{
  "data": {
    "a": {
      "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",
      "dns": "mimo-v2-6-distill-qwen-9b.grade.anchorterminal.com",
      "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": "2026-11-03"
      },
      "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": 1518,
        "categoryRank": 37,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. 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 to run at 4-bit through Ollama in about 8 GB.",
        "strengths": [
          "Runs on Qwen3.5-9B's architecture, so released vLLM, SGLang, Transformers and llama.cpp load it, and Ollama's library carries it at 4-bit and 8-bit",
          "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; the smaller builds are Ollama's",
          "No safety results or agent guidance beyond general cautions, 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 no copy of the base's Apache 2.0 text, and the card has no limitations, context, languages or cut-off",
          "The tool format and parser aren't documented on the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input",
          "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",
          "If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "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": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "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\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q8_0 --jinja",
        "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"
      },
      "sameCompany": [
        "mimo-v2-6-flash-rl"
      ],
      "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",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama mimo-v2.6:9b-q4_K_M",
            "url": "https://ollama.com/library/mimo-v2.6:9b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it (the default 9b tag is listed at 6.6 to 8.7 GB across formats), text and image input, 256K context. Ollama also lists 9b-q8_0 (10 GB), 9b-mxfp8 (11 GB), 9b-nvfp4 (8.7 GB) and BF16 GGUF and MLX builds (19 GB)"
          },
          {
            "name": "ggml-org MiMo-V2.6-Distill-Qwen-9B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 10.15,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 9.53 GB of Q8_0 weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "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"
          }
        ],
        "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": "2026-11-03",
        "minMemoryGB": 8
      },
      "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)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl"
          },
          {
            "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": "2026-11-03",
        "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 six commits are the upload on 21 September 2026, a README update on 22 September and two README updates on 28 October.",
          "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": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.json",
      "live": {
        "slug": "mimo-v2-6-distill-qwen-9b",
        "securityTxt": {
          "url": "https://mimo.xiaomi.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:46.192470354Z"
        },
        "domain": {
          "domain": "xiaomi.com",
          "registered": "2003-07-22",
          "source": "https://rdap.verisign.com/com/v1/domain/xiaomi.com",
          "checkedAt": "2026-11-08T15:08:19.964915329Z"
        },
        "updatedAt": "2026-11-10T16:07:46.192470354Z"
      }
    },
    "answer": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface.",
    "b": {
      "slug": "step-3-7-flash",
      "name": "Step 3.7 Flash",
      "vendor": "StepFun",
      "vendorUrl": "https://platform.stepfun.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds.",
      "url": "https://www.anchorterminal.com/tools/step-3-7-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json",
      "dns": "step-3-7-flash.grade.anchorterminal.com",
      "repo": "https://huggingface.co/stepfun-ai/Step-3.7-Flash",
      "license": "Apache-2.0 (metadata and card; no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-FP8"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (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/stepfun-ai/Step-3.7-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-06-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.8,
        "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": 73,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 70,
          "risk": 45,
          "upkeep": 39
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.",
        "bestFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision",
        "strengths": [
          "Apache 2.0, ungated",
          "GGUF, FP8 and NVFP4 builds from StepFun",
          "Four released runtimes carry it, with Step parsers in vLLM and SGLang",
          "A 256K context with images in",
          "Hosted by StepFun and two providers"
        ],
        "weaknesses": [
          "About 131 GB to run at 4 bits with images, over a 128 GB machine's share",
          "Every serve command on the card runs the repository's code",
          "No reply from StepFun on the repository's discussions",
          "Not changed since 3 June 2026",
          "No limitations, safety results or training data on the card"
        ],
        "agentNotes": [
          "On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server",
          "On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`",
          "The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code",
          "For llama.cpp load the mmproj file from StepFun's GGUF repository for image input",
          "StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`",
          "The same model is on our StepFun API listing, with prices and terms"
        ],
        "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": 59.8
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 70,
          "risk": 45,
          "upkeep": 39
        },
        "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 --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code",
        "http": "# SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1\n# (https://api.stepfun.com/v1 for keys issued in China)"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/step-3-7-flash"
      },
      "sameModel": [
        "stepfun-api"
      ],
      "weights": {
        "trainedBy": "StepFun",
        "params": "198B",
        "active": "11B",
        "context": 262144,
        "goodFor": "Agent work that mixes images, search and code, on a GPU server or through StepFun's API. It's a few GB too big for a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Step-3.7-Flash-GGUF IQ4_XS",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 108.96,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "104.99 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash-GGUF Q4_K_S",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "q4_k_s",
            "sizeGB": 115.47,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "111.50 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash-NVFP4",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 129.25,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "StepFun's 4-bit build for NVIDIA GPUs"
          },
          {
            "name": "Step-3.7-Flash-FP8",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 212.53,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "StepFun's FP8 build"
          },
          {
            "name": "Step-3.7-Flash-GGUF Q8_0",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 213.39,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "209.42 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash (BF16 safetensors)",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 402.73,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "26 files"
          }
        ],
        "run": "llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:IQ4_XS --jinja",
        "runBuild": "Step-3.7-Flash-GGUF IQ4_XS",
        "checked": "2026-11-10",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "StepFun",
        "family": "Step 3",
        "url": "https://www.anchorterminal.com/open-weights/#stepfun",
        "others": [
          {
            "name": "Step-3.5-Flash",
            "url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash"
          },
          {
            "name": "Step3-VL-10B",
            "url": "https://huggingface.co/stepfun-ai/Step3-VL-10B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED",
        "domain": "stepfun.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/stepfun-ai/Step-3.7-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "StepFun's legal entity, domain and security.txt are carried from our StepFun API listing.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing.",
          "The changelog is the commit history, 23 commits from 23 May to 3 June 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "risk",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.",
          "b": "File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45."
        },
        "scores": {
          "a": 65,
          "b": 45
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "step-3-7-flash",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.",
          "b": "Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85."
        },
        "scores": {
          "a": 70,
          "b": 85
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.",
          "b": "Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70."
        },
        "scores": {
          "a": 80,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Xiaomi (MiMo team)",
        "b": "StepFun",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
        "b": "Apache-2.0 (metadata and card; no LICENSE file)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-21",
        "b": "2026-06-03",
        "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": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface.",
        "question": "Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or Step 3.7 Flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 70",
          "Risk, 65 against 45"
        ],
        "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 to run at 4-bit through Ollama in about 8 GB.",
        "slug": "mimo-v2-6-distill-qwen-9b",
        "watchFor": "Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's"
      },
      {
        "aheadOn": [
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision",
        "slug": "step-3-7-flash",
        "watchFor": "About 131 GB to run at 4 bits with images, over a 128 GB machine's share"
      }
    ],
    "history": {
      "mimo-v2-6-distill-qwen-9b": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "step-3-7-flash": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-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/agents-a1-4b-vs-step-3-7-flash.json",
        "title": "Agents-A1-4B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash"
      },
      {
        "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/apertus-v1-5-8b-vs-step-3-7-flash.json",
        "title": "Apertus v1.5 8B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash"
      },
      {
        "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/apodex-1-1-mini-vs-step-3-7-flash.json",
        "title": "Apodex-1.1-mini vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Arete-27B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.json",
        "title": "Arete-27B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash"
      },
      {
        "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/bigbang-v1-vs-step-3-7-flash.json",
        "title": "BigBang-v1 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Carbon-X vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.json",
        "title": "Carbon-X vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Cola2 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.json",
        "title": "Cola2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash"
      },
      {
        "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/cosmos-reason2-2b-vs-step-3-7-flash.json",
        "title": "Cosmos-Reason2-2B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash"
      },
      {
        "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/darwin-36b-opus-vs-step-3-7-flash.json",
        "title": "Darwin-36B-Opus vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash"
      },
      {
        "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-v3-2-vs-step-3-7-flash.json",
        "title": "DeepSeek-V3.2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-step-3-7-flash"
      },
      {
        "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-v4-1-flash-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Devstral Small 2 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b"
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      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-distill-qwen-9b"
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      {
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash.json",
        "title": "OTel-2.0-LLM-31B-IT vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-step-3-7-flash.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-step-3-7-flash.json",
        "title": "Qwen3-4B-Instruct-2507 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-step-3-7-flash.json",
        "title": "Qwen3.5-0.8B vs Step 3.7 Flash",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.json",
        "title": "Qwen3.6-27B vs Step 3.7 Flash",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash.json",
        "title": "Qwen3.6-35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json",
        "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.json",
        "title": "Qwen3-VL-8B-Instruct vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.json",
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      {
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.json",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.json",
        "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json",
        "title": "Step 3.7 Flash vs ZAYA1-8B",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "reach",
        "mimo-v2-6-distill-qwen-9b": 80,
        "name": "Reach",
        "step-3-7-flash": 70,
        "weight": 25
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      {
        "by": 20,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "risk",
        "mimo-v2-6-distill-qwen-9b": 65,
        "name": "Risk",
        "step-3-7-flash": 45,
        "weight": 20
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        "by": 15,
        "edge": "step-3-7-flash",
        "key": "interface",
        "mimo-v2-6-distill-qwen-9b": 70,
        "name": "Agent interface",
        "step-3-7-flash": 85,
        "weight": 10
      },
      {
        "by": 1,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "key": "upkeep",
        "mimo-v2-6-distill-qwen-9b": 40,
        "name": "Documentation and upkeep",
        "step-3-7-flash": 39,
        "weight": 10
      }
    ],
    "summary": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.",
      "step-3-7-flash": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description."
    }
  },
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  "links": {
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    "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.min.md"
  },
  "markdown": "MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface. Both do open-weight models you run yourself.\n\n- MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #47 of 160 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- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiMo-V2.6 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 to run at 4-bit through Ollama in about 8 GB.\n\nAhead on:\n- Reach, 80 against 70\n- Risk, 65 against 45\n\nWatch for: Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's\n\n### Step 3.7 Flash (C)\n\nGood for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share\n\n\n## Why the edge\n\n### Risk: MiMo-V2.6 Distill Qwen 9B +20\n\n- MiMo-V2.6 Distill Qwen 9B (65): File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.\n- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n### Agent interface: Step 3.7 Flash +15\n\n- MiMo-V2.6 Distill Qwen 9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.\n- Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Reach: MiMo-V2.6 Distill Qwen 9B +10\n\n- MiMo-V2.6 Distill Qwen 9B (80): Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.\n- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6 Distill Qwen 9B | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 70 | MiMo-V2.6 Distill Qwen 9B +10 |\n| Risk | 20% (20 this run) | 65 | 45 | MiMo-V2.6 Distill Qwen 9B +20 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Step 3.7 Flash +15 |\n| Documentation and upkeep | 10% (10 this run) | 40 | 39 | MiMo-V2.6 Distill Qwen 9B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6 Distill Qwen 9B | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | StepFun |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | Apache-2.0 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-21 | 2026-06-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### MiMo-V2.6 Distill Qwen 9B\n\n- no change on 3 November 2026: product\n\n### Step 3.7 Flash\n\nNo score change since it was listed.\n\n## Verdicts\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. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.\n\n**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.\n\n## Before you call either\n\n### MiMo-V2.6 Distill Qwen 9B\n\n1. Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input\n2. 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\n3. 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`\n4. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read\n5. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95\n6. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them\n7. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer\n8. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens\n9. If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks\n\n### Step 3.7 Flash\n\n1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server\n2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`\n3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code\n4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input\n5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`\n6. The same model is on our StepFun API listing, with prices and terms\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or Step 3.7 Flash?\n\nMiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-distill-qwen-9b\", \"b\": \"step-3-7-flash\"}`. From a terminal: `anchor compare mimo-v2-6-distill-qwen-9b step-3-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n\n## Other comparisons with MiMo-V2.6 Distill Qwen 9B or Step 3.7 Flash\n\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- [Agents-A1-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.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- [Apertus v1.5 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.md)\n- [Apodex-1.1-mini vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Apodex-1.1-mini vs Step 3.7 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