{
  "data": {
    "a": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 per Google (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://gemma.ai/models/gemma-4/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "grade": "BB",
        "agentReady": true,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 2,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 17 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-31B",
            "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": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M"
          }
        ],
        "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://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (Gemma 4 on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.json"
    },
    "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability.",
    "b": {
      "slug": "glm-5-3-flash",
      "name": "GLM-5.3-Flash",
      "vendor": "Z.ai",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.",
      "url": "https://www.anchorterminal.com/tools/glm-5-3-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json",
      "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-Flash"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-Flash-BF16"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (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://huggingface.co/zai-org/GLM-5.3-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-08-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 65.1,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 13,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 75,
          "interface": 70,
          "reach": 60,
          "risk": 55,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.",
        "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider",
        "strengths": [
          "MIT weights with no use policy, downloadable with no account",
          "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard",
          "1,048,576 tokens of native context and several tool calls per turn in the chat template",
          "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware"
        ],
        "weaknesses": [
          "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists",
          "No safety evaluation, agent-use guidance or security contact",
          "Thinking is always on, and reasoning_effort only lowers it to low",
          "The tool-call format lives only in the chat template, with the parser named by the runtimes",
          "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September"
        ],
        "agentNotes": [
          "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)",
          "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs",
          "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max",
          "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block",
          "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt",
          "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 65.1
          }
        ],
        "editorialScores": {
          "capability": 75,
          "interface": 70,
          "reach": 60,
          "risk": 55,
          "upkeep": 58
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n  --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-5-3-flash"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-4-7-flash",
        "glm-5-3"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "320B",
        "active": "18B",
        "context": 1048576,
        "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "GLM-5.3-Flash-FP4-Local",
            "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 177.5,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "GLM-5.3-Flash-NVFP4",
            "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 204.4,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "GLM-5.3-Flash",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 328.3,
            "memoryGB": 512,
            "format": "safetensors"
          },
          {
            "name": "GLM-5.3-Flash-BF16",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 642.7,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47",
        "runBuild": "GLM-5.3-Flash",
        "checked": "2026-10-21",
        "minMemoryGB": 256
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-5",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-5",
            "url": "https://huggingface.co/zai-org/GLM-5",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.1",
            "url": "https://huggingface.co/zai-org/GLM-5.1",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.2",
            "url": "https://huggingface.co/zai-org/GLM-5.2",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.3",
            "url": "https://huggingface.co/zai-org/GLM-5.3",
            "params": "744B-A40B",
            "listing": "https://www.anchorterminal.com/tools/glm-5-3"
          }
        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)",
        "domain": "z.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Z.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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "b": "2026-08-26",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.8/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability.",
        "question": "Which is better for AI agents, Gemma 4 26B A4B IT or GLM-5.3-Flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 60",
          "Risk, 70 against 55",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 65 against 58"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Rated higher by the reviewer agents, 3.8 against 2.8 out of 5"
        ],
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": [
          "Capability, 75 against 59"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider",
        "slug": "glm-5-3-flash",
        "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json",
        "title": "Agents-A1-4B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json",
        "title": "Apertus v1.5 8B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.json",
        "title": "Apodex-1.1-mini vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json",
        "title": "BigBang-v1 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.json",
        "title": "Cosmos-Reason2-2B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.json",
        "title": "Darwin-36B-Opus vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.json",
        "title": "Darwin-36B-Opus vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.json",
        "title": "DeepSeek-V3.2 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.json",
        "title": "DeepSeek V4.1 Flash vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.json",
        "title": "DiffusionGemma 26B A4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "Gemma 4 12B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 12B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.json",
        "title": "Gemma 4 26B A4B IT vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.json",
        "title": "Gemma 4 26B A4B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.json",
        "title": "Gemma 4 26B A4B IT vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.json",
        "title": "Gemma 4 26B A4B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.json",
        "title": "Gemma 4 26B A4B IT vs Inkling",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.json",
        "title": "Gemma 4 26B A4B IT vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json",
        "title": "Gemma 4 26B A4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
        "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 26B A4B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Gemma 4 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5.json",
        "title": "Gemma 4 26B A4B IT vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.json",
        "title": "Gemma 4 26B A4B IT vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b"
      },
      {
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.json",
        "title": "GLM-5.3-Flash vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-olmo-3-7b-think.json",
        "title": "GLM-5.3-Flash vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.json",
        "title": "GLM-5.3-Flash vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.json",
        "title": "GLM-5.3-Flash vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.json",
        "title": "GLM-5.3-Flash vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "GLM-5.3-Flash vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507.json",
        "title": "GLM-5.3-Flash vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.json",
        "title": "GLM-5.3-Flash vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.json",
        "title": "GLM-5.3-Flash vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.json",
        "title": "GLM-5.3-Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.json",
        "title": "GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.json",
        "title": "GLM-5.3-Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.json",
        "title": "GLM-5.3-Flash vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash.json",
        "title": "GLM-5.3 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash"
      }
    ],
    "scores": [
      {
        "by": 16,
        "edge": "glm-5-3-flash",
        "gemma-4-26b-a4b-it": 59,
        "glm-5-3-flash": 75,
        "key": "capability",
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 30,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 90,
        "glm-5-3-flash": 60,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 70,
        "glm-5-3-flash": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 85,
        "glm-5-3-flash": 70,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 7,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 65,
        "glm-5-3-flash": 58,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
      "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash",
    "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.min.md"
  },
  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Reach, 90 against 60\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 65 against 58\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5\n\nWatch for: No safety evaluation figures published\n\n### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nAhead on:\n- Capability, 75 against 59\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | GLM-5.3-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 59 | 75 | GLM-5.3-Flash +16 |\n| Reach | 25% (25 this run) | 90 | 60 | Gemma 4 26B A4B IT +30 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 26B A4B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 26B A4B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 58 | Gemma 4 26B A4B IT +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **65.1 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | GLM-5.3-Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Z.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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-20 | 2026-08-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\n\n**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\n\n## Before you call either\n\n### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Validate tool names and arguments before running them, and check any JSON yourself\n\n### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 26B A4B IT or GLM-5.3-Flash?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"glm-5-3-flash\"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it glm-5-3-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json\n\n## Other comparisons with Gemma 4 26B A4B IT or GLM-5.3-Flash\n\n- [Agents-A1-4B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.md)\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- 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    "title": "Gemma 4 26B A4B IT vs GLM-5.3-Flash for AI agents (2026)",
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