{
  "data": {
    "a": {
      "slug": "diffusiongemma-26b-a4b-it",
      "name": "DiffusionGemma 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json",
      "dns": "diffusiongemma-26b-a4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/diffusiongemma-26B-A4B-it"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Google also lists the model on Kaggle and in Vertex AI Model Garden.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google sells no per-call access to these weights through the Gemini API, which serves only gemma-4-31b-it and gemma-4-26b-a4b-it among Gemma models. Running them costs the hardware, 51.7 GB of BF16 safetensors; Google's inference guide asks for a GPU with more than 60 GB of memory (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/diffusiongemma.md",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "diffusion",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted",
        "experimental"
      ],
      "lastRelease": "2026-07-15",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.3,
        "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": 105,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 30,
          "interface": 70,
          "reach": 55,
          "risk": 70,
          "upkeep": 60
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.",
        "bestFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, safetensors only, hashed in Google's own organisation",
          "Generates 256-token blocks in parallel, which Google says reaches over 1,100 tokens per second per user on an H100 in FP8 (the publisher's claim)",
          "Gemma 4's chat template with multi-call tool use, thinking on or off per request and a 256K context",
          "Runs natively in released Transformers (5.11.0 and later), SGLang (v0.5.22 and later) and vLLM (v0.24.0 and later), with no repository code",
          "The card covers intended use, limitations, architecture, parameters, context, languages and a January 2025 cut-off"
        ],
        "weaknesses": [
          "No quantised build from Google, so the smallest official build needs the 64 GB band",
          "Not in llama.cpp, Ollama or LM Studio",
          "Google's own benchmarks put it below Gemma 4 26B A4B on most tasks, τ²-bench 56.2% against 68.2%",
          "Tool calling isn't documented for this model, and SGLang rejects JSON Schema and grammar constraints for it",
          "Labelled experimental, with no hosted API and no safety figures"
        ],
        "agentNotes": [
          "Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions",
          "In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler",
          "Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn",
          "Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself",
          "Streaming arrives one 256-token canvas at a time, not token by token",
          "Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.3
          }
        ],
        "editorialScores": {
          "capability": 30,
          "interface": 70,
          "reach": 55,
          "risk": 70,
          "upkeep": 60
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"transformers\u003e=5.11.0\" torch accelerate\npython -c \"from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor; m='google/diffusiongemma-26B-A4B-it'; p=AutoProcessor.from_pretrained(m); model=DiffusionGemmaForBlockDiffusion.from_pretrained(m, dtype='auto', device_map='auto')\"\n# or, with SGLang v0.5.22 or later:\nsglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"google/diffusiongemma-26B-A4B-it\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"max_tokens\": 1024}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/diffusiongemma-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B (architecture, per the card; no base_model declared)",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "Fast local text generation from text, image and video prompts at low concurrency, with optional thinking and tool calls, trading some quality against Gemma 4 26B A4B for speed.",
        "runtimes": [
          "SGLang",
          "Transformers",
          "vLLM"
        ],
        "builds": [
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.7,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "SGLang",
              "vLLM"
            ],
            "note": "The only build from Google. Google's guide asks for more than 60 GB of GPU memory"
          }
        ],
        "run": "sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000",
        "runBuild": "diffusiongemma-26B-A4B-it",
        "checked": "2026-11-04",
        "minMemoryGB": 64
      },
      "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-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "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": "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 (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026. gemma.ai links to huggingface.co/collections/google/diffusiongemma, which holds only this repository.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai's dated releases page has no DiffusionGemma entry. The Hugging Face commit history has four commits from 10 June to 15 July 2026 whose messages say what changed (two chat template fixes and the diffusers files).",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.json",
      "live": {
        "slug": "diffusiongemma-26b-a4b-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": "2026-11-10T15:42:54.469048104Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:44.487774347Z",
            "changedAt": "2026-11-10T16:22:44.487774347Z",
            "fingerprint": "e1a181f74521"
          }
        ],
        "updatedAt": "2026-11-10T16:22:44.487774347Z"
      }
    },
    "answer": "Qwen-AgentWorld-35B-A3B and DiffusionGemma 26B A4B IT score within a point of each other on agent readiness, 52.1 (D) and 51.3 (D). DiffusionGemma 26B A4B IT leads on risk, agent interface and 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": 20,
        "category": "interface",
        "leads": "diffusiongemma-26b-a4b-it",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10).",
          "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": 70,
          "b": 50
        }
      },
      {
        "by": 19,
        "category": "upkeep",
        "leads": "diffusiongemma-26b-a4b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10). The files last changed on 15 July 2026, 112 days before this check (10). Google organisation members answered 10 of the 20 discussions (10). It's a first release (5). File health gets 5 because the diffusers scheduler config disagrees with the card's sampling settings, though the files on the documented path agree.",
          "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": 60,
          "b": 41
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "diffusiongemma-26b-a4b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma.",
          "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": "2026-07-15",
        "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": "2.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen-AgentWorld-35B-A3B and DiffusionGemma 26B A4B IT score within a point of each other on agent readiness, 52.1 (D) and 51.3 (D). DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, DiffusionGemma 26B A4B IT or Qwen-AgentWorld-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 70 against 55",
          "Agent interface, 70 against 50",
          "Documentation and upkeep, 60 against 41"
        ],
        "also": null,
        "goodFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4",
        "slug": "diffusiongemma-26b-a4b-it",
        "watchFor": "No quantised build from Google, so the smallest official build needs the 64 GB band"
      },
      {
        "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": {
      "diffusiongemma-26b-a4b-it": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.3,
          "from": 50,
          "fromGrade": "D",
          "to": 51.3,
          "toGrade": "D"
        }
      ],
      "qwen-agentworld-35b-a3b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Agents-A1-4B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-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-diffusiongemma-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-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-diffusiongemma-26b-a4b-it.json",
        "title": "Apodex-1.1-mini vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-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-diffusiongemma-26b-a4b-it.json",
        "title": "Arete-27B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "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-diffusiongemma-26b-a4b-it.json",
        "title": "BigBang-v1 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "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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      {
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-2b-instruct"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b"
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b"
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        "qwen-agentworld-35b-a3b": 41,
        "weight": 10
      }
    ],
    "summary": "Qwen-AgentWorld-35B-A3B and DiffusionGemma 26B A4B IT score within a point of each other on agent readiness, 52.1 (D) and 51.3 (D). DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "diffusiongemma-26b-a4b-it": "A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.",
      "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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  "links": {
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    "html": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.min.md"
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  "markdown": "Qwen-AgentWorld-35B-A3B and DiffusionGemma 26B A4B IT score within a point of each other on agent readiness, 52.1 (D) and 51.3 (D). DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- DiffusionGemma 26B A4B IT: grade D, 51.3/100, rank #143 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-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### DiffusionGemma 26B A4B IT (D)\n\nGood for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4\n\nAhead on:\n- Risk, 70 against 55\n- Agent interface, 70 against 50\n- Documentation and upkeep, 60 against 41\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\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### Agent interface: DiffusionGemma 26B A4B IT +20\n\n- DiffusionGemma 26B A4B IT (70): The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10).\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: DiffusionGemma 26B A4B IT +19\n\n- DiffusionGemma 26B A4B IT (60): The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10). The files last changed on 15 July 2026, 112 days before this check (10). Google organisation members answered 10 of the 20 discussions (10). It's a first release (5). File health gets 5 because the diffusers scheduler config disagrees with the card's sampling settings, though the files on the documented path agree.\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: DiffusionGemma 26B A4B IT +15\n\n- DiffusionGemma 26B A4B IT (70): File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma.\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 | DiffusionGemma 26B A4B IT | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 55 | even |\n| Risk | 20% (20 this run) | 70 | 55 | DiffusionGemma 26B A4B IT +15 |\n| Agent interface | 10% (10 this run) | 70 | 50 | DiffusionGemma 26B A4B IT +20 |\n| Documentation and upkeep | 10% (10 this run) | 60 | 41 | DiffusionGemma 26B A4B IT +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.3 · D** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | DiffusionGemma 26B A4B 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 | 2026-07-15 | 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 | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### DiffusionGemma 26B A4B IT\n\n- +1.3 on 4 November 2026: correction\n\n### Qwen-AgentWorld-35B-A3B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**DiffusionGemma 26B A4B IT.** A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.\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### DiffusionGemma 26B A4B IT\n\n1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions\n2. In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler\n3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn\n4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself\n5. Streaming arrives one 256-token canvas at a time, not token by token\n6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs\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, DiffusionGemma 26B A4B IT or Qwen-AgentWorld-35B-A3B?\n\nQwen-AgentWorld-35B-A3B and DiffusionGemma 26B A4B IT score within a point of each other on agent readiness, 52.1 (D) and 51.3 (D). DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"diffusiongemma-26b-a4b-it\", \"b\": \"qwen-agentworld-35b-a3b\"}`. 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    "title": "DiffusionGemma 26B A4B IT vs Qwen-AgentWorld-35B-A3B (2026)",
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