{
  "data": {
    "a": {
      "slug": "glm-5-3",
      "name": "GLM-5.3",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-5.3 is Z.ai's open-weight mixture-of-experts model for coding and long-horizon agent work, 744B parameters with 40B active and a 1M-token context, published on Hugging Face in FP8 and BF16 under its own licence.",
      "url": "https://www.anchorterminal.com/tools/glm-5-3",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3.json",
      "repo": "https://huggingface.co/zai-org/GLM-5.3",
      "license": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-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 GLM-5.3 License. A licensee running a model-as-a-service business with more than US$10 billion of revenue in 12 months must pass Z.AI's security review before commercial use. Running it yourself costs the hardware, 755.7 GB of FP8 files (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",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "long-context",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-10-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.6,
        "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": 14,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 80,
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.",
        "bestFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider",
        "strengths": [
          "Model code in released llama.cpp b11768, vLLM 0.28.0, SGLang 0.5.22 and Transformers 5.12.0",
          "1,048,576 tokens of native context and three reasoning budgets per request",
          "Template renders several tool calls per turn, parsed by glm47 in vLLM and SGLang",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard",
          "Downloadable with no account, and hosted by Z.ai and five live providers on Hugging Face"
        ],
        "weaknesses": [
          "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build",
          "No safety evaluation, agent-use guidance or security contact",
          "Thinking is always on, and reasoning_effort only lowers it",
          "Commercial use by model-as-a-service operators above US$10 billion revenue needs Z.AI's security review",
          "The card leaves out limitations, architecture, parameters and cut-off, and zai-org answered 2 of 16 discussions, none since 8 September"
        ],
        "agentNotes": [
          "Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)",
          "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text",
          "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max",
          "Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default",
          "Don't send `enable_thinking: false`; the template ignores it and always opens a think block",
          "Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API",
          "No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe"
        ],
        "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": 64.6
          }
        ],
        "editorialScores": {
          "capability": 80,
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 58
        },
        "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": "# vLLM's recipe, FP8 on one 8xH200 node\nuv pip install \"vllm\u003e=0.28.0\" --torch-backend=auto\nvllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 \\\n  --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 \\\n  --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice \\\n  --served-model-name glm-5.3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-5.3\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"reasoning_effort\": \"low\", \"clear_thinking\": true}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-5-3"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-4-7-flash",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "builtOn": "GLM-5.2's base model (same base, new post-training)",
        "params": "744B",
        "active": "40B",
        "context": 1048576,
        "goodFor": "Large model for coding and long-horizon agent work with tool calling and always-on reasoning, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "GLM-5.3",
            "url": "https://huggingface.co/zai-org/GLM-5.3",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 755.6,
            "memoryGB": 1024,
            "format": "safetensors"
          },
          {
            "name": "GLM-5.3-BF16",
            "url": "https://huggingface.co/zai-org/GLM-5.3-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 1506.7,
            "memoryGB": 2048,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3",
        "runBuild": "GLM-5.3",
        "checked": "2026-10-21",
        "minMemoryGB": 1024
      },
      "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-Flash",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash",
            "params": "320B-A18B",
            "listing": "https://www.anchorterminal.com/tools/glm-5-3-flash"
          }
        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai). The licence names Z.AI",
        "domain": "z.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-5.3/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Z.ai's terms of use name JINGSHENG HENGXING TECHNOLOGY PTE.LTD as the operator of Z.ai, and RDAP names the same company as the registrant of z.ai. The weights' LICENSE names Z.AI as the copyright holder.",
          "Nothing is hosted for this listing, so the GLM-5.3 License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 11 commits from 27 August to 14 October 2026, whose messages say what changed (two chat template fixes, on 4 September and 14 October, and evaluation results).",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3.json"
    },
    "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk.",
    "b": {
      "slug": "olmo-3-7b-think",
      "name": "Olmo 3 7B Think",
      "vendor": "Ai2 (Allen Institute for AI)",
      "vendorUrl": "https://allenai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Olmo 3 7B Think is Ai2's fully open 7B reasoning model for maths, code and step-by-step answers, with training data, code and checkpoints published, a 65,536-token context and thinking always on, released in November 2025 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/olmo-3-7b-think",
      "markdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json",
      "repo": "https://huggingface.co/allenai/Olmo-3-7B-Think",
      "license": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "allenai/Olmo-3-7B-Think"
        },
        {
          "registry": "ollama",
          "name": "olmo-3:7b-think"
        }
      ],
      "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. The card says it is intended for research and educational use in line with Ai2's Responsible Use Guidelines, which list disallowed uses outside research. Running it yourself costs the hardware, 14.6 GB in BF16 and 4.5 GB for Ollama's 4-bit build (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/allenai/Olmo-3-7B-Think",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "fully-open",
        "open-data",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-06-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.3,
        "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": 1197,
        "categoryRank": 69,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.",
        "bestFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "strengths": [
          "Training data, code, checkpoints and evaluation harness all published, the data with sources and sizes",
          "Safety results published with a harness to rerun them",
          "Ollama's 4-bit build fits in about 5.4 GB, and released llama.cpp, Ollama, vLLM, LM Studio and Transformers run it",
          "Safetensors only with no repository code, a checksum on every file and no gate",
          "Apache 2.0 with no revenue threshold"
        ],
        "weaknesses": [
          "Not trained for tool calling, and the tool format lives only in the template",
          "Thinking is always on with no way to turn it off, and 65,536 tokens of context",
          "Ai2 publishes no quantised build and no provider hosts it",
          "The card binds use to Ai2's Responsible Use Guidelines, which Ai2 can change",
          "No Ai2 reply in the discussions, and the template's default prompt contradicts the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB",
          "With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend",
          "Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long",
          "Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags",
          "Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available",
          "For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling",
          "A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens",
          "Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.3
          }
        ],
        "editorialScores": {
          "capability": 28,
          "interface": 35,
          "reach": 75,
          "risk": 75,
          "upkeep": 52
        },
        "provenanceScore": 71,
        "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 olmo-3:7b-think\n# or\nvllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"olmo-3:7b-think\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"temperature\": 0.6, \"top_p\": 0.95}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/olmo-3-7b-think"
      },
      "weights": {
        "trainedBy": "Ai2 (Allen Institute for AI)",
        "builtOn": "allenai/Olmo-3-7B-Think-DPO",
        "params": "7.3B",
        "context": 65536,
        "goodFor": "Fully open 7B reasoning model for maths, code and step-by-step answers in English, with published training data and checkpoints for research, always thinking, not trained for tool calls.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama olmo-3:7b-think-q4_K_M",
            "url": "https://ollama.com/library/olmo-3:7b-think-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Digest b8d4c92ac9c1, also the 7b-think and latest tags. q8_0 is 7.8 GB and fp16 15 GB. Sizes as Ollama's tags page shows them"
          },
          {
            "name": "LM Studio allenai/olmo-3-7b-think (Q4_K_M GGUF)",
            "url": "https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 4.5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Olmo-3-7B-Think-Q4_K_M.gguf, 4.47 GB from the Hub's file list. The catalogue entry also lists Q6_K, Q8_0 and MLX 4, 6 and 8-bit builds; the MLX 4-bit is 4.12 GB (Mac only)"
          },
          {
            "name": "Olmo-3-7B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 14.6,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536",
        "runBuild": "Olmo-3-7B-Think",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Ai2 (Allen Institute for AI)",
        "family": "Olmo 3 (3, 3.1)",
        "url": "https://www.anchorterminal.com/open-weights/#ai2",
        "others": [
          {
            "name": "Olmo-3.1-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3.1-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Mix",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Mix",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3.1-32B-Instruct-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-DPO",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Math",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Math",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-IF",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-IF",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-General",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-General",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-RL-Zero-Code",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Code",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-7B-Instruct-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-32B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-SFT",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-32B-Think-DPO",
            "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-DPO",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-1125-32B",
            "url": "https://huggingface.co/allenai/Olmo-3-1125-32B",
            "params": "32B (Hub safetensors count 32.23B)"
          },
          {
            "name": "Olmo-3-7B-Think-SFT",
            "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-SFT",
            "params": "7B (Hub safetensors count 7.30B)"
          },
          {
            "name": "Olmo-3-1025-7B",
            "url": "https://huggingface.co/allenai/Olmo-3-1025-7B",
            "params": "7B (Hub safetensors count 7.30B)"
          }
        ],
        "olderCount": 95,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "The Allen Institute for Artificial Intelligence (Ai2), a 501(c)(3) nonprofit, per its Responsible Use Guidelines",
        "domain": "allenai.org",
        "domainRegistered": "2010-12-03",
        "endpointOnVendorDomain": null,
        "terms": "https://allenai.org/terms",
        "privacy": "https://allenai.org/privacy-policy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "expired",
        "checked": "2026-10-21",
        "notes": [
          "RDAP for allenai.org gives a registration date of 3 December 2010.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. Ai2's guidelines say its website terms also govern use of its tools.",
          "allenai.org serves a security.txt with a contact and an Expires date of 31 July 2026, which has passed. The Responsible Use Guidelines name a channel for bugs and security concerns with Ai2's models.",
          "No changelog. The commits are card edits, two configuration fixes from pull requests in November and December 2025, and a parameter-count fix on 25 June 2026.",
          "allenai.org links to huggingface.co/allenai, so the repository is the publisher's own."
        ],
        "score": 71
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.json"
    },
    "facts": [
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        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "Ai2 (Allen Institute for 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": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)",
        "b": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
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      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
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      {
        "a": "2026-10-14",
        "b": "2026-06-25",
        "name": "Last release"
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      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "",
        "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": "2.8/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
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    ],
    "faq": [
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        "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk.",
        "question": "Which is better for AI agents, GLM-5.3 or Olmo 3 7B Think?"
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    ],
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        "aheadOn": [
          "Capability, 80 against 28",
          "Agent interface, 70 against 35",
          "Documentation and upkeep, 58 against 52"
        ],
        "also": null,
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        "slug": "glm-5-3",
        "watchFor": "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build"
      },
      {
        "aheadOn": [
          "Reach, 75 against 55",
          "Risk, 75 against 50"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand",
        "slug": "olmo-3-7b-think",
        "watchFor": "Not trained for tool calling, and the tool format lives only in the template"
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    ],
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3"
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Olmo 3 7B Think vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507.json",
        "title": "Olmo 3 7B Think vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b.json",
        "title": "Olmo 3 7B Think vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next.json",
        "title": "Olmo 3 7B Think vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.json",
        "title": "Olmo 3 7B Think vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.json",
        "title": "Olmo 3 7B Think vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.json",
        "title": "Olmo 3 7B Think vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 52,
        "edge": "glm-5-3",
        "glm-5-3": 80,
        "key": "capability",
        "name": "Capability",
        "olmo-3-7b-think": 28,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "olmo-3-7b-think",
        "glm-5-3": 55,
        "key": "reach",
        "name": "Reach",
        "olmo-3-7b-think": 75,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "olmo-3-7b-think",
        "glm-5-3": 50,
        "key": "risk",
        "name": "Risk",
        "olmo-3-7b-think": 75,
        "weight": 20
      },
      {
        "by": 35,
        "edge": "glm-5-3",
        "glm-5-3": 70,
        "key": "interface",
        "name": "Agent interface",
        "olmo-3-7b-think": 35,
        "weight": 10
      },
      {
        "by": 6,
        "edge": "glm-5-3",
        "glm-5-3": 58,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "olmo-3-7b-think": 52,
        "weight": 10
      }
    ],
    "summary": "GLM-5.3 scores 64.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "glm-5-3": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.",
      "olmo-3-7b-think": "A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think",
    "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.min.md"
  },
  "markdown": "GLM-5.3 scores 64.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk. Both do open-weight models you run yourself.\n\n- GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.json\n- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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### GLM-5.3 (B)\n\nGood for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider\n\nAhead on:\n- Capability, 80 against 28\n- Agent interface, 70 against 35\n- Documentation and upkeep, 58 against 52\n\nWatch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build\n\n### Olmo 3 7B Think (D)\n\nGood for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand\n\nAhead on:\n- Reach, 75 against 55\n- Risk, 75 against 50\n\nWatch for: Not trained for tool calling, and the tool format lives only in the template\n\n\n## Score by category\n\n| Category | Weight | GLM-5.3 | Olmo 3 7B Think | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 80 | 28 | GLM-5.3 +52 |\n| Reach | 25% (25 this run) | 55 | 75 | Olmo 3 7B Think +20 |\n| Risk | 20% (20 this run) | 50 | 75 | Olmo 3 7B Think +25 |\n| Agent interface | 10% (10 this run) | 70 | 35 | GLM-5.3 +35 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 52 | GLM-5.3 +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.6 · B** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3 | Olmo 3 7B Think |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | Ai2 (Allen Institute for 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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-06-25 |\n| Terms last updated | no document linked |  |\n| Privacy policy last updated | 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 | 2.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.\n\n**Olmo 3 7B Think.** A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.\n\n## Before you call either\n\n### GLM-5.3\n\n1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default\n5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block\n6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API\n7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe\n\n### Olmo 3 7B Think\n\n1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB\n2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend\n3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long\n4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags\n5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available\n6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling\n7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens\n8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3 or Olmo 3 7B Think?\n\nGLM-5.3 scores 64.6 (B) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 5 scored categories. Olmo 3 7B Think leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3\", \"b\": \"olmo-3-7b-think\"}`. From a terminal: `anchor compare glm-5-3 olmo-3-7b-think`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json\n\n## Other comparisons with GLM-5.3 or Olmo 3 7B Think\n\n- [Agents-A1-4B vs GLM-5.3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.md)\n- [Agents-A1-4B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.md)\n- [Apertus v1.5 8B vs GLM-5.3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.md)\n- [Apertus v1.5 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.md)\n- [Apodex-1.1-mini vs GLM-5.3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.md)\n- [Apodex-1.1-mini vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.md)\n- [BigBang-v1 vs GLM-5.3](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.md)\n- [BigBang-v1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think.md)\n- [Cosmos-Reason2-2B vs GLM-5.3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3.md)\n- [Cosmos-Reason2-2B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-olmo-3-7b-think.md)\n- [Darwin-36B-Opus vs GLM-5.3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.md)\n- [Darwin-36B-Opus vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-olmo-3-7b-think.md)\n- [DeepSeek-V3.2 vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.md)\n- [DeepSeek-V3.2 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B 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