{
  "data": {
    "a": {
      "slug": "glm-4-7-flash",
      "name": "GLM-4.7-Flash",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-4.7-Flash is Z.ai's open-weight mixture-of-experts reasoning model, 30B parameters with about 3B active per token, for English and Chinese, published on Hugging Face in BF16 under the MIT licence.",
      "url": "https://www.anchorterminal.com/tools/glm-4-7-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json",
      "repo": "https://huggingface.co/zai-org/GLM-4.7-Flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.7-Flash"
        },
        {
          "registry": "ollama",
          "name": "glm-4.7-flash"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 62.5 GB for the BF16 files and 19 GB for Ollama's default build. Z.ai's own API also serves GLM-4.7-Flash, which its docs describe as completely free (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/zai-org/GLM-4.7-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-01-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 21,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.",
        "bestFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "strengths": [
          "MIT weights with no use policy, downloadable with no account",
          "Model code in released vLLM, SGLang, Transformers and llama.cpp, and a local Ollama build",
          "Ollama's 19 GB build fits a 24 GB GPU, and BF16 fits our reference machine",
          "Z.ai documents the glm47 tool parser, OpenAI-style tool declarations and turning thinking off per request",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard"
        ],
        "weaknesses": [
          "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January",
          "No quantised build from Z.ai and no LICENSE file in the repository",
          "No safety evaluation, agent-use guidance or security contact",
          "The card leaves out intended use, limitations, context length, knowledge cut-off and training data",
          "config.json and tokenizer_config.json disagree on context length"
        ],
        "agentNotes": [
          "On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`",
          "Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build",
          "Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text",
          "Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026",
          "Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default",
          "Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench",
          "Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 63
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run glm-4.7-flash\n# or, per Z.ai's card:\nvllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 \\\n  --enable-auto-tool-choice --served-model-name glm-4.7-flash",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-4.7-flash\",\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/glm-4-7-flash"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-5-3",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "30B",
        "active": "3B",
        "context": 202752,
        "goodFor": "Small mixture-of-experts reasoning model for English and Chinese with tool calling, a local coding and agent model.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama glm-4.7-flash:latest",
            "url": "https://ollama.com/library/glm-4.7-flash:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "GLM-4.7-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash",
        "runBuild": "GLM-4.7-Flash",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-4",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-4-9B",
            "url": "https://huggingface.co/zai-org/glm-4-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat-1M",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m",
            "params": "9B"
          },
          {
            "name": "GLM-4V-9B",
            "url": "https://huggingface.co/zai-org/glm-4v-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-4-32B-Base-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-Z1-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-Rumination-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4.1V-9B-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base",
            "params": "9B"
          },
          {
            "name": "GLM-4.1V-9B-Thinking",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking",
            "params": "9B"
          },
          {
            "name": "GLM-4.5-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Base",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5",
            "url": "https://huggingface.co/zai-org/GLM-4.5",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5-Air-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.5-Air",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air",
            "params": "106B-A12B",
            "listing": "https://www.anchorterminal.com/tools/glm-4-5-air"
          },
          {
            "name": "GLM-4.5V",
            "url": "https://huggingface.co/zai-org/GLM-4.5V",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.6",
            "url": "https://huggingface.co/zai-org/GLM-4.6",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.6V",
            "url": "https://huggingface.co/zai-org/GLM-4.6V",
            "params": "106B"
          },
          {
            "name": "GLM-4.6V-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash",
            "params": "9B"
          },
          {
            "name": "GLM-4.7",
            "url": "https://huggingface.co/zai-org/GLM-4.7",
            "params": "355B-A32B"
          }
        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai)",
        "domain": "z.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Z.ai's terms of use name JINGSHENG HENGXING TECHNOLOGY PTE.LTD as the operator of Z.ai, and RDAP names the same company as the registrant of z.ai. The weights' card names no legal entity and the repository has no LICENSE file.",
          "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 12 commits from 19 to 29 January 2026, mostly card and benchmark updates.",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json"
    },
    "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 5 scored categories. Qwen3.5-2B leads on documentation and upkeep.",
    "b": {
      "slug": "qwen3-5-2b",
      "name": "Qwen3.5-2B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3.5-2B is a small model in Alibaba's Qwen3.5 family, 2B language parameters with a vision encoder, thinking off by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0 for prototyping, fine-tuning and research.",
      "url": "https://www.anchorterminal.com/tools/qwen3-5-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json",
      "repo": "https://huggingface.co/Qwen/Qwen3.5-2B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-2B"
        },
        {
          "registry": "ollama",
          "name": "qwen3.5:2b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 4.6 GB for the BF16 files and 1.9 to 2.7 GB for Ollama's 4-bit and 8-bit builds (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-2B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-03-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 73,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 37
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.",
        "bestFor": "Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU",
        "strengths": [
          "Runs in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers, in about 2 GB at 4 bits",
          "Apache 2.0 with nothing added, no account or gate to download",
          "Safetensors only, no remote code, files hashed in the publisher's organisation",
          "262,144 tokens native and several tool calls per turn",
          "The card is honest about its size, naming prototyping and fine-tuning as the uses and warning about thinking loops"
        ],
        "weaknesses": [
          "No quantised build from Alibaba, and only one inference provider",
          "No safety results, agent-use guidance or reporting route for flaws",
          "No reply from Qwen to any discussion, and a template fix waits since April",
          "Tool format only in the template, structured output undocumented, and no word on where reasoning comes back",
          "No knowledge cut-off or training data sources on the card"
        ],
        "agentNotes": [
          "Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build",
          "Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out",
          "There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking",
          "Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests",
          "Validate any JSON it returns, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "capability": 28,
          "interface": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 37
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run qwen3.5:2b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3.5:2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-5-2b"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "builtOn": "Qwen/Qwen3.5-2B-Base",
        "params": "2B",
        "context": 262144,
        "goodFor": "Small vision-language model for prototyping, fine-tuning and light local agent steps: text, images and video in, tool calling, thinking off by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.6,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "ollama qwen3.5:2b-q4_K_M",
            "url": "https://ollama.com/library/qwen3.5:2b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.9,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M language model (1.3 GB) with a BF16 vision projector (671 MB), digest 1e2d21a4f03a. The default qwen3.5:2b is Q8_0 at 2.7 GB (3.1 GB for the MLX variant). Size as Ollama's pages show it"
          },
          {
            "name": "LM Studio qwen/qwen3.5-2b (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/Qwen3.5-2B-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 1.9,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Qwen3.5-2B-Q4_K_M.gguf (1.27 GB) plus mmproj-Qwen3.5-2B-BF16.gguf (0.67 GB), summed from the Hub's file list. LM Studio's catalogue entry also lists MLX 4-bit and 8-bit builds"
          }
        ],
        "run": "vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Qwen3.5-2B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "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-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright Alibaba Cloud. The domain's registration date is carried from the 20 October 2026 RDAP check for qwen3-8-27b.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The commit history's 16 messages are uploads, README edits and one template and tokenizer config update on 1 March 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 2 March 2026.",
          "qwen.ai answers every path with its web app, so there is no security.txt, and QwenLM/Qwen3.5 has no SECURITY.md."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-2b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "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": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-01-29",
        "b": "2026-03-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.8/5 (4)",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 5 scored categories. Qwen3.5-2B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-4.7-Flash or Qwen3.5-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 59 against 28",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "slug": "glm-4-7-flash",
        "watchFor": "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 37 against 28"
        ],
        "also": null,
        "goodFor": "Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU",
        "slug": "qwen3-5-2b",
        "watchFor": "No quantised build from Alibaba, and only one inference provider"
      }
    ],
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      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-2b.json",
        "title": "Agents-A1-4B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b.json",
        "title": "Apertus v1.5 8B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.json",
        "title": "Apodex-1.1-mini vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-2b.json",
        "title": "Apodex-1.1-mini vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-2b.json",
        "title": "Cosmos-Reason2-2B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-2b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.json",
        "title": "DeepSeek V4.1 Flash vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b.json",
        "title": "Gemma 4 12B IT vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-7-flash"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b"
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        "title": "GLM-4.7-Flash vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b.json",
        "title": "Qwen3.5-2B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen3.5-2B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b.json",
        "title": "Qwen3.5-2B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b"
      },
      {
        "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/qwen3-5-2b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-2B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3.5-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b.json",
        "title": "Qwen3.5-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 31,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 59,
        "key": "capability",
        "name": "Capability",
        "qwen3-5-2b": 28,
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "glm-4-7-flash": 80,
        "key": "reach",
        "name": "Reach",
        "qwen3-5-2b": 80,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "glm-4-7-flash": 55,
        "key": "risk",
        "name": "Risk",
        "qwen3-5-2b": 55,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 85,
        "key": "interface",
        "name": "Agent interface",
        "qwen3-5-2b": 60,
        "weight": 10
      },
      {
        "by": 9,
        "edge": "qwen3-5-2b",
        "glm-4-7-flash": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-5-2b": 37,
        "weight": 10
      }
    ],
    "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 5 scored categories. Qwen3.5-2B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "glm-4-7-flash": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.",
      "qwen3-5-2b": "A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b",
    "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.min.md"
  },
  "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 5 scored categories. Qwen3.5-2B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json\n- Qwen3.5-2B: grade D, 50.5/100, rank #92 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-4.7-Flash (B)\n\nGood for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime\n\nAhead on:\n- Capability, 59 against 28\n- Agent interface, 85 against 60\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n### Qwen3.5-2B (D)\n\nGood for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU\n\nAhead on:\n- Documentation and upkeep, 37 against 28\n\nWatch for: No quantised build from Alibaba, and only one inference provider\n\n\n## Score by category\n\n| Category | Weight | GLM-4.7-Flash | Qwen3.5-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 28 | GLM-4.7-Flash +31 |\n| Reach | 25% (25 this run) | 80 | 80 | even |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 60 | GLM-4.7-Flash +25 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 37 | Qwen3.5-2B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63 · B** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | GLM-4.7-Flash | Qwen3.5-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | 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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-01-29 | 2026-03-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.8/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.\n\n**Qwen3.5-2B.** A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.\n\n## Before you call either\n\n### GLM-4.7-Flash\n\n1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`\n2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build\n3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text\n4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026\n5. Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default\n6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench\n7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000\n\n### Qwen3.5-2B\n\n1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build\n2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out\n4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking\n5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests\n6. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, GLM-4.7-Flash or Qwen3.5-2B?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 5 scored categories. Qwen3.5-2B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"qwen3-5-2b\"}`. From a terminal: `anchor compare glm-4-7-flash qwen3-5-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json\n\n## Other comparisons with GLM-4.7-Flash or Qwen3.5-2B\n\n- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.md)\n- [Agents-A1-4B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-2b.md)\n- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)\n- [Apertus v1.5 8B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b.md)\n- [Apodex-1.1-mini vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.md)\n- [Apodex-1.1-mini vs Qwen3.5-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-2b.md)\n- [BigBang-v1 vs 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Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 31B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 31B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 E2B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E2B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 E4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E4B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-2b.md)\n- [GLM-4.5-Air vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.md)\n- [GLM-4.5-Air vs Qwen3.5-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-2b.md)\n- [GLM-4.7-Flash vs 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