{
  "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 scores 51.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.",
    "b": {
      "slug": "kimi-k3-1-pro",
      "name": "Kimi K3.1 Pro",
      "vendor": "Moonshot AI",
      "vendorUrl": "https://www.moonshot.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kimi K3.1 Pro is Moonshot AI's open-weight multimodal mixture-of-experts model, post-trained from Kimi K3, with 2.8T parameters, 104B active and a 1M-token context, published in MXFP4 under the modified-MIT Kimi K3.1 Pro Licence.",
      "url": "https://www.anchorterminal.com/tools/kimi-k3-1-pro",
      "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json",
      "dns": "kimi-k3-1-pro.grade.anchorterminal.com",
      "repo": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
      "license": "Kimi K3.1 Pro License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "moonshotai/Kimi-K3.1-Pro"
        }
      ],
      "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 the Kimi K3.1 Pro Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (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/moonshotai/Kimi-K3.1-Pro",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "modified-mit",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-11-05",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 48.6,
        "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": 109,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.",
        "bestFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API",
        "strengths": [
          "Published weights for a model Moonshot calls its most capable, with a 1M-token context and images in",
          "Free commercial use under a modified MIT licence, ungated, with a hash on every file",
          "Moonshot's API and Together serve the same model for agents without the hardware",
          "reasoning_effort per request and several tool calls a turn",
          "vLLM and SGLang both publish serving recipes for it"
        ],
        "weaknesses": [
          "1,561 GB of files, so it needs a node of eight large accelerators at least",
          "Only llama.cpp has the model code in a release, and no GGUF is published",
          "No chat template, and prompts are built by code from the repository",
          "No limitations, safety guidance or security contact on the card, and no independent safety evaluation yet",
          "None of the newest discussions has a reply from Moonshot"
        ],
        "agentNotes": [
          "Use Moonshot's API or Together unless you have a node of eight B300-class accelerators",
          "With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`",
          "Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires",
          "Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off",
          "Kimi K3's model code doesn't load it, because the attention layers changed",
          "Validate JSON output yourself, since the card doesn't document structured output for the weights"
        ],
        "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": 48.6
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 40,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 70,
        "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, per its Kimi-K3.1-Pro recipe linked from the card (vLLM 0.34.0 or later, two GB300 trays)\nvllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code \\\n  --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP \\\n  --load-format fastsafetensors --enable-auto-tool-choice \\\n  --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"moonshotai/Kimi-K3.1-Pro\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"reasoning_effort\": \"high\"\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kimi-k3-1-pro"
      },
      "sameCompany": [
        "kimi-k3"
      ],
      "weights": {
        "trainedBy": "Moonshot AI",
        "builtOn": "Kimi K3, post-trained, per the card. Not declared in the metadata",
        "params": "2.8T",
        "active": "104B",
        "context": 1048576,
        "goodFor": "Large multimodal agent model for coding, research and long tool-calling sessions, text and images in, with always-on reasoning you can budget.",
        "runtimes": [
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Kimi-K3.1-Pro",
            "url": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 1561.3,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Trained in MXFP4, the only build Moonshot publishes. vLLM's recipe needs 0.34.0 or later"
          }
        ],
        "run": "vllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP --load-format fastsafetensors --enable-auto-tool-choice --tool-call-parser kimi_k3 --reasoning-parser kimi_k3",
        "runBuild": "Kimi-K3.1-Pro",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Moonshot AI (Kimi)",
        "family": "Kimi K3.1",
        "url": "https://www.anchorterminal.com/open-weights/#moonshot",
        "olderCount": 18
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)",
        "domain": "moonshot.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "The .ai registry's RDAP server gave moonshot.ai a registration date of 15 December 2017, read 24 October 2026 for the Kimi K3 listing.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Moonshot AI. The Kimi Platform's terms name Moonshot AI PTE. LTD. as the party for its API, as the Kimi K3 listing records.",
          "Nothing is hosted, so the Kimi K3.1 Pro Licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, two commits, the first on 2 November 2026 and an update to encoding_k3.py on 5 November.",
          "No security.txt was found on moonshot.ai or kimi.com for the Kimi K3 listing on 24 October 2026. unchecked on 10 November."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.json"
    },
    "edges": [
      {
        "by": 20,
        "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 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50."
        },
        "scores": {
          "a": 70,
          "b": 50
        }
      },
      {
        "by": 15,
        "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 only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40."
        },
        "scores": {
          "a": 55,
          "b": 40
        }
      },
      {
        "by": 9,
        "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, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51."
        },
        "scores": {
          "a": 60,
          "b": 51
        }
      },
      {
        "by": 5,
        "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 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65."
        },
        "scores": {
          "a": 70,
          "b": 65
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Moonshot AI",
        "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": "Kimi K3.1 Pro License (modified MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-15",
        "b": "2026-11-05",
        "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": "DiffusionGemma 26B A4B IT scores 51.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, DiffusionGemma 26B A4B IT or Kimi K3.1 Pro?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 55 against 40",
          "Risk, 70 against 50",
          "Agent interface, 70 against 65",
          "Documentation and upkeep, 60 against 51"
        ],
        "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": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API",
        "slug": "kimi-k3-1-pro",
        "watchFor": "1,561 GB of files, so it needs a node of eight large accelerators at least"
      }
    ],
    "history": {
      "diffusiongemma-26b-a4b-it": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.3,
          "from": 50,
          "fromGrade": "D",
          "to": 51.3,
          "toGrade": "D"
        }
      ],
      "kimi-k3-1-pro": 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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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro.json",
        "title": "Agents-A1-4B vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3-1-pro"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it"
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro"
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      {
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3-1-pro"
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      {
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      },
      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3-1-pro"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-diffusiongemma-26b-a4b-it.json",
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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-kimi-k3-1-pro"
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      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3-1-pro"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0.json",
        "title": "Kimi K3.1 Pro vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.json",
        "title": "Kimi K3.1 Pro vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think.json",
        "title": "Kimi K3.1 Pro vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b.json",
        "title": "Kimi K3.1 Pro vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.json",
        "title": "Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Kimi K3.1 Pro vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507.json",
        "title": "Kimi K3.1 Pro vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b"
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      {
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-4b"
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        "title": "Kimi K3.1 Pro vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-6-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next.json",
        "title": "Kimi K3.1 Pro vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.json",
        "title": "Kimi K3.1 Pro vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview.json",
        "title": "Kimi K3.1 Pro vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.json",
        "title": "Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b.json",
        "title": "Kimi K3.1 Pro vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.json",
        "title": "Kimi K3.1 Pro vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.json",
        "title": "Kimi K3.1 Pro vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.json",
        "title": "Kimi K3.1 Pro vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json",
        "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json",
        "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json",
        "title": "Kimi K3.1 Pro vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.json",
        "title": "Kimi K3 vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "diffusiongemma-26b-a4b-it": 55,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "reach",
        "kimi-k3-1-pro": 40,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 20,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "risk",
        "kimi-k3-1-pro": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "diffusiongemma-26b-a4b-it": 70,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "interface",
        "kimi-k3-1-pro": 65,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 9,
        "diffusiongemma-26b-a4b-it": 60,
        "edge": "diffusiongemma-26b-a4b-it",
        "key": "upkeep",
        "kimi-k3-1-pro": 51,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "DiffusionGemma 26B A4B IT scores 51.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. 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.",
      "kimi-k3-1-pro": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "markdown": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3-1-pro.md",
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  "markdown": "DiffusionGemma 26B A4B IT scores 51.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. 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- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.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 40\n- Risk, 70 against 50\n- Agent interface, 70 against 65\n- Documentation and upkeep, 60 against 51\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\n\n### Kimi K3.1 Pro (D)\n\nGood for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n\n## Why the edge\n\n### Risk: DiffusionGemma 26B A4B IT +20\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- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.\n\n### Reach: DiffusionGemma 26B A4B IT +15\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- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.\n\n### Documentation and upkeep: DiffusionGemma 26B A4B IT +9\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- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.\n\n### Agent interface: DiffusionGemma 26B A4B IT +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- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.\n\n## Score by category\n\n| Category | Weight | DiffusionGemma 26B A4B IT | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 40 | DiffusionGemma 26B A4B IT +15 |\n| Risk | 20% (20 this run) | 70 | 50 | DiffusionGemma 26B A4B IT +20 |\n| Agent interface | 10% (10 this run) | 70 | 65 | DiffusionGemma 26B A4B IT +5 |\n| Documentation and upkeep | 10% (10 this run) | 60 | 51 | DiffusionGemma 26B A4B IT +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.3 · D** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | DiffusionGemma 26B A4B IT | Kimi K3.1 Pro |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Moonshot AI |\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 | Kimi K3.1 Pro License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-15 | 2026-11-05 |\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### Kimi K3.1 Pro\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**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.\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### Kimi K3.1 Pro\n\n1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators\n2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`\n3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires\n4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off\n5. Kimi K3's model code doesn't load it, because the attention layers changed\n6. Validate JSON output yourself, since the card doesn't document structured output for the weights\n\n## Questions\n\n### Which is better for AI agents, DiffusionGemma 26B A4B IT or Kimi K3.1 Pro?\n\nDiffusionGemma 26B A4B IT scores 51.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"diffusiongemma-26b-a4b-it\", \"b\": \"kimi-k3-1-pro\"}`. 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