{
  "data": {
    "a": {
      "slug": "qwen3-5-122b-a10b",
      "name": "Qwen3.5-122B-A10B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3.5-122B-A10B is Alibaba's mid-size open-weight mixture-of-experts vision-language model, 122B parameters with 10B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json",
      "dns": "qwen3-5-122b-a10b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-122B-A10B"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-122B-A10B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-122B-A10B-GPTQ-Int4"
        },
        {
          "registry": "ollama",
          "name": "qwen3.5:122b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Alibaba Cloud Model Studio and several inference providers also serve it per token. Running it yourself costs the hardware, 250.2 GB for the BF16 files, 78.9 GB for Alibaba's GPTQ 4-bit build and 81 GB for Ollama's build (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/Qwen/Qwen3.5-122B-A10B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-02-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63.1,
        "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": 1350,
        "categoryRank": 19,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 62,
          "interface": 70,
          "reach": 80,
          "risk": 55,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.",
        "bestFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Alibaba's own FP8 and GPTQ 4-bit builds, the 4-bit one about 95 GB in use",
          "Plain Apache 2.0 with no gate, no account and no use policy",
          "Safetensors only, with a checksum on every file in the publisher's own organisation",
          "262,144 tokens native, several tool calls per turn and thinking that turns off per request",
          "Served by Alibaba's Model Studio and at least four other providers"
        ],
        "weaknesses": [
          "Too big for a single 64 GB machine even at 4 bits",
          "No safety results, agent-use guidance or reporting route for flaws",
          "No reply from Qwen to any of the 21 discussions, and a template fix waits since April",
          "Tool format is only in the template and structured output isn't documented",
          "No limitations, knowledge cut-off or training data sources on the card"
        ],
        "agentNotes": [
          "Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)",
          "Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151",
          "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do",
          "Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported",
          "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without",
          "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens",
          "Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text",
          "Validate any JSON it returns, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 63.1
          }
        ],
        "editorialScores": {
          "capability": 62,
          "interface": 70,
          "reach": 80,
          "risk": 55,
          "upkeep": 34
        },
        "provenanceScore": 54,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "lastMoved": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run qwen3.5:122b\n# or, with the card's vLLM command for Alibaba's 4-bit build:\nvllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3.5:122b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-5-122b-a10b"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "params": "122B total, 10B active",
        "context": 262144,
        "goodFor": "Mid-size mixture-of-experts vision-language model for coding, agents and tool calling, text, images and video in, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3.5-122B-A10B-GPTQ-Int4",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-GPTQ-Int4",
            "who": "publisher",
            "precision": "gptq-int4",
            "sizeGB": 78.9,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang. Running this kernel on ROCm gfx1151 isn't documented"
          },
          {
            "name": "lmstudio-community Qwen3.5-122B-A10B-GGUF Q4_K_M",
            "url": "https://huggingface.co/lmstudio-community/Qwen3.5-122B-A10B-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 75.1,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "74.2 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list. LM Studio's own catalogue page for this size wasn't found, so it's recorded but not used for Reach"
          },
          {
            "name": "ollama qwen3.5:122b",
            "url": "https://ollama.com/library/qwen3.5:122b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 81,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with image input (digest 8b9d11d807c5), the same build as the 122b-a10b and 122b-a10b-q4_K_M tags and the only 122B tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "Qwen3.5-122B-A10B-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 127.2,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "ollama run qwen3.5:122b",
        "runBuild": "ollama qwen3.5:122b",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen3",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "others": [
          {
            "name": "Qwen3-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B",
            "url": "https://huggingface.co/Qwen/Qwen3-14B",
            "params": "14B"
          },
          {
            "name": "Qwen3-235B-A22B",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-32B",
            "url": "https://huggingface.co/Qwen/Qwen3-32B",
            "params": "32B"
          },
          {
            "name": "Qwen3-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-14B-Base",
            "params": "14B"
          },
          {
            "name": "Qwen3-30B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-8B-Base",
            "params": "8B"
          },
          {
            "name": "Qwen3-Reranker-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-Embedding-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b"
          },
          {
            "name": "Qwen3-Embedding-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b"
          },
          {
            "name": "Qwen3-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b"
          },
          {
            "name": "Qwen3-Reranker-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-235B-A22B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-Coder-480B-A35B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct",
            "params": "480B-A35B"
          },
          {
            "name": "Qwen3-235B-A22B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507"
          },
          {
            "name": "Qwen3-30B-A3B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Coder-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-4B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507"
          },
          {
            "name": "Qwen3-4B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507",
            "params": "4B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Captioner",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3Guard-Gen-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Gen-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Gen-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3Guard-Stream-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Stream-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Stream-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-4B-SafeRL",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-VL-4B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct"
          },
          {
            "name": "Qwen3-VL-4B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-8B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct"
          },
          {
            "name": "Qwen3-VL-8B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking",
            "params": "8B"
          },
          {
            "name": "Qwen3-VL-2B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct"
          },
          {
            "name": "Qwen3-VL-2B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-32B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct",
            "params": "32B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct"
          },
          {
            "name": "Qwen3-VL-32B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking",
            "params": "32B"
          },
          {
            "name": "Qwen3-VL-Embedding-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b"
          },
          {
            "name": "Qwen3-VL-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b"
          },
          {
            "name": "Qwen3-VL-Reranker-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-Tokenizer-12Hz",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz",
            "params": "not in name"
          },
          {
            "name": "Qwen3-ASR-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b"
          },
          {
            "name": "Qwen3-ASR-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b"
          },
          {
            "name": "Qwen3-ForcedAligner-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Coder-Next",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
            "params": "not in name; Hub safetensors total 79.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next"
          },
          {
            "name": "Qwen3-Coder-Next-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base",
            "params": "not in name; Hub safetensors total 79.7B"
          },
          {
            "name": "WebWorld-14B",
            "url": "https://huggingface.co/Qwen/WebWorld-14B",
            "params": "14B"
          },
          {
            "name": "WebWorld-32B",
            "url": "https://huggingface.co/Qwen/WebWorld-32B",
            "params": "32B"
          },
          {
            "name": "WebWorld-8B",
            "url": "https://huggingface.co/Qwen/WebWorld-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3.5-397B-A17B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
            "params": "397B-A17B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b"
          },
          {
            "name": "Qwen3.5-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-27B",
            "params": "27B"
          },
          {
            "name": "Qwen3.5-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-35B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-9B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base",
            "params": "9B"
          },
          {
            "name": "Qwen3.5-4B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b"
          },
          {
            "name": "Qwen3.5-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "params": "9B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b"
          },
          {
            "name": "Qwen3.5-0.8B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B",
            "params": "0.8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b"
          },
          {
            "name": "Qwen3.5-0.8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base",
            "params": "0.8B"
          },
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b"
          },
          {
            "name": "Qwen3.5-2B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The commit history's messages are uploads and README updates, the last on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 24 February 2026.",
          "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.json",
      "live": {
        "slug": "qwen3-5-122b-a10b",
        "securityTxt": {
          "url": "https://qwen.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-04T15:54:26.521321366Z"
        },
        "domain": {
          "domain": "qwen.ai",
          "registered": "2023-08-27",
          "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai",
          "checkedAt": "2026-11-01T14:24:05.748198353Z"
        },
        "updatedAt": "2026-11-04T15:54:26.521321366Z"
      }
    },
    "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.",
    "b": {
      "slug": "ripple-65b-preview",
      "name": "Ripple-65B Preview",
      "vendor": "Ricursive Intelligence",
      "vendorUrl": "https://www.ricursive.com/ripple",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.",
      "url": "https://www.anchorterminal.com/tools/ripple-65b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json",
      "dns": "ripple-65b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview"
        },
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).",
      "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 2026-11-04).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "tool-calling",
        "coding",
        "apache-2.0",
        "safetensors",
        "remote-code",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.3,
        "grade": "D",
        "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": 1350,
        "categoryRank": 78,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.",
        "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to",
          "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately",
          "Trained for long context at 2M-token sequences, with 1M stated",
          "Base model and a speculative-decoding draft published beside the instruct model",
          "Safetensors only, with a checksum on every file"
        ],
        "weaknesses": [
          "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code",
          "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF",
          "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run",
          "Thinking can't be turned off or budgeted",
          "No safety guidance for agent use, no security contact and no knowledge cut-off"
        ],
        "agentNotes": [
          "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default",
          "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags",
          "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields",
          "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on",
          "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.3
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "provenanceScore": 36,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n  --model-path ../.. \\\n  --speculative-draft-model-path ../../../ripple-dflash2",
        "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ripple-65b-preview"
      },
      "weights": {
        "trainedBy": "Ricursive Intelligence",
        "params": "65B",
        "context": 1048576,
        "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ripple-65B-Preview-NVFP4",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 57.2,
            "memoryGB": 69,
            "format": "safetensors",
            "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day"
          },
          {
            "name": "Ripple-65B-Preview",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 129.6,
            "memoryGB": 160,
            "format": "safetensors",
            "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs"
          }
        ],
        "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2",
        "runBuild": "Ripple-65B-Preview",
        "checked": "2026-11-04",
        "minMemoryGB": 69
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ricursive Intelligence, Inc.",
        "domain": "ricursive.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-04",
        "notes": [
          "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.",
          "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.",
          "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.",
          "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.",
          "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one."
        ],
        "score": 36
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "qwen3-5-122b-a10b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5.",
          "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read."
        },
        "scores": {
          "a": 80,
          "b": 45
        }
      },
      {
        "by": 15,
        "category": "upkeep",
        "leads": "ripple-65b-preview",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.",
          "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M."
        },
        "scores": {
          "a": 34,
          "b": 49
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "ripple-65b-preview",
        "name": "Agent interface",
        "reasons": {
          "a": "The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.",
          "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M."
        },
        "scores": {
          "a": 70,
          "b": 75
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Alibaba (Qwen team)",
        "b": "Ricursive Intelligence",
        "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": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-02-24",
        "b": "2026-11-04",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Qwen3.5-122B-A10B or Ripple-65B Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 45"
        ],
        "also": null,
        "goodFor": "Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.",
        "slug": "qwen3-5-122b-a10b",
        "watchFor": "Too big for a single 64 GB machine even at 4 bits"
      },
      {
        "aheadOn": [
          "Agent interface, 75 against 70",
          "Documentation and upkeep, 49 against 34"
        ],
        "also": null,
        "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "slug": "ripple-65b-preview",
        "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code"
      }
    ],
    "history": {
      "qwen3-5-122b-a10b": [
        {
          "causes": [
            "time"
          ],
          "date": "2026-11-02",
          "delta": -0.5,
          "from": 63.6,
          "fromGrade": "B",
          "to": 63.1,
          "toGrade": "B"
        }
      ],
      "ripple-65b-preview": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.json",
        "title": "Agents-A1-4B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.json",
        "title": "Apertus v1.5 8B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b"
      },
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.json",
        "title": "Apertus v1.5 8B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.json",
        "title": "Apodex-1.1-mini vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b"
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        "title": "BigBang-v1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-122b-a10b.json",
        "title": "Carbon-X vs Qwen3.5-122B-A10B",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-122b-a10b.json",
        "title": "Cosmos-Reason2-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-122b-a10b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview"
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      {
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        "title": "Kronos vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.json",
        "title": "Kronos vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.json",
        "title": "Laguna M.1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.json",
        "title": "Laguna M.1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-122b-a10b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.json",
        "title": "Laguna S 2.1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-122b-a10b.json",
        "title": "Laguna XS 2.1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ripple-65b-preview.json",
        "title": "Laguna XS 2.1 vs Ripple-65B Preview",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ripple-65b-preview.json",
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        "title": "Ling-3.0-tiny vs Qwen3.5-122B-A10B",
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      {
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        "title": "Ling-3.0-tiny vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview"
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        "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B",
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM5-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-122b-a10b"
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        "title": "MiniMax-M2.5 vs Qwen3.5-122B-A10B",
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        "title": "MiniMax-M2.7 vs Qwen3.5-122B-A10B",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-2b-instruct"
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      {
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        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint.json",
        "title": "Qwen3.5-122B-A10B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.json",
        "title": "Qwen3.5-122B-A10B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-122B-A10B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-2B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-397B-A17B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-4B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-9B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.json",
        "title": "Qwen3.6-27B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.json",
        "title": "Qwen3.6-35B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.json",
        "title": "Qwen3.8-27B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.json",
        "title": "Qwen3.8-Flash-Next vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.json",
        "title": "Qwen3-Coder-Next vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-2B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json",
        "title": "Recursive Flint vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json",
        "title": "Ripple-65B Preview vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json",
        "title": "Ripple-65B Preview vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json",
        "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "qwen3-5-122b-a10b",
        "key": "reach",
        "name": "Reach",
        "qwen3-5-122b-a10b": 80,
        "ripple-65b-preview": 45,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "qwen3-5-122b-a10b": 55,
        "ripple-65b-preview": 55,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "ripple-65b-preview",
        "key": "interface",
        "name": "Agent interface",
        "qwen3-5-122b-a10b": 70,
        "ripple-65b-preview": 75,
        "weight": 10
      },
      {
        "by": 15,
        "edge": "ripple-65b-preview",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-5-122b-a10b": 34,
        "ripple-65b-preview": 49,
        "weight": 10
      }
    ],
    "summary": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "qwen3-5-122b-a10b": "A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.",
      "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview",
    "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.min.md"
  },
  "markdown": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Qwen3.5-122B-A10B: grade B, 63.1/100, rank #25 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.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 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Qwen3.5-122B-A10B (B)\n\nGood for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.\n\nAhead on:\n- Reach, 80 against 45\n\nWatch for: Too big for a single 64 GB machine even at 4 bits\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nAhead on:\n- Agent interface, 75 against 70\n- Documentation and upkeep, 49 against 34\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Reach: Qwen3.5-122B-A10B +35\n\n- Qwen3.5-122B-A10B (80): Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5.\n- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Documentation and upkeep: Ripple-65B Preview +15\n\n- Qwen3.5-122B-A10B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.\n- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Agent interface: Ripple-65B Preview +5\n\n- Qwen3.5-122B-A10B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n## Score by category\n\n| Category | Weight | Qwen3.5-122B-A10B | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 45 | Qwen3.5-122B-A10B +35 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 75 | Ripple-65B Preview +5 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 49 | Ripple-65B Preview +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.1 · B** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Qwen3.5-122B-A10B | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | Ricursive Intelligence |\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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-02-24 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Qwen3.5-122B-A10B\n\n- -0.5 on 2 November 2026: time\n\n### Ripple-65B Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.\n\n**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\n\n### Qwen3.5-122B-A10B\n\n1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)\n2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do\n4. Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported\n5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text\n8. Validate any JSON it returns, since structured output isn't documented\n\n### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Qwen3.5-122B-A10B or Ripple-65B Preview?\n\nQwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 1 of 4 scored categories. Ripple-65B Preview leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-5-122b-a10b\", \"b\": \"ripple-65b-preview\"}`. 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[Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [Ripple-65B Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.md)\n",
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