "No inference provider is listed on the Hub and no inclusionAI API serves it",
          "The card's SGLang recipe asks for built-in MTP the files don't have, and the safetensors index misstates the total size",
          "The documented vLLM route needs --trust-remote-code",
          "No safety results for the model, no structured output and no security contact",
          "Card and SGLang disagree on which tool and reasoning parsers to use, and only one of the seven discussions opened after 18 August has an inclusionAI reply, on 3 November"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3",
          "With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code",
          "With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer",
          "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks",
          "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20",
          "The template keeps earlier reasoning in the prompt, which grows long agent sessions",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented",
          "For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.2
          }
        ],
        "editorialScores": {
          "capability": 31,
          "interface": 60,
          "reach": 95,
          "risk": 50,
          "upkeep": 56
        },
        "provenanceScore": 61,
        "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": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.32.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.0-tiny\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}, \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 20}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-0-tiny"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "lbs-27b",
        "ling-3-1-flash-1019"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "params": "7.9B (1.3B active)",
        "context": 131072,
        "goodFor": "Small, fast hybrid reasoning model for local agents, tool calling and coding on a laptop, with thinking switchable per request.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers",
          "Ollama"
        ],
        "builds": [
          {
            "name": "Ling-3.0-tiny-int4",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 5.8,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "compressed-tensors, 4-bit weights in groups of 32, with attention and dense layers kept at higher precision"
          },
          {
            "name": "Ling-3.0-tiny-Q6_K",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 6.5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-Q5_K_M",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 5.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-Q4_K_M",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 4.8,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-fp8",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 8.4,
            "memoryGB": 12,
            "format": "safetensors"
          },
          {
            "name": "Ling-3.0-tiny-Q8_0",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 8.4,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 15.8,
            "memoryGB": 24,
            "format": "safetensors"
          }
        ],
        "run": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768",
        "runBuild": "Ling-3.0-tiny-Q4_K_M",
        "checked": "2026-11-04",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ling 3",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ling-3.0-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-Fin",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-VL",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL",
            "params": "124B total, 5.5B active"
          },
          {
            "name": "Ling-3.0-flash-dspark",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark",
            "params": "1.36B (draft)"
          },
          {
            "name": "Ling-3.0-flash-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe",
            "params": "5.18M (probe)"
          },
          {
            "name": "Ling-3.0-tiny-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe",
            "params": "3.22M (probe)"
          },
          {
            "name": "Ling-3.1-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash",
            "params": "560B total, 25B active"
          },
          {
            "name": "Ling-3.1-flash-1019",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
            "params": "560B total, 25B active",
            "listing": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019"
          },
          {
            "name": "Ling-3.1-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 17 commits from 10 August to 24 September 2026, mostly README updates, the last adding the training-content summary.",
          "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026, and the inclusionAI .github repository holds only a profile README."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.json",
      "live": {
        "slug": "ling-3-0-tiny",
        "securityTxt": {
          "url": "https://inclusion-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:09:19.556283743Z"
        },
        "domain": {
          "domain": "inclusion-ai.org",
          "registered": "2025-04-03",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/inclusion-ai.org",
          "checkedAt": "2026-11-08T15:01:29.637357411Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:52.43027497Z",
            "changedAt": "2026-11-10T16:22:52.43027497Z",
            "fingerprint": "249f92207430"
          }
        ],
        "updatedAt": "2026-11-10T16:22:52.43027497Z"
      }
    },
    "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk.",
    "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": 30,
        "category": "reach",
        "leads": "ling-3-0-tiny",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it 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": 95,
          "b": 65
        }
      },
      {
        "by": 25,
        "category": "interface",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 tokens native.",
          "b": "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."
        },
        "scores": {
          "a": 60,
          "b": 85
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "ling-3-0-tiny",
        "name": "Risk",
        "reasons": {
          "a": "File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code. Provenance gets 15 for files in inclusionAI's own organisation with a checksum on each. Safety evaluation is 0, since SingProbe's results grade the probe, not the model. Agent guidance gets 5 for the SingProbe guardrail inclusionAI publishes for this model, with no statement of trained refusals. MIT carries no licence deductions (15). No security contact or disclosure policy was found (0).",
          "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": 50,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "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": "MIT",
        "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": "2026-08-31",
        "b": "2026-10-28",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk.",
        "question": "Which is better for AI agents, Ling-3.0-tiny or MiMo-V2.6-Flash-RL?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 65",
          "Risk, 50 against 45"
        ],
        "also": null,
        "goodFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT",
        "slug": "ling-3-0-tiny",
        "watchFor": "No inference provider is listed on the Hub and no inclusionAI API serves it"
      },
      {
        "aheadOn": [
          "Agent interface, 85 against 60"
        ],
        "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": {
      "ling-3-0-tiny": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.2,
          "from": 55,
          "fromGrade": "C",
          "to": 56.2,
          "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-ling-3-0-tiny.json",
        "title": "Agents-A1-4B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "Apertus v1.5 8B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny"
      },
      {
        "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/apodex-1-1-mini-vs-ling-3-0-tiny.json",
        "title": "Apodex-1.1-mini vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny"
      },
      {
        "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/arete-27b-vs-ling-3-0-tiny.json",
        "title": "Arete-27B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny"
      },
      {
        "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/bigbang-v1-vs-ling-3-0-tiny.json",
        "title": "BigBang-v1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny"
      },
      {
        "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/carbon-x-vs-ling-3-0-tiny.json",
        "title": "Carbon-X vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny"
      },
      {
        "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/cola2-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl"
      },
      {
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        "title": "Cosmos-Reason2-2B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny"
      },
      {
        "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/darwin-36b-opus-vs-ling-3-0-tiny.json",
        "title": "Darwin-36B-Opus vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny"
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      {
        "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/deepseek-v3-2-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/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-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny"
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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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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny"
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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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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny"
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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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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny"
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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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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny.json",
        "title": "GLM-5.3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.json",
        "title": "GLM-5.3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-120b vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl.json",
        "title": "gpt-oss-120b vs MiMo-V2.6-Flash-RL",
        "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-20b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-20b vs Ling-3.0-tiny",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.json",
        "title": "gpt-oss-20b vs MiMo-V2.6-Flash-RL",
        "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/granite-4-2-30b-vs-ling-3-0-tiny.json",
        "title": "Granite 4.2 30B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 30B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.json",
        "title": "Granite 4.2 3B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.json",
        "title": "Granite 4.2 3B vs MiMo-V2.6-Flash-RL",
        "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-4-2-8b-vs-ling-3-0-tiny.json",
        "title": "Granite 4.2 8B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ling-3-0-tiny"
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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/hy-memory-30b-a3b-vs-ling-3-0-tiny.json",
        "title": "Hy-Memory-30B-A3B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-0-tiny"
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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/hy3-vs-ling-3-0-tiny.json",
        "title": "Hy3 vs Ling-3.0-tiny",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json",
        "title": "Inkling-Small vs Ling-3.0-tiny",
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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-vs-ling-3-0-tiny.json",
        "title": "Inkling vs Ling-3.0-tiny",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny",
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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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        "title": "Kimi K3.1 Pro vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json",
        "title": "Kimi K3 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.json",
        "title": "Kronos vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Laguna M.1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.json",
        "title": "Laguna S 2.1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl.json",
        "title": "Laguna S 2.1 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny.json",
        "title": "Laguna XS 2.1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny"
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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/laguna-xs-2-1-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.json",
        "title": "LBS-27B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl"
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        "title": "LFM3-1.7B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ling-3-0-tiny.json",
        "title": "LFM3-Audio-2B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-Audio-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl.json",
        "title": "LFM3-Omni-2B vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-0-tiny.json",
        "title": "LFM3-VL-2B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-0-tiny"
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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/ling-3-0-tiny-vs-ling-3-1-flash-1019.json",
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      },
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-0-8b"
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      {
        "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"
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      {
        "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"
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      {
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-4b"
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      {
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b"
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        "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"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-27b"
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        "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",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint"
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      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b.json",
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        "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",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.json",
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        "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",
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        "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",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.json",
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        "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",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "ling-3-0-tiny",
        "key": "reach",
        "ling-3-0-tiny": 95,
        "mimo-v2-6-flash-rl": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "ling-3-0-tiny",
        "key": "risk",
        "ling-3-0-tiny": 50,
        "mimo-v2-6-flash-rl": 45,
        "name": "Risk",
        "weight": 20
      },
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        "by": 25,
        "edge": "mimo-v2-6-flash-rl",
        "key": "interface",
        "ling-3-0-tiny": 60,
        "mimo-v2-6-flash-rl": 85,
        "name": "Agent interface",
        "weight": 10
      },
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        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "ling-3-0-tiny": 56,
        "mimo-v2-6-flash-rl": 59,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ling-3-0-tiny": "A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.",
      "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."
    }
  },
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  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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    "slim": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.min.md"
  },
  "markdown": "MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk. Both do open-weight models you run yourself.\n\n- Ling-3.0-tiny: grade C, 56.2/100, rank #117 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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### Ling-3.0-tiny (C)\n\nGood for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT\n\nAhead on:\n- Reach, 95 against 65\n- Risk, 50 against 45\n\nWatch for: No inference provider is listed on the Hub and no inclusionAI API serves it\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- Agent interface, 85 against 60\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: Ling-3.0-tiny +30\n\n- Ling-3.0-tiny (95): Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added. Runtimes earn the full 25, from released Transformers, vLLM, SGLang and llama.cpp, and Ollama, whose v0.40.14 (2 November 2026) pins llama.cpp b11963 with the bailingmoe3 architecture (Ollama v0.40.7 of 20 October already pinned b11738, which has it). LM Studio has nothing. Builds earn 15 for FP8, INT4 and four GGUF precisions from inclusionAI. MIT with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, because the Hub's model page lists no inference provider and no inclusionAI or Ant Group API serving it 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### Agent interface: MiMo-V2.6-Flash-RL +25\n\n- Ling-3.0-tiny (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format is only in the template and the card names the ling3 parser in commands without describing it, while SGLang uses another parser. Trained tool calling 20, several calls a turn and agentic results on the card. Structured output 0, not documented. Reasoning control 0, thinking turns off per request but the card doesn't say where the reasoning comes back. Context 10, 131,072 tokens native.\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\n### Risk: Ling-3.0-tiny +5\n\n- Ling-3.0-tiny (50): File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code. Provenance gets 15 for files in inclusionAI's own organisation with a checksum on each. Safety evaluation is 0, since SingProbe's results grade the probe, not the model. Agent guidance gets 5 for the SingProbe guardrail inclusionAI publishes for this model, with no statement of trained refusals. MIT carries no licence deductions (15). No security contact or disclosure policy was found (0).\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 | Ling-3.0-tiny | MiMo-V2.6-Flash-RL | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 65 | Ling-3.0-tiny +30 |\n| Risk | 20% (20 this run) | 50 | 45 | Ling-3.0-tiny +5 |\n| Agent interface | 10% (10 this run) | 60 | 85 | MiMo-V2.6-Flash-RL +25 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 59 | MiMo-V2.6-Flash-RL +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.2 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.0-tiny | MiMo-V2.6-Flash-RL |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 | MIT | MIT (metadata only, no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-31 | 2026-10-28 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### Ling-3.0-tiny\n\n- +1.2 on 4 November 2026: correction\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Ling-3.0-tiny.** A small, quick hybrid reasoning model for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with no host. The risks are a card that doesn't match the files in places, a vLLM route running repository code, and no safety results.\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### Ling-3.0-tiny\n\n1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3\n2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code\n3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20\n6. The template keeps earlier reasoning in the prompt, which grows long agent sessions\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches\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, Ling-3.0-tiny or MiMo-V2.6-Flash-RL?\n\nMiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Ling-3.0-tiny's 56.2 (C), and leads in 2 of 4 scored categories. Ling-3.0-tiny leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-0-tiny\", \"b\": \"mimo-v2-6-flash-rl\"}`. From a terminal: `anchor compare ling-3-0-tiny mimo-v2-6-flash-rl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n\n## Other comparisons with Ling-3.0-tiny or MiMo-V2.6-Flash-RL\n\n- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.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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WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.md)\n- [Ling-3.0-tiny vs ZAYA1-8B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-zaya1-8b.md)\n- [Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl.md)\n- [LongCat-2.5 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 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