{
  "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": "DiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other on agent readiness, 51.3 (D) and 50.5 (D). LongCat-2.5 leads on agent interface.",
    "b": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (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/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "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": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "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": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "reach",
        "leads": "diffusiongemma-26b-a4b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it.",
          "b": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35."
        },
        "scores": {
          "a": 55,
          "b": 35
        }
      },
      {
        "by": 14,
        "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, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46."
        },
        "scores": {
          "a": 60,
          "b": 46
        }
      },
      {
        "by": 10,
        "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, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "longcat-2-5",
        "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 (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75."
        },
        "scores": {
          "a": 70,
          "b": 75
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Meituan (LongCat)",
        "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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-15",
        "b": "2026-11-10",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "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": "DiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other on agent readiness, 51.3 (D) and 50.5 (D). LongCat-2.5 leads on agent interface.",
        "question": "Which is better for AI agents, DiffusionGemma 26B A4B IT or LongCat-2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 55 against 35",
          "Risk, 70 against 60",
          "Documentation and upkeep, 60 against 46"
        ],
        "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": [
          "Agent interface, 75 against 70"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      }
    ],
    "history": {
      "diffusiongemma-26b-a4b-it": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.3,
          "from": 50,
          "fromGrade": "D",
          "to": 51.3,
          "toGrade": "D"
        }
      ],
      "longcat-2-5": 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",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json",
        "title": "Agents-A1-4B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
      },
      {
        "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-longcat-2-5.json",
        "title": "Apertus v1.5 8B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5"
      },
      {
        "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-longcat-2-5.json",
        "title": "Apodex-1.1-mini vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5"
      },
      {
        "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-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
      },
      {
        "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-longcat-2-5.json",
        "title": "BigBang-v1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Carbon-X vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.json",
        "title": "Carbon-X vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Cola2 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-diffusiongemma-26b-a4b-it.json",
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-diffusiongemma-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b"
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        "title": "LongCat-2.5 vs Nemotron 3 Nano 30B A3B",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LongCat-2.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.json",
        "title": "LongCat-2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json",
        "title": "LongCat-2.5 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json",
        "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-2B",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.json",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "diffusiongemma-26b-a4b-it": 55,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "reach",
        "longcat-2-5": 35,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "risk",
        "longcat-2-5": 60,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "longcat-2-5",
        "key": "interface",
        "longcat-2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 14,
        "diffusiongemma-26b-a4b-it": 60,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "upkeep",
        "longcat-2-5": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "DiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other on agent readiness, 51.3 (D) and 50.5 (D). LongCat-2.5 leads on agent interface. 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.",
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up."
    }
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  "markdown": "DiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other on agent readiness, 51.3 (D) and 50.5 (D). LongCat-2.5 leads on agent interface. 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- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.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- Reach, 55 against 35\n- Risk, 70 against 60\n- Documentation and upkeep, 60 against 46\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Reach: DiffusionGemma 26B A4B IT +20\n\n- DiffusionGemma 26B A4B IT (55): Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it.\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n### Documentation and upkeep: DiffusionGemma 26B A4B IT +14\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- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Risk: DiffusionGemma 26B A4B IT +10\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- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n\n### Agent interface: LongCat-2.5 +5\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- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n\n## Score by category\n\n| Category | Weight | DiffusionGemma 26B A4B IT | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 35 | DiffusionGemma 26B A4B IT +20 |\n| Risk | 20% (20 this run) | 70 | 60 | DiffusionGemma 26B A4B IT +10 |\n| Agent interface | 10% (10 this run) | 70 | 75 | LongCat-2.5 +5 |\n| Documentation and upkeep | 10% (10 this run) | 60 | 46 | DiffusionGemma 26B A4B IT +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.3 · D** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | DiffusionGemma 26B A4B IT | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Meituan (LongCat) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-15 | 2026-11-10 |\n| Terms last updated | no document linked | not read yet |\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### LongCat-2.5\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**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\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### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, DiffusionGemma 26B A4B IT or LongCat-2.5?\n\nDiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other on agent readiness, 51.3 (D) and 50.5 (D). LongCat-2.5 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"diffusiongemma-26b-a4b-it\", \"b\": \"longcat-2-5\"}`. 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    "description": "DiffusionGemma 26B A4B IT and LongCat-2.5 score within a point of each other for open-weight models you run yourself, 51.3 and 50.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "DiffusionGemma 26B A4B IT vs LongCat-2.5 for AI agents (2026)",
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