{
  "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",
      "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": "2026-10-21"
      },
      "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": 62.4,
        "grade": "B",
        "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": 1197,
        "categoryRank": 23,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 66,
          "interface": 75,
          "reach": 70,
          "risk": 50,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "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 six 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",
          "Six 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 (Together, 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": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 62.4
          }
        ],
        "editorialScores": {
          "capability": 66,
          "interface": 75,
          "reach": 70,
          "risk": 50,
          "upkeep": 43
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "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\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/deepseek-v4-flash-0731"
      },
      "sameCompany": [
        "deepseek-api",
        "deepseek-search-api",
        "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": "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": 256
      },
      "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": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json"
    },
    "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on risk.",
    "b": {
      "slug": "mimo-v2-6-distill-qwen-9b",
      "name": "MiMo-V2.6 Distill Qwen 9B",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6 Distill Qwen 9B is Xiaomi's open-weight 9B agentic model, Qwen3.5-9B fine-tuned on data generated by MiMo-V2.6 for coding, agent, visual coding and cybersecurity tasks, released in BF16 under MIT as a starting point for RL research.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "license": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT on the card and Apache 2.0 for the Qwen3.5-9B base. Xiaomi's MiMo API serves the larger MiMo-V2.6 models, not this one. Running it yourself costs the hardware, 18.8 GB for the BF16 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": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "distillation",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-09-21",
      "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": 1197,
        "categoryRank": 34,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 70,
          "risk": 60,
          "upkeep": 40
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.",
        "bestFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger.",
        "strengths": [
          "Runs on Qwen3.5-9B's architecture, so released vLLM, SGLang, Transformers and llama.cpp all load it",
          "MIT over an Apache 2.0 base, no gate, no account and no use policy",
          "Safetensors only in Xiaomi's own organisation, with a checksum on every file",
          "The fine-tuning mix is published by domain with token counts, and a technical report covers it",
          "Several tool calls a turn, thinking switched per request and 262,144 tokens of context from the base"
        ],
        "weaknesses": [
          "Only BF16 from Xiaomi, about 23 GB to run",
          "No safety results or agent guidance, with 11.0B tokens of offensive-security tuning",
          "No reply from Xiaomi to any of its 14 discussions, including template and tool-call reports",
          "No LICENSE file, and the card has no limitations, context, languages or cut-off",
          "The tool format and parser aren't documented on the card"
        ],
        "agentNotes": [
          "Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls",
          "SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`",
          "Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read",
          "The generation config samples at temperature 0.6, top_k 20 and top_p 0.95",
          "Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them",
          "The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer",
          "It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 70,
          "risk": 60,
          "upkeep": 40
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \\\n  --reasoning-parser mimo --tool-call-parser mimo --host 0.0.0.0 --port 30000",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-distill-qwen-9b"
      },
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "builtOn": "Qwen/Qwen3.5-9B (supervised fine-tune on data generated by MiMo-V2.6; base Apache 2.0, declared in the card's metadata)",
        "params": "9.41B",
        "context": 262144,
        "goodFor": "A 9B agentic model for coding, terminal and tool tasks, with images in, meant as a starting point for RL fine-tuning.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.85,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "Xiaomi's only build. Ollama's library and LM Studio's catalogue have no entry for it"
          }
        ],
        "run": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo --tool-call-parser mimo --context-length 131072 --port 30000",
        "runBuild": "MiMo-V2.6-Distill-Qwen-9B",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-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-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "params": "310.76B (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,
        "unrestrictedCopies": 5
      },
      "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": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation XiaomiMiMo isn't verified. mimo.xiaomi.com links to it, and the MiMo-V2.6 technical report links this repository.",
          "Nothing is hosted for this model, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The four commits are the upload on 21 September 2026 and a README update on 22 September.",
          "mimo.xiaomi.com has no security.txt (404) and www.mi.com's returns 403. Xiaomi runs a company security centre at sec.xiaomi.com, which no MiMo page links."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.json"
    },
    "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); built on Qwen3.5-9B under Apache 2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-31",
        "b": "2026-09-21",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on risk.",
        "question": "Which is better for AI agents, DeepSeek-V4-Flash-0731 or MiMo-V2.6 Distill Qwen 9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 75 against 70"
        ],
        "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": [
          "Risk, 60 against 50"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger.",
        "slug": "mimo-v2-6-distill-qwen-9b",
        "watchFor": "Only BF16 from Xiaomi, about 23 GB to run"
      }
    ],
    "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-distill-qwen-9b.json",
        "title": "Agents-A1-4B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "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-distill-qwen-9b.json",
        "title": "Apertus v1.5 8B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Apodex-1.1-mini vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731.json",
        "title": "BigBang-v1 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "BigBang-v1 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Cosmos-Reason2-2B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.json",
        "title": "Darwin-36B-Opus vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.json",
        "title": "DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.json",
        "title": "DeepSeek V4.1 Flash vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-diffusiongemma-26b-a4b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-120b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-120b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling.json",
        "title": "DeepSeek-V4-Flash-0731 vs Inkling",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling-small.json",
        "title": "DeepSeek-V4-Flash-0731 vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.json",
        "title": "DeepSeek-V4-Flash-0731 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1"
      },
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-4b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-ultra-550b-a55b"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b"
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    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 0,
        "deepseek-v4-flash-0731": 70,
        "edge": "",
        "key": "reach",
        "mimo-v2-6-distill-qwen-9b": 70,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "deepseek-v4-flash-0731": 50,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "risk",
        "mimo-v2-6-distill-qwen-9b": 60,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "deepseek-v4-flash-0731": 75,
        "edge": "deepseek-v4-flash-0731",
        "key": "interface",
        "mimo-v2-6-distill-qwen-9b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 3,
        "deepseek-v4-flash-0731": 43,
        "edge": "deepseek-v4-flash-0731",
        "key": "upkeep",
        "mimo-v2-6-distill-qwen-9b": 40,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on risk. Both do open-weight models you run yourself.",
    "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 six 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-distill-qwen-9b": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports."
    }
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  "kind": "anchor.page",
  "links": {
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    "html": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b",
    "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.md",
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  "markdown": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on risk. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 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 Distill Qwen 9B: grade C, 61/100, rank #41 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek-V4-Flash-0731 (B)\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- Agent interface, 75 against 70\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 Distill Qwen 9B (C)\n\nGood for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger.\n\nAhead on:\n- Risk, 60 against 50\n\nWatch for: Only BF16 from Xiaomi, about 23 GB to run\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | MiMo-V2.6 Distill Qwen 9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 70 | even |\n| Risk | 20% (20 this run) | 50 | 60 | MiMo-V2.6 Distill Qwen 9B +10 |\n| Agent interface | 10% (10 this run) | 75 | 70 | DeepSeek-V4-Flash-0731 +5 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 40 | DeepSeek-V4-Flash-0731 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | MiMo-V2.6 Distill Qwen 9B |\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); built on Qwen3.5-9B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-09-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.5/5 (4) | 2.3/5 (4) |\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 six 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 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.\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 (Together, 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 Distill Qwen 9B\n\n1. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls\n2. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`\n3. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read\n4. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95\n5. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them\n6. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer\n7. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V4-Flash-0731 or MiMo-V2.6 Distill Qwen 9B?\n\nDeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against MiMo-V2.6 Distill Qwen 9B's 61 (C), and leads in 2 of 4 scored categories. MiMo-V2.6 Distill Qwen 9B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"mimo-v2-6-distill-qwen-9b\"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 mimo-v2-6-distill-qwen-9b`\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-distill-qwen-9b.json\n\n## Other comparisons with DeepSeek-V4-Flash-0731 or MiMo-V2.6 Distill Qwen 9B\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 Distill Qwen 9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b.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 Distill Qwen 9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Apodex-1.1-mini vs 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Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-next.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-2b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-30b-a3b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-8b-instruct.md)\n- 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