{
  "data": {
    "a": {
      "slug": "cola2",
      "name": "Cola2",
      "vendor": "ByteDance (Seed)",
      "vendorUrl": "https://github.com/ByteDance-Seed",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.",
      "url": "https://www.anchorterminal.com/tools/cola2",
      "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json",
      "dns": "cola2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ByteDance-Seed/Cola2",
      "license": "None stated for the weights (the code is Apache 2.0)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ByteDance-Seed/Cola2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (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://github.com/ByteDance-Seed/Cola2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "base-model",
        "research",
        "diffusion",
        "continuous-latent",
        "no-licence",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 33.9,
        "grade": "F",
        "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": 113,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
        "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "strengths": [
          "A documented new architecture with a paper and a reproduction protocol",
          "Evaluation data and checksums ship with the weights",
          "A security policy that points to ByteDance's security centre",
          "The model card says plainly what it isn't for"
        ],
        "weaknesses": [
          "No licence stated for the weights",
          "A base model: no chat, no tools, 32 new tokens by default",
          "Loads only with the repository's own Python, on Transformers 4.x",
          "35 GB of F32 files and no quantised build",
          "Below Llama-3-8B on the card's own eight-task table",
          "No stated context length or cut-off"
        ],
        "agentNotes": [
          "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions",
          "Don't use it commercially on the strength of this listing. The weights have no stated licence",
          "If you study it, read modeling_cola2.py first: trust_remote_code runs it",
          "Pin Transformers below 5, as the card requires",
          "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding",
          "Check the download with `sha256sum -c SHA256SUMS`"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "F",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 33.9
          }
        ],
        "editorialScores": {
          "interface": 20,
          "reach": 25,
          "risk": 40,
          "upkeep": 58
        },
        "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 \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first",
        "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/cola2"
      },
      "weights": {
        "trainedBy": "ByteDance Seed",
        "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card",
        "params": "8.8B",
        "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.",
        "runtimes": [],
        "builds": [
          {
            "name": "Cola2 (F32 safetensors)",
            "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 35.07,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards in 32-bit floats. The card's example loads them as BF16"
          }
        ],
        "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"",
        "runBuild": "Cola2 (F32 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "ByteDance (Seed)",
        "family": "Cola",
        "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed",
        "others": [
          {
            "name": "Cola-DLM",
            "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up",
        "domain": "bytedance.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.",
          "No licence is stated for the weights, so there are no terms to read.",
          "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.",
          "The changelog is the commit history, six commits on 6 November 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json"
    },
    "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.",
    "b": {
      "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": 1518,
        "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",
        "qwen-agentworld-35b-a3b",
        "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": "2026-11-10T16:09:18.413964975Z"
        },
        "domain": {
          "domain": "qwen.ai",
          "registered": "2023-08-27",
          "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai",
          "checkedAt": "2026-11-08T15:07:12.468678256Z"
        },
        "updatedAt": "2026-11-10T16:09:18.413964975Z"
      }
    },
    "edges": [
      {
        "by": 55,
        "category": "reach",
        "leads": "qwen3-5-122b-a10b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.",
          "b": "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."
        },
        "scores": {
          "a": 25,
          "b": 80
        }
      },
      {
        "by": 50,
        "category": "interface",
        "leads": "qwen3-5-122b-a10b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.",
          "b": "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."
        },
        "scores": {
          "a": 20,
          "b": 70
        }
      },
      {
        "by": 24,
        "category": "upkeep",
        "leads": "cola2",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.",
          "b": "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."
        },
        "scores": {
          "a": 58,
          "b": 34
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "qwen3-5-122b-a10b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.",
          "b": "Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open."
        },
        "scores": {
          "a": 40,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "ByteDance (Seed)",
        "b": "Alibaba (Qwen team)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "None stated for the weights (the code is 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": "2026-11-06",
        "b": "2026-02-24",
        "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": "none",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Cola2 or Qwen3.5-122B-A10B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 58 against 34"
        ],
        "also": null,
        "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study",
        "slug": "cola2",
        "watchFor": "No licence stated for the weights"
      },
      {
        "aheadOn": [
          "Reach, 80 against 25",
          "Risk, 55 against 40",
          "Agent interface, 70 against 20"
        ],
        "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"
      }
    ],
    "history": {
      "cola2": null,
      "qwen3-5-122b-a10b": [
        {
          "causes": [
            "time"
          ],
          "date": "2026-11-02",
          "delta": -0.5,
          "from": 63.6,
          "fromGrade": "B",
          "to": 63.1,
          "toGrade": "B"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b.json",
        "title": "Inkling vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-122b-a10b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.json",
        "title": "Kimi K3 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-122b-a10b.json",
        "title": "Kolibri-1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-122b-a10b.json",
        "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/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-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-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/lbs-27b-vs-qwen3-5-122b-a10b.json",
        "title": "LBS-27B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b"
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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/lfm3-1-7b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-1.7B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-Omni-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-122b-a10b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.json",
        "title": "LongCat-2.5 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-122b-a10b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-122B-A10B",
        "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-flash-rl-vs-qwen3-5-122b-a10b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-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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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-122b-a10b.json",
        "title": "MiniMax-M2.5 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-122b-a10b.json",
        "title": "MiniMax-M3.1-Flash vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b.json",
        "title": "Mistral Large 4 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b.json",
        "title": "North Mini Code 1.0 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b.json",
        "title": "occamy-1.0 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-122b-a10b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-122B-A10B",
        "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/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B",
        "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/qwen3-5-0-8b-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen3.5-0.8B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-4b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-27b"
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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"
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      {
        "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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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-30b-a3b-instruct.json",
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-122B-A10B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.5-122B-A10B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-sensenova-loop-9b-preview"
      },
      {
        "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"
      },
      {
        "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-step-3-7-flash.json",
        "title": "Qwen3.5-122B-A10B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-step-3-7-flash"
      },
      {
        "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/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b.json",
        "title": "Qwen3.5-122B-A10B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 55,
        "cola2": 25,
        "edge": "qwen3-5-122b-a10b",
        "key": "reach",
        "name": "Reach",
        "qwen3-5-122b-a10b": 80,
        "weight": 25
      },
      {
        "by": 15,
        "cola2": 40,
        "edge": "qwen3-5-122b-a10b",
        "key": "risk",
        "name": "Risk",
        "qwen3-5-122b-a10b": 55,
        "weight": 20
      },
      {
        "by": 50,
        "cola2": 20,
        "edge": "qwen3-5-122b-a10b",
        "key": "interface",
        "name": "Agent interface",
        "qwen3-5-122b-a10b": 70,
        "weight": 10
      },
      {
        "by": 24,
        "cola2": 58,
        "edge": "cola2",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-5-122b-a10b": 34,
        "weight": 10
      }
    ],
    "summary": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b",
    "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.min.md"
  },
  "markdown": "Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json\n- Qwen3.5-122B-A10B: grade B, 63.1/100, rank #25 of 160 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- 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### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nAhead on:\n- Documentation and upkeep, 58 against 34\n\nWatch for: No licence stated for the weights\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 25\n- Risk, 55 against 40\n- Agent interface, 70 against 20\n\nWatch for: Too big for a single 64 GB machine even at 4 bits\n\n\n## Why the edge\n\n### Reach: Qwen3.5-122B-A10B +55\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\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\n### Agent interface: Qwen3.5-122B-A10B +50\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\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\n### Documentation and upkeep: Cola2 +24\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\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\n### Risk: Qwen3.5-122B-A10B +15\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\n- Qwen3.5-122B-A10B (55): Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open.\n\n## Score by category\n\n| Category | Weight | Cola2 | Qwen3.5-122B-A10B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 80 | Qwen3.5-122B-A10B +55 |\n| Risk | 20% (20 this run) | 40 | 55 | Qwen3.5-122B-A10B +15 |\n| Agent interface | 10% (10 this run) | 20 | 70 | Qwen3.5-122B-A10B +50 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 34 | Cola2 +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **63.1 · B** | |\n\n## Facts side by side\n\n| Fact | Cola2 | Qwen3.5-122B-A10B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-02-24 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### Cola2\n\nNo score change since it was listed.\n\n### Qwen3.5-122B-A10B\n\n- -0.5 on 2 November 2026: time\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\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## Before you call either\n\n### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\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## Questions\n\n### Which is better for AI agents, Cola2 or Qwen3.5-122B-A10B?\n\nQwen3.5-122B-A10B scores 63.1 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"qwen3-5-122b-a10b\"}`. From a terminal: `anchor compare cola2 qwen3-5-122b-a10b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cola2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json\n\n## Other comparisons with Cola2 or Qwen3.5-122B-A10B\n\n- [Agents-A1-4B vs Cola2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.md)\n- [Agents-A1-4B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-122b-a10b.md)\n- [Apertus v1.5 8B vs Cola2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.md)\n- [Apertus v1.5 8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.md)\n- [Apodex-1.1-mini vs Cola2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.md)\n- [Apodex-1.1-mini vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-122b-a10b.md)\n- [Arete-27B vs 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