{
  "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": "gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep.",
    "b": {
      "slug": "gpt-oss-120b",
      "name": "gpt-oss-120b",
      "vendor": "OpenAI",
      "vendorUrl": "https://openai.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "gpt-oss-120b is OpenAI's open-weight mixture-of-experts reasoning model, 116.8B parameters with 5.1B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gpt-oss-120b",
      "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json",
      "repo": "https://huggingface.co/openai/gpt-oss-120b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openai/gpt-oss-120b"
        },
        {
          "registry": "pypi",
          "name": "gpt-oss"
        },
        {
          "registry": "pypi",
          "name": "openai-harmony"
        },
        {
          "registry": "ollama",
          "name": "gpt-oss:120b"
        }
      ],
      "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 with a one-line usage policy asking users to comply with applicable law. OpenAI's API page for the model shows no price and rate limits of 0 at every tier. Running it yourself costs the hardware, 65.2 GB for the MXFP4 files and 65 GB for Ollama's gpt-oss:120b 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/openai/gpt-oss-120b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2025-08-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 70.2,
        "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": 4,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 95,
          "upkeep": 45
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.",
        "bestFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support",
        "strengths": [
          "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama, with LM Studio linked from the card",
          "Apache 2.0, no gate, and 11 inference providers serve it for agents without the hardware",
          "Safetensors only, a trained instruction hierarchy and an outside biosecurity evaluation",
          "Harmony tool format, JSON Schema output and three reasoning levels documented by OpenAI"
        ],
        "weaknesses": [
          "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision",
          "One tool call at a time",
          "No OpenAI reply in the last 40 discussions, including open template and tokenizer reports",
          "No change to the files since August 2025 or to the model code since November 2025",
          "Reasoning can be lowered but not turned off"
        ],
        "agentNotes": [
          "Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits",
          "On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs",
          "With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony",
          "Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise",
          "Set `Reasoning: low` in the system message for short tasks, medium is the default",
          "Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users",
          "Expect one tool call per turn and loop, don't wait for parallel calls"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 70.2
          }
        ],
        "editorialScores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 95,
          "upkeep": 45
        },
        "provenanceScore": 87,
        "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 gpt-oss:120b\n# or, with vLLM's recipe:\nvllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gpt-oss:120b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gpt-oss-120b"
      },
      "sameCompany": [
        "openai-api",
        "openai-embeddings",
        "openai-guardrails",
        "openai-moderation",
        "openai-image-api",
        "openai-sora",
        "openai-speech-to-text",
        "gpt-live",
        "openai-realtime",
        "openai-agents-sdk",
        "openai-decisions-api",
        "openai-codex",
        "gpt-oss-20b"
      ],
      "weights": {
        "trainedBy": "OpenAI",
        "params": "116.83B",
        "active": "5.13B",
        "context": 131072,
        "goodFor": "Larger text reasoning model for agents and coding, with function calling, built-in browser and Python tools and three reasoning levels, in the harmony format.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gpt-oss-120b",
            "url": "https://huggingface.co/openai/gpt-oss-120b",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 65.2,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "MoE weights in MXFP4, the rest BF16. original/ and metal/ duplicate the weights. vLLM's gpt-oss recipe doesn't list gfx1151"
          },
          {
            "name": "ollama gpt-oss:120b",
            "url": "https://ollama.com/library/gpt-oss:120b",
            "who": "ollama",
            "precision": "mxfp4",
            "sizeGB": 65,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          }
        ],
        "run": "ollama run gpt-oss:120b",
        "runBuild": "ollama gpt-oss:120b",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "OpenAI",
        "family": "gpt-oss",
        "url": "https://www.anchorterminal.com/open-weights/#openai",
        "others": [
          {
            "name": "gpt-oss-20b",
            "url": "https://huggingface.co/openai/gpt-oss-20b",
            "params": "21B total, 3.6B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/gpt-oss-20b"
          },
          {
            "name": "gpt-oss-safeguard-120b",
            "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b",
            "params": "117B total, 5.1B active (MoE)"
          },
          {
            "name": "gpt-oss-safeguard-20b",
            "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b",
            "params": "21B total, 3.6B active (MoE)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenAI (named as publisher on the card; the openai-api listing's terms name OpenAI OpCo, LLC)",
        "domain": "openai.com",
        "domainRegistered": "2007-01-19",
        "domainNote": "openai.com was registered in 2007, before OpenAI existed.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/openai/gpt-oss/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file is Apache 2.0 with the template's placeholder copyright line, so the legal entity is the one OpenAI's own terms name, read for the openai-api listing.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the gpt-oss GitHub repository's commit history and tags (v0.0.1 to v0.0.9); the Hugging Face repository's commits stop on 26 August 2025.",
          "openai.com/.well-known/security.txt answered on 21 October 2026 with a Bugcrowd contact, an acknowledgements page and a coordinated vulnerability disclosure policy. It has no Expires line, which RFC 9116 requires."
        ],
        "score": 87
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "OpenAI",
        "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",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-14",
        "b": "2025-08-26",
        "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": "2.8/5 (4)",
        "b": "3.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-5.3 or gpt-oss-120b?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 80 against 54",
          "Documentation and upkeep, 58 against 45"
        ],
        "also": null,
        "goodFor": "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",
        "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, 95 against 50",
          "Agent interface, 90 against 70"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5"
        ],
        "goodFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support",
        "slug": "gpt-oss-120b",
        "watchFor": "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b"
      },
      {
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        "title": "Apertus v1.5 8B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3"
      },
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        "title": "Apertus v1.5 8B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.json",
        "title": "Apodex-1.1-mini vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-2b-instruct.json",
        "title": "gpt-oss-120b vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "gpt-oss-120b vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-32b-instruct.json",
        "title": "gpt-oss-120b vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct.json",
        "title": "gpt-oss-120b vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.json",
        "title": "gpt-oss-120b vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "gpt-oss-120b vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.json",
        "title": "gpt-oss-120b vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.json",
        "title": "gpt-oss-120b vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 26,
        "edge": "glm-5-3",
        "glm-5-3": 80,
        "gpt-oss-120b": 54,
        "key": "capability",
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 20,
        "edge": "gpt-oss-120b",
        "glm-5-3": 55,
        "gpt-oss-120b": 75,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 45,
        "edge": "gpt-oss-120b",
        "glm-5-3": 50,
        "gpt-oss-120b": 95,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "gpt-oss-120b",
        "glm-5-3": 70,
        "gpt-oss-120b": 90,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 13,
        "edge": "glm-5-3",
        "glm-5-3": 58,
        "gpt-oss-120b": 45,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep. 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.",
      "gpt-oss-120b": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b",
    "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b.min.md"
  },
  "markdown": "gpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep. 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- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.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 54\n- Documentation and upkeep, 58 against 45\n\nWatch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build\n\n### gpt-oss-120b (BB)\n\nGood for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support\n\nAhead on:\n- Reach, 75 against 55\n- Risk, 95 against 50\n- Agent interface, 90 against 70\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision\n\n\n## Score by category\n\n| Category | Weight | GLM-5.3 | gpt-oss-120b | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 80 | 54 | GLM-5.3 +26 |\n| Reach | 25% (25 this run) | 55 | 75 | gpt-oss-120b +20 |\n| Risk | 20% (20 this run) | 50 | 95 | gpt-oss-120b +45 |\n| Agent interface | 10% (10 this run) | 70 | 90 | gpt-oss-120b +20 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 45 | GLM-5.3 +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.6 · B** | **70.2 · BB** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3 | gpt-oss-120b |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | OpenAI |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2025-08-26 |\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 | 2.8/5 (4) | 3.5/5 (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**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.\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### gpt-oss-120b\n\n1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits\n2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs\n3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony\n4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise\n5. Set `Reasoning: low` in the system message for short tasks, medium is the default\n6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users\n7. Expect one tool call per turn and loop, don't wait for parallel calls\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3 or gpt-oss-120b?\n\ngpt-oss-120b scores 70.2 (BB) on agent readiness against GLM-5.3's 64.6 (B), and leads in 3 of 5 scored categories. GLM-5.3 leads on capability and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3\", \"b\": \"gpt-oss-120b\"}`. From a terminal: `anchor compare glm-5-3 gpt-oss-120b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3.json and https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json\n\n## Other comparisons with GLM-5.3 or gpt-oss-120b\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 gpt-oss-120b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.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 gpt-oss-120b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.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 gpt-oss-120b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b.md)\n- [BigBang-v1 vs GLM-5.3](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.md)\n- [BigBang-v1 vs 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    "section": "tools",
    "title": "GLM-5.3 vs gpt-oss-120b for AI agents in 2026: scores and prices",
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    "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-120b"
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