{
  "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",
      "dns": "glm-4-7-flash.grade.anchorterminal.com",
      "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": "2026-11-02"
      },
      "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": 1518,
        "categoryRank": 20,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "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": "0.7",
            "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": 73,
        "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 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\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-4.7-Flash-GGUF:Q4_K --jinja",
        "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": "ggml-org GLM-4.7-Flash-GGUF (Q4_K)",
            "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k",
            "sizeGB": 18.24,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 18.24 GB of Q4_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "ggml-org GLM-4.7-Flash-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 31.84,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 31.84 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "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": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json",
      "live": {
        "slug": "glm-4-7-flash",
        "securityTxt": {
          "url": "https://z.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:36.154640571Z"
        },
        "domain": {
          "domain": "z.ai",
          "registered": "2017-12-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/z.ai",
          "checkedAt": "2026-11-08T15:05:18.801433245Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:24:00.649563516Z",
            "changedAt": "2026-11-10T16:24:00.649563516Z",
            "fingerprint": "7db42760a7b7"
          }
        ],
        "updatedAt": "2026-11-10T16:24:00.649563516Z"
      }
    },
    "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on documentation and upkeep.",
    "b": {
      "slug": "qwen-agentworld-35b-a3b",
      "name": "Qwen-AgentWorld-35B-A3B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json",
      "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen-AgentWorld-35B-A3B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit 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/Qwen/Qwen-AgentWorld-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "world-model",
        "agent-simulation",
        "reasoning",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.1,
        "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": 1518,
        "categoryRank": 104,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.",
        "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "strengths": [
          "Simulates seven kinds of agent environment in one model, with the system prompts for each published",
          "Apache 2.0, ungated, safetensors only",
          "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases",
          "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF",
          "A 262,144-token context for long interaction histories"
        ],
        "weaknesses": [
          "Not a general assistant, and the card doesn't describe using it to call tools",
          "No quantised build from Alibaba, so the GGUF route depends on a third party",
          "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026",
          "No limitations, safety results or security contact on the card",
          "Its results are on Alibaba's own benchmark"
        ],
        "agentNotes": [
          "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation",
          "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights",
          "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends",
          "Keep at least 128K of context, since the simulation depends on the history",
          "Thinking is on by default and comes back through the qwen3 reasoning parser",
          "A simulated observation is a prediction. Check anything that matters against the real environment"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.1
          }
        ],
        "editorialScores": {
          "interface": 50,
          "reach": 55,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 66,
        "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": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n  --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen-agentworld-35b-a3b"
      },
      "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",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen)",
        "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata",
        "params": "34.7B",
        "active": "3B",
        "context": 262144,
        "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 22.13,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 69.32,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "The only build Alibaba publishes. Language weights only"
          }
        ],
        "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja",
        "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen-AgentWorld",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "olderCount": 109
      },
      "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": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.",
          "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November."
        ],
        "score": 66
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "interface",
        "leads": "glm-4-7-flash",
        "name": "Agent interface",
        "reasons": {
          "a": "A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.",
          "b": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50."
        },
        "scores": {
          "a": 85,
          "b": 50
        }
      },
      {
        "by": 25,
        "category": "reach",
        "leads": "glm-4-7-flash",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.",
          "b": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55."
        },
        "scores": {
          "a": 80,
          "b": 55
        }
      },
      {
        "by": 13,
        "category": "upkeep",
        "leads": "qwen-agentworld-35b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.",
          "b": "Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41."
        },
        "scores": {
          "a": 28,
          "b": 41
        }
      }
    ],
    "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": "2026-06-25",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-4.7-Flash or Qwen-AgentWorld-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 55",
          "Agent interface, 85 against 50"
        ],
        "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, 41 against 28"
        ],
        "also": null,
        "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine",
        "slug": "qwen-agentworld-35b-a3b",
        "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
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        "title": "Agents-A1-4B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Agents-A1-4B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json",
        "title": "Apertus v1.5 8B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.json",
        "title": "Apodex-1.1-mini vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.json",
        "title": "Arete-27B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash"
      },
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Arete-27B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b"
      },
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        "title": "BigBang-v1 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.json",
        "title": "BigBang-v1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b"
      },
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.json",
        "title": "Carbon-X vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.json",
        "title": "Cola2 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash"
      },
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.json",
        "title": "Cola2 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.json",
        "title": "Cosmos-Reason2-2B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Cosmos-Reason2-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash.json",
        "title": "Darwin-36B-Opus vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json",
        "title": "Darwin-36B-Opus vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b"
      },
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b"
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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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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct",
        "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-coder-next.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2.json",
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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-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview.json",
        "title": "GLM-4.7-Flash vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-step-3-7-flash"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B",
        "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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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-zaya1-8b.json",
        "title": "GLM-4.7-Flash vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b.json",
        "title": "GLM-5.3-Flash vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b.json",
        "title": "GLM-5.3 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.json",
        "title": "gpt-oss-120b vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b.json",
        "title": "gpt-oss-20b vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy3 vs Qwen-AgentWorld-35B-A3B",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling vs Qwen-AgentWorld-35B-A3B",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b"
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        "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kronos vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LBS-27B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-1.7B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniMax-M2.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniMax-M3.1-Flash vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Muse Glimmer 30B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nanbeige4.3-33B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b.json",
        "title": "North Mini Code 1.0 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b.json",
        "title": "occamy-1.0 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b.json",
        "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-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-397B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-27b"
      },
      {
        "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/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 80,
        "key": "reach",
        "name": "Reach",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "glm-4-7-flash": 55,
        "key": "risk",
        "name": "Risk",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 20
      },
      {
        "by": 35,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 85,
        "key": "interface",
        "name": "Agent interface",
        "qwen-agentworld-35b-a3b": 50,
        "weight": 10
      },
      {
        "by": 13,
        "edge": "qwen-agentworld-35b-a3b",
        "glm-4-7-flash": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen-agentworld-35b-a3b": 41,
        "weight": 10
      }
    ],
    "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "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.",
      "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.min.md"
  },
  "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B 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 160 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- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.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### 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- Reach, 80 against 55\n- Agent interface, 85 against 50\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nAhead on:\n- Documentation and upkeep, 41 against 28\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Agent interface: GLM-4.7-Flash +35\n\n- GLM-4.7-Flash (85): A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, says tools are declared in OpenAI style and shows calls coming back as tool_calls. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, thinking turned off per request with enable_thinking and the reasoning returned by the glm45 parser. Context is 10, 202,752 positions native with no rope scaling.\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n### Reach: GLM-4.7-Flash +25\n\n- GLM-4.7-Flash (80): Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n### Documentation and upkeep: Qwen-AgentWorld-35B-A3B +13\n\n- GLM-4.7-Flash (28): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.\n- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n## Score by category\n\n| Category | Weight | GLM-4.7-Flash | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 55 | GLM-4.7-Flash +25 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 50 | GLM-4.7-Flash +35 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 41 | Qwen-AgentWorld-35B-A3B +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63 · B** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | GLM-4.7-Flash | Qwen-AgentWorld-35B-A3B |\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 | 2026-06-25 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\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**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, GLM-4.7-Flash or Qwen-AgentWorld-35B-A3B?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B 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-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"qwen-agentworld-35b-a3b\"}`. 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