{
  "data": {
    "a": {
      "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"
    },
    "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Ornith-1.5-397B leads on reach and agent interface.",
    "b": {
      "slug": "ornith-1-5-397b",
      "name": "Ornith-1.5-397B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-GGUF"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:397b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith's Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-397B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59,
        "grade": "C",
        "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": 53,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.",
        "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions",
          "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no code in the repository"
        ],
        "weaknesses": [
          "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine",
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference",
          "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters"
        ],
        "agentNotes": [
          "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards",
          "Pin a revision when you download, since Ornith has replaced files in place",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "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 ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-397b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)",
        "params": "397B",
        "context": 262144,
        "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-397B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 418.4,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 319.5,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB"
          },
          {
            "name": "Ornith-1.5-397B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 331,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 286.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 244.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine"
          },
          {
            "name": "ollama ornith-1.5:397b",
            "url": "https://ollama.com/library/ornith-1.5:397b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 242,
            "memoryGB": 512,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 806.8,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 428.5,
            "memoryGB": 1024,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          }
        ],
        "run": "ollama run ornith-1.5:397b",
        "runBuild": "ollama ornith-1.5:397b",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ],
        "unrestrictedCopies": 4
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai",
        "b": "Ornith",
        "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": "MIT",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-26",
        "b": "2026-08-23",
        "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": "2/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Ornith-1.5-397B leads on reach and agent interface.",
        "question": "Which is better for AI agents, GLM-5.3-Flash or Ornith-1.5-397B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 50",
          "Documentation and upkeep, 58 against 36"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 2.0 out of 5"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Reach, 65 against 60",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-397b",
        "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "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/agents-a1-4b-vs-ornith-1-5-397b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b"
      },
      {
        "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/apertus-v1-5-8b-vs-ornith-1-5-397b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b"
      },
      {
        "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/apodex-1-1-mini-vs-ornith-1-5-397b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b"
      },
      {
        "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/bigbang-v1-vs-ornith-1-5-397b.json",
        "title": "BigBang-v1 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b"
      },
      {
        "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/cosmos-reason2-2b-vs-ornith-1-5-397b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b"
      },
      {
        "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/darwin-36b-opus-vs-ornith-1-5-397b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b"
      },
      {
        "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-v3-2-vs-ornith-1-5-397b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b"
      },
      {
        "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-v4-1-flash-vs-ornith-1-5-397b.json",
        "title": "DeepSeek V4.1 Flash vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b"
      },
      {
        "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/deepseek-v4-flash-0731-vs-ornith-1-5-397b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b"
      },
      {
        "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/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b"
      },
      {
        "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-4-12b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 12B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3-Flash",
        "url": "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-ornith-1-5-397b.json",
        "title": "Gemma 4 26B A4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 31B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 31B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 E2B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 E2B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 E4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 E4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json",
        "title": "GLM-4.5-Air vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.json",
        "title": "GLM-4.5-Air vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.json",
        "title": "GLM-4.7-Flash vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.json",
        "title": "GLM-4.7-Flash vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-120b.json",
        "title": "GLM-5.3-Flash vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.json",
        "title": "GLM-5.3-Flash vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.json",
        "title": "GLM-5.3-Flash vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.json",
        "title": "GLM-5.3-Flash vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling.json",
        "title": "GLM-5.3-Flash vs Inkling",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.json",
        "title": "GLM-5.3-Flash vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "GLM-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.json",
        "title": "GLM-5.3-Flash vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.json",
        "title": "GLM-5.3-Flash vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.json",
        "title": "GLM-5.3-Flash vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.json",
        "title": "GLM-5.3-Flash vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.json",
        "title": "GLM-5.3-Flash vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b.json",
        "title": "occamy-1.0 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-397B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-397B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-397B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "ornith-1-5-397b",
        "glm-5-3-flash": 60,
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-397b": 65,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "glm-5-3-flash",
        "glm-5-3-flash": 55,
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-397b": 50,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "ornith-1-5-397b",
        "glm-5-3-flash": 70,
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-397b": 85,
        "weight": 10
      },
      {
        "by": 22,
        "edge": "glm-5-3-flash",
        "glm-5-3-flash": 58,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-397b": 36,
        "weight": 10
      }
    ],
    "summary": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Ornith-1.5-397B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "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.",
      "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "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-1-5-397b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.min.md"
  },
  "markdown": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Ornith-1.5-397B leads on reach and agent interface. Both do open-weight models you run yourself.\n\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- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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-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- Risk, 55 against 50\n- Documentation and upkeep, 58 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.0 out of 5\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### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 65 against 60\n- Agent interface, 85 against 70\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | Ornith-1.5-397B +5 |\n| Risk | 20% (20 this run) | 55 | 50 | GLM-5.3-Flash +5 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-397B +15 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 36 | GLM-5.3-Flash +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai | Ornith |\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 | MIT | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-26 | 2026-08-23 |\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) | 2/5 (4) |\n\n## Verdicts\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**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\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### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Ornith-1.5-397B?\n\nGLM-5.3-Flash scores 65.1 (B) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Ornith-1.5-397B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare glm-5-3-flash ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with GLM-5.3-Flash or Ornith-1.5-397B\n\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.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- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.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- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.md)\n- [Cosmos-Reason2-2B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.md)\n- [Darwin-36B-Opus vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.md)\n- [Darwin-36B-Opus vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.md)\n- [DeepSeek-V3.2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.md)\n- [DiffusionGemma 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 12B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 12B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E2B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [GLM-4.5-Air vs 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Inkling-Small](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.md)\n- [GLM-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.md)\n- [GLM-5.3-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.md)\n- [GLM-5.3-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.md)\n- [GLM-5.3-Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [GLM-5.3-Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-o-4-5.md)\n- [GLM-5.3-Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm5-2b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-olmo-3-7b-think.md)\n- [GLM-5.3-Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3-Flash vs 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Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.md)\n- [GLM-5.3-Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.md)\n- [GLM-5.3-Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.md)\n- [GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.md)\n- [GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [GLM-5.3-Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.md)\n- [GLM-5.3-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash.md)\n- [GLM-5.3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.md)\n- [gpt-oss-120b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b.md)\n- [gpt-oss-20b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b.md)\n- [Granite 4.2 3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.md)\n- [Hy3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.md)\n- [Inkling-Small vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b.md)\n- [Inkling vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-397b.md)\n- [Kimi K3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.md)\n- [Kolibri-1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.md)\n- [Laguna M.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b.md)\n- [Laguna S 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.md)\n- [Laguna XS 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.md)\n- [Ling-3.0-tiny vs 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