{
  "data": {
    "a": {
      "slug": "deepseek-v4-flash-0731",
      "name": "DeepSeek-V4-Flash-0731",
      "vendor": "DeepSeek",
      "vendorUrl": "https://www.deepseek.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "DeepSeek-V4-Flash-0731 is the 31 July 2026 release of DeepSeek's V4 Flash, a mixture-of-experts text model with 284B parameters, 13B active and a 1M-token context, under MIT. DeepSeek's API retired V4 Flash on 10 September 2026.",
      "url": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731",
      "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json",
      "dns": "deepseek-v4-flash-0731.grade.anchorterminal.com",
      "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "deepseek-ai/DeepSeek-V4-Flash-0731"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's API retired V4 Flash on 10 September 2026 and routes its old names to V4.1 Flash, so no hosted DeepSeek endpoint serves these weights. Running them yourself costs the hardware, 166.9 GB of files (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": "2026-11-02"
      },
      "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "long-context",
        "mit",
        "safetensors",
        "fp8",
        "fp4",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-07-31",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61.9,
        "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": 24,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 66,
          "interface": 75,
          "reach": 70,
          "risk": 50,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though five inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.",
        "bestFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context",
        "strengths": [
          "MIT licence with nothing added, downloadable with no account",
          "1M tokens of context trained in",
          "Runs in vLLM, SGLang, llama.cpp, Transformers and LM Studio from released versions",
          "Parallel tool calls in a documented DSML format, with reasoning off or at three effort levels",
          "Five live inference providers on Hugging Face for agents without the hardware",
          "Safetensors only, with a SHA-256 on every shard"
        ],
        "weaknesses": [
          "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine",
          "No Jinja template, so prompts come from running DeepSeek's encoding code or a runtime's own encoder",
          "No safety evaluation or agent-use guidance published, and only a company-wide security address",
          "DeepSeek hasn't replied to any of the 61 discussions, including reports of tool-call errors",
          "The card lacks intended use, limitations, languages and a knowledge cut-off"
        ],
        "agentNotes": [
          "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)",
          "Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template",
          "Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"",
          "Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max",
          "Without the hardware, call it through a Hugging Face inference provider (DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash",
          "LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.9
          }
        ],
        "editorialScores": {
          "capability": 66,
          "interface": 75,
          "reach": 70,
          "risk": 50,
          "upkeep": 38
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "lastMoved": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# From DeepSeek's card (one 4x GB300 node). Add vLLM's --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 for tool calls\nvllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \\\n  --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \\\n  --data-parallel-size 4 --enable-expert-parallel \\\n  --moe-backend deep_gemm_mega_moe \\\n  --attention-config '{\"use_fp4_indexer_cache\": true}' \\\n  --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}'\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/DeepSeek-V4-Flash-0731-GGUF:MXFP4 --jinja"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/deepseek-v4-flash-0731"
      },
      "sameCompany": [
        "deepseek-api",
        "deepseek-search-api",
        "deepseek-ocr-2",
        "deepseek-v4-1-flash",
        "deepseek-v3-2"
      ],
      "weights": {
        "trainedBy": "DeepSeek",
        "params": "284B",
        "active": "13B",
        "context": 1048576,
        "goodFor": "Text model for agents and long documents, with thinking or chat mode, reasoning effort and tool calls in DSML tags.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ggml-org DeepSeek-V4-Flash-0731-GGUF (MXFP4)",
            "url": "https://huggingface.co/ggml-org/DeepSeek-V4-Flash-0731-GGUF",
            "who": "llama.cpp",
            "precision": "mxfp4",
            "sizeGB": 154.99,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 154.99 GB of MXFP4 weights, from the Hub's model information on 10 November 2026. The 2-bit files (Q2_K 117.3 GB, Q2_K_S 98.6 GB) are below the 4-bit line and aren't listed. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "DeepSeek-V4-Flash-0731",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731",
            "who": "publisher",
            "precision": "fp8+fp4",
            "sizeGB": 166.9,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "MoE experts in FP4, most other weights FP8; the only build DeepSeek publishes"
          }
        ],
        "run": "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4",
        "runBuild": "DeepSeek-V4-Flash-0731",
        "checked": "2026-10-21",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "DeepSeek",
        "family": "DeepSeek V4",
        "url": "https://www.anchorterminal.com/open-weights/#deepseek",
        "others": [
          {
            "name": "DeepSeek-V4-Flash-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base",
            "params": "284B total, 13B activated"
          },
          {
            "name": "DeepSeek-V4-Flash",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash",
            "params": "284B total, 13B activated"
          },
          {
            "name": "DeepSeek-V4-Flash-DSpark",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark",
            "params": "284B total, 13B activated"
          },
          {
            "name": "DeepSeek-V4-Flash-Vision-Exp",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
            "params": "not stated on card"
          },
          {
            "name": "DeepSeek-V4-Pro-Base",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base",
            "params": "1.6T total, 49B activated"
          },
          {
            "name": "DeepSeek-V4-Pro",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro",
            "params": "1.6T total, 49B activated"
          },
          {
            "name": "DeepSeek-V4-Pro-DSpark",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark",
            "params": "1.6T total, 49B activated"
          },
          {
            "name": "DeepSeek-V4-Pro-0813",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813",
            "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)"
          },
          {
            "name": "DeepSeek-V4.1-Flash",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash",
            "params": "552B backbone, 8B activated in prefill / 16B in decode",
            "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash"
          },
          {
            "name": "DeepSeek-V4.1-Flash-1017",
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017",
            "params": "552B backbone, 8B / 16B activated (V4.1-Flash figures; not restated)",
            "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash"
          }
        ],
        "olderCount": 72,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)",
        "domain": "deepseek.com",
        "domainRegistered": "2000-08-13",
        "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, four commits from 31 July to 1 August 2026 (the release and a card edit adding the SGLang cookbook).",
          "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.",
          "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json",
      "live": {
        "slug": "deepseek-v4-flash-0731",
        "securityTxt": {
          "url": "https://deepseek.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:51.938889615Z"
        },
        "domain": {
          "domain": "deepseek.com",
          "registered": "2000-08-13",
          "source": "https://rdap.verisign.com/com/v1/domain/deepseek.com",
          "checkedAt": "2026-11-08T14:52:49.217816183Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:28.560002209Z",
            "changedAt": "2026-11-10T16:22:28.560002209Z",
            "fingerprint": "d68af396a249"
          }
        ],
        "updatedAt": "2026-11-10T16:22:28.560002209Z"
      }
    },
    "answer": "DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.9 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on agent interface and 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": 21,
        "category": "upkeep",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, the card and the DSpark card it names give architecture, parameters and context, with intended use, limitations, languages and cut-off missing. Training data 5, a general description in the report with no cut-off. Last change 10, a card edit on 1 August 2026, 93 days before 2 November, so past 90 and within 180; deepseek-recipe 0.2.0 (20 October) stays read as a library-wide release, not a change to this model's files. Responsiveness 0, DeepSeek answered none of the 61 discussions, the newest opened on 17 October. Version policy 10, the preview and DSpark copies keep their own repositories and the dated 0731 release says what it supersedes. File health 5, the files agree with each other, but reports of tool-call errors and looping and a pull request for a missing chat template wait without a reply from DeepSeek.",
          "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": 38,
          "b": 59
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "mimo-v2-6-flash-rl",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 10, there is no Jinja template and the format is given as Python to run. Tool-calling format 10, DeepSeek documents how tools are declared and returned and ships its own parser, but only vLLM's recipe names a runtime parser, so the 25 line isn't met. Trained tool calling 20, several invoke elements in one call block. Structured output 15, DeepSeek's deepseek-recipe library documents JSON Schema response formats for V4 prompts. Reasoning control 10, chat mode with no thinking and three effort levels in thinking mode, with reasoning returned as reasoning_content. Context 10, 1M tokens trained.",
          "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": 75,
          "b": 85
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "deepseek-v4-flash-0731",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5, because the smallest build of 4-bit or better from a runtime's own library is LM Studio's MXFP4 GGUF at 156.4 GB, which with a fifth added needs 187.7 GB, more than our 107.4 GB machine and within 256 GB. DeepSeek's own release, 166.9 GB, sits in the same band. Runtimes 25, the cap, from vLLM v0.32.0, SGLang v0.5.22, llama.cpp release b11771, Transformers 5.20.0 and LM Studio's library, with Ollama not counted since its library entry is retired with no models pushed. Builds 10, DeepSeek ships one build, the FP8 and FP4 release itself. Terms 15, MIT with nothing added. Download 10, not gated. Hosted 5, DeepSeek's API moved to V4.1 Flash, but five inference providers on Hugging Face serve this repository live (Together's mapping has gone since 21 October).",
          "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": 70,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "deepseek-v4-flash-0731",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands pass --trust-remote-code. Provenance 15, the deepseek-ai organisation and a SHA-256 on every shard. Safety evaluation 0, none published. Agent guidance 0, the encoding README's notes about the parser and the reserved developer role are usage notes, read alike for all three DeepSeek listings. Licence risk 15, MIT with no deductions. Security programme 5, for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories.",
          "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": "DeepSeek",
        "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": "2026-07-31",
        "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": "2.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.9 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, DeepSeek-V4-Flash-0731 or MiMo-V2.6-Flash-RL?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 70 against 65",
          "Risk, 50 against 45"
        ],
        "also": null,
        "goodFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context",
        "slug": "deepseek-v4-flash-0731",
        "watchFor": "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine"
      },
      {
        "aheadOn": [
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 59 against 38"
        ],
        "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": {
      "deepseek-v4-flash-0731": [
        {
          "causes": [
            "time"
          ],
          "date": "2026-11-02",
          "delta": -0.5,
          "from": 62.4,
          "fromGrade": "B",
          "to": 61.9,
          "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-deepseek-v4-flash-0731.json",
        "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731"
      },
      {
        "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-deepseek-v4-flash-0731.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731"
      },
      {
        "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-deepseek-v4-flash-0731.json",
        "title": "Apodex-1.1-mini vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.json",
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        "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-deepseek-v4-flash-0731.json",
        "title": "Arete-27B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl"
      },
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731"
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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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.json",
        "title": "Carbon-X vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731"
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        "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-deepseek-v4-flash-0731.json",
        "title": "Cola2 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-deepseek-v4-flash-0731"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json",
        "title": "Cola2 vs MiMo-V2.6-Flash-RL",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731",
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m3-1-flash.json",
        "title": "MiMo-V2.6-Flash-RL vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m3-1-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4.json",
        "title": "MiMo-V2.6-Flash-RL vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-muse-glimmer-30b.json",
        "title": "MiMo-V2.6-Flash-RL vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-30b-a3b.json",
        "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"
      },
      {
        "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",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-9b"
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      {
        "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"
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        "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"
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      {
        "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"
      },
      {
        "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"
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      {
        "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"
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        "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",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "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"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "deepseek-v4-flash-0731": 70,
        "edge": "deepseek-v4-flash-0731",
        "key": "reach",
        "mimo-v2-6-flash-rl": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "deepseek-v4-flash-0731": 50,
        "edge": "deepseek-v4-flash-0731",
        "key": "risk",
        "mimo-v2-6-flash-rl": 45,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "deepseek-v4-flash-0731": 75,
        "edge": "mimo-v2-6-flash-rl",
        "key": "interface",
        "mimo-v2-6-flash-rl": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 21,
        "deepseek-v4-flash-0731": 38,
        "edge": "mimo-v2-6-flash-rl",
        "key": "upkeep",
        "mimo-v2-6-flash-rl": 59,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.9 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "deepseek-v4-flash-0731": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though five inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.",
      "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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  "markdown": "DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.9 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade C, 61.9/100, rank #33 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.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### DeepSeek-V4-Flash-0731 (C)\n\nGood for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context\n\nAhead on:\n- Reach, 70 against 65\n- Risk, 50 against 45\n\nWatch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine\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 75\n- Documentation and upkeep, 59 against 38\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n\n## Why the edge\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +21\n\n- DeepSeek-V4-Flash-0731 (38): Model card 8, the card and the DSpark card it names give architecture, parameters and context, with intended use, limitations, languages and cut-off missing. Training data 5, a general description in the report with no cut-off. Last change 10, a card edit on 1 August 2026, 93 days before 2 November, so past 90 and within 180; deepseek-recipe 0.2.0 (20 October) stays read as a library-wide release, not a change to this model's files. Responsiveness 0, DeepSeek answered none of the 61 discussions, the newest opened on 17 October. Version policy 10, the preview and DSpark copies keep their own repositories and the dated 0731 release says what it supersedes. File health 5, the files agree with each other, but reports of tool-call errors and looping and a pull request for a missing chat template wait without a reply from DeepSeek.\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### Agent interface: MiMo-V2.6-Flash-RL +10\n\n- DeepSeek-V4-Flash-0731 (75): Chat template 10, there is no Jinja template and the format is given as Python to run. Tool-calling format 10, DeepSeek documents how tools are declared and returned and ships its own parser, but only vLLM's recipe names a runtime parser, so the 25 line isn't met. Trained tool calling 20, several invoke elements in one call block. Structured output 15, DeepSeek's deepseek-recipe library documents JSON Schema response formats for V4 prompts. Reasoning control 10, chat mode with no thinking and three effort levels in thinking mode, with reasoning returned as reasoning_content. Context 10, 1M tokens trained.\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### Reach: DeepSeek-V4-Flash-0731 +5\n\n- DeepSeek-V4-Flash-0731 (70): Memory 5, because the smallest build of 4-bit or better from a runtime's own library is LM Studio's MXFP4 GGUF at 156.4 GB, which with a fifth added needs 187.7 GB, more than our 107.4 GB machine and within 256 GB. DeepSeek's own release, 166.9 GB, sits in the same band. Runtimes 25, the cap, from vLLM v0.32.0, SGLang v0.5.22, llama.cpp release b11771, Transformers 5.20.0 and LM Studio's library, with Ollama not counted since its library entry is retired with no models pushed. Builds 10, DeepSeek ships one build, the FP8 and FP4 release itself. Terms 15, MIT with nothing added. Download 10, not gated. Hosted 5, DeepSeek's API moved to V4.1 Flash, but five inference providers on Hugging Face serve this repository live (Together's mapping has gone since 21 October).\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### Risk: DeepSeek-V4-Flash-0731 +5\n\n- DeepSeek-V4-Flash-0731 (50): File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands pass --trust-remote-code. Provenance 15, the deepseek-ai organisation and a SHA-256 on every shard. Safety evaluation 0, none published. Agent guidance 0, the encoding README's notes about the parser and the reserved developer role are usage notes, read alike for all three DeepSeek listings. Licence risk 15, MIT with no deductions. Security programme 5, for the security@deepseek.com reporting address in DeepSeek's site footer, read as for DeepSeek-V3.2, with no security.txt and no public advisories.\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 | DeepSeek-V4-Flash-0731 | MiMo-V2.6-Flash-RL | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 65 | DeepSeek-V4-Flash-0731 +5 |\n| Risk | 20% (20 this run) | 50 | 45 | DeepSeek-V4-Flash-0731 +5 |\n| Agent interface | 10% (10 this run) | 75 | 85 | MiMo-V2.6-Flash-RL +10 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 59 | MiMo-V2.6-Flash-RL +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.9 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | MiMo-V2.6-Flash-RL |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | 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 | 2026-07-31 | 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 | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### DeepSeek-V4-Flash-0731\n\n- -0.5 on 2 November 2026 (B to C): time\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n## Verdicts\n\n**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though five inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.\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### DeepSeek-V4-Flash-0731\n\n1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)\n2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template\n3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"\n4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max\n5. Without the hardware, call it through a Hugging Face inference provider (DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash\n6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory\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, DeepSeek-V4-Flash-0731 or MiMo-V2.6-Flash-RL?\n\nDeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61.9 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"mimo-v2-6-flash-rl\"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 mimo-v2-6-flash-rl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n\n## Other comparisons with DeepSeek-V4-Flash-0731 or MiMo-V2.6-Flash-RL\n\n- [Agents-A1-4B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.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 DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.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 DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731.md)\n- [Apodex-1.1-mini vs 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        "name": "DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash-RL",
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    "description": "DeepSeek-V4-Flash-0731 and MiMo-V2.6-Flash-RL score within a point of each other for open-weight models you run yourself, 61.9 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash-RL for AI agents (2026)",
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    "updated": "2026-11-10",
    "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl"
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