{
  "data": {
    "a": {
      "slug": "gemma-4-31b-it",
      "name": "Gemma 4 31B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json",
      "dns": "gemma-4-31b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-31B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-31B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:31b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are 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, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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://gemma.ai/models/gemma-4/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 74.1,
        "grade": "BB",
        "agentReady": true,
        "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": 1,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
        "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "strengths": [
          "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB",
          "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too",
          "Tool calling documented by Google with a response schema Transformers parses, several calls per turn",
          "Thinking that can be switched on or off per request and 262,144 tokens of native context"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "No prompt-injection guidance and no security route tied to Gemma",
          "Structured output isn't documented by Google",
          "Training data described only in general terms",
          "Files unchanged since 20 July and no Google reply in the discussions since 30 July"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images",
          "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build",
          "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does",
          "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block",
          "Drop earlier thoughts from the history except on tool-call turns, as the card asks",
          "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64",
          "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding",
          "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 74.1
          }
        ],
        "editorialScores": {
          "capability": 61,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n  --reasoning-parser gemma4 --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-31b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-31B",
        "params": "30.7B",
        "context": 262144,
        "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-31B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:31b",
            "url": "https://ollama.com/library/gemma4:31b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-31B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 23.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.5,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768",
        "runBuild": "gemma-4-31B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (the registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.",
          "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json",
      "live": {
        "slug": "gemma-4-31b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:43:12.714423166Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
      }
    },
    "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.",
    "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": 40,
        "category": "reach",
        "leads": "gemma-4-31b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini API serves it today.",
          "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": 95,
          "b": 55
        }
      },
      {
        "by": 35,
        "category": "interface",
        "leads": "gemma-4-31b-it",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents the declaration and call syntax and ships a response schema that Transformers parses. Trained tool calling 20, several calls and responses in one turn. Structured output 0, Google documents none and the card doesn't claim it. Reasoning control 10, thinking turns on or off per request and comes back in its own block. Context 10, 262,144 tokens native.",
          "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": 24,
        "category": "upkeep",
        "leads": "gemma-4-31b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, all seven items are there, with languages given as counts. Training data 10, a general description with a January 2025 cut-off. Last change 10, the files last changed on 20 July, 104 days before 1 November, and Google's reference code for Gemma 4 last changed in a way that runs this model on 6 July, both inside 180 days. Responsiveness 10, Google answered 14 of the last 40 discussions, none since 30 July. Version policy 10, earlier generations are kept in their own repositories with dated releases. File health 5, two template reports wait 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": 65,
          "b": 41
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "gemma-4-31b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public advisories were found.",
          "b": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55."
        },
        "scores": {
          "a": 70,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "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": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "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": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Gemma 4 31B IT or Qwen-AgentWorld-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 55",
          "Risk, 70 against 55",
          "Agent interface, 85 against 50",
          "Documentation and upkeep, 65 against 41"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence",
        "slug": "gemma-4-31b-it",
        "watchFor": "No safety evaluation results published"
      },
      {
        "aheadOn": null,
        "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
      },
      {
        "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-gemma-4-31b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
      },
      {
        "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-gemma-4-31b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it"
      },
      {
        "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-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it"
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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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b"
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      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-31b-it"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b"
      },
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.json",
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      {
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 31B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b.json",
        "title": "Gemma 4 31B IT vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json",
        "title": "Gemma 4 31B IT vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b.json",
        "title": "Gemma 4 31B IT vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next.json",
        "title": "Gemma 4 31B IT vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next.json",
        "title": "Gemma 4 31B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-32b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.json",
        "title": "Gemma 4 31B IT vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Gemma 4 31B IT vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.json",
        "title": "Gemma 4 31B IT vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview.json",
        "title": "Gemma 4 31B IT vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview.json",
        "title": "Gemma 4 31B IT vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b.json",
        "title": "Gemma 4 31B IT vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.json",
        "title": "Gemma 4 31B IT vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash.json",
        "title": "Gemma 4 31B IT vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Gemma 4 31B IT vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.json",
        "title": "Gemma 4 31B IT vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b.json",
        "title": "Gemma 4 31B IT vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.json",
        "title": "Gemma 4 31B IT vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b.json",
        "title": "Gemma 4 31B IT vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b.json",
        "title": "Gemma 4 E2B IT vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen-agentworld-35b-a3b.json",
        "title": "Gemma 4 E4B IT vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen-agentworld-35b-a3b.json",
        "title": "GLM-4.5-Air vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.json",
        "title": "GLM-4.7-Flash vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy3 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling-Small vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.json",
        "title": "Inkling vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json",
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kimi K3 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kolibri-1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna M.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Qwen-AgentWorld-35B-A3B",
        "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",
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        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-2B",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-397b-a17b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "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",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json",
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        "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"
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        "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"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b"
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        "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",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
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      {
        "by": 40,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 95,
        "key": "reach",
        "name": "Reach",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 25
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      {
        "by": 15,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 70,
        "key": "risk",
        "name": "Risk",
        "qwen-agentworld-35b-a3b": 55,
        "weight": 20
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        "by": 35,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 85,
        "key": "interface",
        "name": "Agent interface",
        "qwen-agentworld-35b-a3b": 50,
        "weight": 10
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      {
        "by": 24,
        "edge": "gemma-4-31b-it",
        "gemma-4-31b-it": 65,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen-agentworld-35b-a3b": 41,
        "weight": 10
      }
    ],
    "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.",
      "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."
    }
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    "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.min.md"
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  "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.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### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Reach, 95 against 55\n- Risk, 70 against 55\n- Agent interface, 85 against 50\n- Documentation and upkeep, 65 against 41\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation results published\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\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### Reach: Gemma 4 31B IT +40\n\n- Gemma 4 31B IT (95): Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini API serves it today.\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### Agent interface: Gemma 4 31B IT +35\n\n- Gemma 4 31B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents the declaration and call syntax and ships a response schema that Transformers parses. Trained tool calling 20, several calls and responses in one turn. Structured output 0, Google documents none and the card doesn't claim it. Reasoning control 10, thinking turns on or off per request and comes back in its own block. Context 10, 262,144 tokens native.\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### Documentation and upkeep: Gemma 4 31B IT +24\n\n- Gemma 4 31B IT (65): Model card 20, all seven items are there, with languages given as counts. Training data 10, a general description with a January 2025 cut-off. Last change 10, the files last changed on 20 July, 104 days before 1 November, and Google's reference code for Gemma 4 last changed in a way that runs this model on 6 July, both inside 180 days. Responsiveness 10, Google answered 14 of the last 40 discussions, none since 30 July. Version policy 10, earlier generations are kept in their own repositories with dated releases. File health 5, two template reports wait 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### Risk: Gemma 4 31B IT +15\n\n- Gemma 4 31B IT (70): File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public advisories were found.\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 31B IT +40 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 31B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 50 | Gemma 4 31B IT +35 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 41 | Gemma 4 31B IT +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | 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 | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 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 | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\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### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\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, Gemma 4 31B IT or Qwen-AgentWorld-35B-A3B?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"qwen-agentworld-35b-a3b\"}`. 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    "title": "Gemma 4 31B IT vs Qwen-AgentWorld-35B-A3B for AI agents (2026)",
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