{
  "data": {
    "a": {
      "slug": "ornith-1-5-122b",
      "name": "Ornith-1.5-122B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json",
      "dns": "ornith-1-5-122b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:122b-q4_K_M"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 64,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
        "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published",
          "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`",
          "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends",
          "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "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": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-122b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it",
        "params": "125B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-122B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 77.7,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-122B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 75.8,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-122B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 69.7,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:122b-q4_K_M",
            "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 77,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build"
          },
          {
            "name": "Ornith-1.5-122B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 102.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 88.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 133,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 132.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 130.7,
            "memoryGB": 192,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 249.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 245.2,
            "memoryGB": 512,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:122b-q4_K_M",
        "runBuild": "ollama ornith-1.5:122b-q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. The commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json"
    },
    "answer": "Qwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Ornith-1.5-122B leads on reach, risk and agent interface.",
    "b": {
      "slug": "qwen3-8-flash-next",
      "name": "Qwen3.8-Flash-Next",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3.8-Flash-Next is Alibaba's open-weight multimodal mixture-of-experts model and an experimental preview of the Qwen4 architecture, 125B parameters with 6B active plus a 51B n-gram embedding table, published on Hugging Face in BF16 and FP8.",
      "url": "https://www.anchorterminal.com/tools/qwen3-8-flash-next",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json",
      "dns": "qwen3-8-flash-next.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
      "license": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.8-Flash-Next"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.8-Flash-Next-FP8"
        },
        {
          "registry": "ollama",
          "name": "qwen3.8-flash-next"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the Qwen Community License 1.0. A licensee whose group runs a Model as a Service or AI Work Assistant business (AI coding or office productivity products) needs a separate licence from Qwen, with no published price, before any commercial use beyond internal use. Running it yourself costs the hardware, 360.0 GB for the BF16 files, 185.5 GB for FP8 and 105 GB for Ollama's MLX 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/Qwen3.8-Flash-Next",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "source-available",
        "safetensors",
        "fp8",
        "self-hosted",
        "preview"
      ],
      "lastRelease": "2026-08-31",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 25,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 74,
          "interface": 70,
          "reach": 65,
          "risk": 45,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.",
        "bestFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "262,144 tokens of native context, several tool calls per turn, and thinking that turns off per request with three effort levels",
          "Safetensors only, a chat template in the repository and a SHA-256 for every weight file",
          "No gate on the download, and Alibaba's Qwen Cloud serves it for agents without the hardware"
        ],
        "weaknesses": [
          "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB",
          "The Qwen Community License requires a separate licence before commercial use by a model-as-a-service or coding or office assistant business",
          "No safety results, agent guidance, training data, limitations, languages or knowledge cut-off published",
          "Labelled an experimental preview, and Qwen has answered none of the 40 most recent discussions",
          "The tool-calling format is only in the template, and structured output isn't documented"
        ],
        "agentNotes": [
          "On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share",
          "On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does",
          "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off",
          "`reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error",
          "Send at most one system message and put it first, since the template raises an error otherwise",
          "Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without",
          "vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented",
          "Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.8
          }
        ],
        "editorialScores": {
          "capability": 74,
          "interface": 70,
          "reach": 65,
          "risk": 45,
          "upkeep": 36
        },
        "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",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run qwen3.8-flash-next:125b-mlx\n# or, as Qwen's GitHub README serves it (4 GPUs):\nvllm serve Qwen/Qwen3.8-Flash-Next --port 8000 --tensor-parallel-size 4 --max-model-len 262144 \\\n  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/Qwen3.8-Flash-Next-GGUF:IQ4_NL --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"Qwen/Qwen3.8-Flash-Next\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-8-flash-next"
      },
      "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",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "params": "125B",
        "active": "6B",
        "context": 262144,
        "goodFor": "Experimental preview of the Qwen4 architecture: a multimodal mixture-of-experts model with tool calling and adjustable reasoning effort.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3.8-Flash-Next-FP4-Local",
            "url": "https://huggingface.co/nvidia/Qwen3.8-Flash-Next-FP4-Local",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 102.2,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "ggml-org Qwen3.8-Flash-Next-GGUF (IQ4_NL)",
            "url": "https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF",
            "who": "llama.cpp",
            "precision": "iq4_nl",
            "sizeGB": 102.67,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 102.05 GB of IQ4_NL weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. The 2-bit Q2_K file (83.1 GB) is below the 4-bit line and isn't listed. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "ollama qwen3.8-flash-next:125b-a6b-q4_K_M",
            "url": "https://ollama.com/library/qwen3.8-flash-next:125b-a6b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 120,
            "memoryGB": 192,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Smallest non-MLX Ollama build; the library has no latest tag, and its 105 GB 125b-mlx tag is Apple-only. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Qwen3.8-Flash-Next-NVFP4",
            "url": "https://huggingface.co/nvidia/Qwen3.8-Flash-Next-NVFP4",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 132.7,
            "memoryGB": 192,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "Qwen3.8-Flash-Next-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 185.5,
            "memoryGB": 256,
            "format": "safetensors"
          },
          {
            "name": "ggml-org Qwen3.8-Flash-Next-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/Qwen3.8-Flash-Next-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 163.24,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 162.62 GB of Q8_0 weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 360,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Qwen3.8-Flash-Next-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 128
      },
      "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": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "params": "122B-A10B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b"
          },
          {
            "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": "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 (Singapore) Private Limited, the registrant of qwen.ai. The licence names Qwen as copyright holder",
        "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 (c) 2026 Qwen. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.",
          "Nothing is hosted, so the Qwen Community License 1.0 stands in for terms of service and no privacy policy is scored.",
          "No changelog. The BF16 repository's commit messages are an upload and README updates; the FP8 repository's say what changed.",
          "qwen.ai redirects /.well-known/security.txt to its home page, so there is no security.txt."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-flash-next.json",
      "live": {
        "slug": "qwen3-8-flash-next",
        "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": 15,
        "category": "interface",
        "leads": "ornith-1-5-122b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "ornith-1-5-122b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.",
          "b": "Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights."
        },
        "scores": {
          "a": 75,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "ornith-1-5-122b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.",
          "b": "File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent."
        },
        "scores": {
          "a": 50,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Ornith",
        "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": "MIT",
        "b": "Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-24",
        "b": "2026-08-31",
        "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": "2/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Ornith-1.5-122B leads on reach, risk and agent interface.",
        "question": "Which is better for AI agents, Ornith-1.5-122B or Qwen3.8-Flash-Next?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 75 against 65",
          "Risk, 50 against 45",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-122b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.",
        "slug": "qwen3-8-flash-next",
        "watchFor": "No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-flash-next.json",
        "title": "Agents-A1-4B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next.json",
        "title": "Apertus v1.5 8B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next.json",
        "title": "Apodex-1.1-mini vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.json",
        "title": "Arete-27B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.json",
        "title": "Arete-27B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.json",
        "title": "BigBang-v1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.json",
        "title": "BigBang-v1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b.json",
        "title": "Carbon-X vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next.json",
        "title": "Carbon-X vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json",
        "title": "Cola2 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-flash-next.json",
        "title": "Cola2 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-flash-next.json",
        "title": "Cosmos-Reason2-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next.json",
        "title": "Darwin-36B-Opus vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-flash-next.json",
        "title": "DeepSeek-V3.2 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b.json",
        "title": "DeepSeek V4.1 Flash vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b.json",
        "title": "Devstral Small 2 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-8-flash-next.json",
        "title": "Devstral Small 2 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b"
      },
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b.json",
        "title": "EXAONE 4.5 33B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next.json",
        "title": "EXAONE 4.5 33B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b.json",
        "title": "Gemma 4 12B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 12B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-122b.json",
        "title": "Gemma 4 26B A4B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 26B A4B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-122b.json",
        "title": "Gemma 4 31B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 31B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-122b.json",
        "title": "Gemma 4 E2B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 E2B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-122b.json",
        "title": "Gemma 4 E4B IT vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 E4B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-122b.json",
        "title": "GLM-4.5-Air vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-8-flash-next.json",
        "title": "GLM-4.5-Air vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.json",
        "title": "GLM-4.7-Flash vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.json",
        "title": "GLM-4.7-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-122b.json",
        "title": "GLM-5.3-Flash vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-122b.json",
        "title": "GLM-5.3 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next.json",
        "title": "GLM-5.3 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-122b.json",
        "title": "gpt-oss-120b vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-8-flash-next.json",
        "title": "gpt-oss-120b vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b.json",
        "title": "gpt-oss-20b vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next.json",
        "title": "gpt-oss-20b vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b.json",
        "title": "Granite 4.2 30B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next.json",
        "title": "Granite 4.2 30B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b.json",
        "title": "Granite 4.2 3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next.json",
        "title": "Granite 4.2 3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.json",
        "title": "Granite 4.2 8B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-8-flash-next.json",
        "title": "Granite 4.2 8B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-122b.json",
        "title": "Hy3 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next.json",
        "title": "Hy3 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-122b.json",
        "title": "Inkling-Small vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-flash-next.json",
        "title": "Inkling-Small vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.json",
        "title": "Inkling vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next.json",
        "title": "Inkling vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-flash-next.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next.json",
        "title": "Kimi K3.1 Pro vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-122b.json",
        "title": "Kimi K3 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.json",
        "title": "Kimi K3 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b.json",
        "title": "Kolibri-1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next.json",
        "title": "Kolibri-1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b.json",
        "title": "Kronos vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-flash-next.json",
        "title": "Kronos vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b.json",
        "title": "Laguna M.1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json",
        "title": "Laguna M.1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b.json",
        "title": "Laguna S 2.1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.json",
        "title": "Laguna S 2.1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b.json",
        "title": "Laguna XS 2.1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.json",
        "title": "Laguna XS 2.1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.json",
        "title": "LBS-27B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next.json",
        "title": "LBS-27B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b.json",
        "title": "LFM2.5-2.6B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.json",
        "title": "LFM2.5-2.6B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.json",
        "title": "LFM3-1.7B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-1.7B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b.json",
        "title": "LFM3-Audio-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-Audio-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b.json",
        "title": "LFM3-Omni-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-Omni-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b.json",
        "title": "LFM3-VL-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-VL-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-122b.json",
        "title": "Ling-3.0-tiny vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next.json",
        "title": "Ling-3.0-tiny vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b.json",
        "title": "Ling-3.1-flash-1019 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-flash-next.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-flash-next.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b.json",
        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM5-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next.json",
        "title": "MiniCPM5-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-122b.json",
        "title": "MiniMax-M2.5 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-122b.json",
        "title": "MiniMax-M3.1-Flash vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-flash-next.json",
        "title": "MiniMax-M3.1-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-122b.json",
        "title": "Mistral Large 4 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.json",
        "title": "Mistral Large 4 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-122b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-122b.json",
        "title": "Nanbeige4.2-3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next.json",
        "title": "Nanbeige4.2-3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-122b.json",
        "title": "Nanbeige4.3-33B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-122b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next"
      },
      {
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        "title": "North Mini Code 1.0 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-flash-next.json",
        "title": "North Mini Code 1.0 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.json",
        "title": "occamy-1.0 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.json",
        "title": "occamy-1.0 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next"
      },
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b.json",
        "title": "Ornith-1.5-122B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b"
      },
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        "title": "Ornith-1.5-122B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b"
      },
      {
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        "title": "Ornith-1.5-122B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b"
      },
      {
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        "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it"
      },
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        "title": "Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507"
      },
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        "title": "Ornith-1.5-122B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507"
      },
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-122B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.json",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json",
        "title": "Ornith-1.5-122B vs Recursive Flint",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-flash-next.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-flash-next.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-0.8B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next"
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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/qwen3-5-2b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-397B-A17B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-4B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.6-27B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.8-27B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-30b-a3b-instruct"
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        "title": "Qwen3.8-Flash-Next vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.json",
        "title": "Qwen3.8-Flash-Next vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint"
      },
      {
        "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/qwen3-8-flash-next-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.json",
        "title": "Qwen3.8-Flash-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.json",
        "title": "Qwen3.8-Flash-Next vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash.json",
        "title": "Qwen3.8-Flash-Next vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json",
        "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json",
        "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.json",
        "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.json",
        "title": "Qwen3.8-Flash-Next vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "ornith-1-5-122b",
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-122b": 75,
        "qwen3-8-flash-next": 65,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "ornith-1-5-122b",
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-122b": 50,
        "qwen3-8-flash-next": 45,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "ornith-1-5-122b",
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-122b": 85,
        "qwen3-8-flash-next": 70,
        "weight": 10
      },
      {
        "by": 0,
        "edge": "",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-122b": 36,
        "qwen3-8-flash-next": 36,
        "weight": 10
      }
    ],
    "summary": "Qwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Ornith-1.5-122B leads on reach, risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
      "qwen3-8-flash-next": "A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses."
    }
  },
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  "links": {
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    "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.min.md"
  },
  "markdown": "Qwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Ornith-1.5-122B leads on reach, risk and agent interface. Both do open-weight models you run yourself.\n\n- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #34 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.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### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 75 against 65\n- Risk, 50 against 45\n- Agent interface, 85 against 70\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n### Qwen3.8-Flash-Next (C)\n\nGood for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.\n\nWatch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB\n\n\n## Why the edge\n\n### Agent interface: Ornith-1.5-122B +15\n\n- Ornith-1.5-122B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n- Qwen3.8-Flash-Next (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, and Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented for the weights. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.\n\n### Reach: Ornith-1.5-122B +10\n\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n- Qwen3.8-Flash-Next (65): Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB. Ollama's Apple-only 105 GB MLX build isn't among the builds and would land in the same band. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen4_exp architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 10 of 15, a custom licence that's free for commercial use except for model-as-a-service and coding or office assistant businesses, which need a separate licence. Download 10, neither repository is gated. Hosted 5, Hugging Face lists two live providers, Featherless AI for the BF16 repository and Nscale for the FP8 build, and Alibaba's Qwen Cloud serves Qwen3.8-Flash, which the card says is based on these weights.\n\n### Risk: Ornith-1.5-122B +5\n\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n- Qwen3.8-Flash-Next (45): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published on the card or in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5 of 15, 5 off for the field-of-use limit on model-as-a-service and coding or office assistant businesses and 5 off for the naming duty above a user or revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-122B | Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 65 | Ornith-1.5-122B +10 |\n| Risk | 20% (20 this run) | 50 | 45 | Ornith-1.5-122B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-122B +15 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 36 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61.8 · C** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-122B | Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Ornith | 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 | MIT | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-24 | 2026-08-31 |\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 | 2/5 (4) |\n\n## Verdicts\n\n**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\n\n**Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.\n\n## Before you call either\n\n### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n### Qwen3.8-Flash-Next\n\n1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share\n2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off\n5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Send at most one system message and put it first, since the template raises an error otherwise\n7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without\n8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe\n9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product\n\n## Questions\n\n### Which is better for AI agents, Ornith-1.5-122B or Qwen3.8-Flash-Next?\n\nQwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Ornith-1.5-122B leads on reach, risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-122b\", \"b\": \"qwen3-8-flash-next\"}`. 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    "description": "Qwen3.8-Flash-Next and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61.8 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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