{
  "data": {
    "a": {
      "slug": "carbon-x",
      "name": "Carbon-X",
      "vendor": "Hugging Face (HuggingFaceBio)",
      "vendorUrl": "https://huggingface.co/HuggingFaceBio",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Carbon-X is Hugging Face's open-weight 14.8B language model for DNA, a fine-tune of Qwen3-14B that reads raw DNA in a chat and answers in English about variant effects and regulatory regions, with a 40,960-token context, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/carbon-x",
      "markdownUrl": "https://www.anchorterminal.com/tools/carbon-x.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/carbon-x.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/carbon-x.json",
      "repo": "https://huggingface.co/HuggingFaceBio/Carbon-X",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "HuggingFaceBio/Carbon-X"
        },
        {
          "registry": "huggingface",
          "name": "HuggingFaceBio/Carbon-X-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for HuggingFaceBio/Carbon-X and HuggingFaceBio/Carbon-X-GGUF (checked 2026-10-23). The Python tokenizer fetches Qwen/Qwen3-4B-Base's tokenizer at a pinned revision on first load, which is also ungated.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, and the base Qwen3-14B is Apache 2.0 too. Hugging Face sells no per-call access to it and no inference provider serves it. Running it costs the hardware, 29.6 GB for the BF16 safetensors and 9.6 GB for the publisher's smallest GGUF, Q4_K_M (checked 2026-10-23).",
      "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 2026-10-23).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-23"
      },
      "docsUrl": "https://huggingface.co/HuggingFaceBio/Carbon-X",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "dna",
        "genomics",
        "biology",
        "fine-tune",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-10-22",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "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": 1201,
        "categoryRank": 30,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 35,
          "reach": 90,
          "risk": 65,
          "upkeep": 77
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-23"
        },
        "negative": 0,
        "verdict": "A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.",
        "bestFor": "Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.",
        "strengths": [
          "Hugging Face's own GGUFs at four precisions, the smallest about 12 GB with a fifth added",
          "Runs in released llama.cpp, vLLM, SGLang and Transformers",
          "Apache 2.0 on the model and its Qwen3-14B base, nothing gated",
          "A ready-made skill, an OpenAI-style tool example and an Inspect example for calling it from another agent",
          "Mid-training data listed by source with shares of 302B tokens"
        ],
        "weaknesses": [
          "Not clinically validated and no wet-lab validation reported",
          "The Python tokenizer needs trust_remote_code, and llama.cpp before b11742 tokenizes DNA wrongly",
          "Not trained for tool calling, structured output or reasoning",
          "The card says llama.cpp is unsupported while the publisher's GGUF repository says it is",
          "No inference provider or hosted API, and no knowledge cut-off"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0`, on llama.cpp b11742 or later",
          "With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load",
          "Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used",
          "Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences",
          "Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token",
          "Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text",
          "Cap max_tokens for next-base questions, long DNA generation is slow and degrades"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 35,
          "reach": 90,
          "risk": 65,
          "upkeep": 77
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-23",
            "reach": "2026-10-23",
            "risk": "2026-10-23",
            "upkeep": "2026-10-23"
          },
          "last": "2026-10-23",
          "oldest": "2026-10-23",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# llama.cpp b11742 or later (older builds tokenize DNA wrongly):\nllama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja \\\n  --chat-template-kwargs '{\"enable_thinking\": false}' \\\n  --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 --port 8080\n# or, with vLLM:\nvllm serve HuggingFaceBio/Carbon-X --trust-remote-code",
        "http": "curl http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Is this sequence more likely coding or non-coding? ATGGCGCTGTGGATGCGCCTCCTG\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/carbon-x"
      },
      "sameCompany": [
        "hugging-face-inference-endpoints",
        "smollm3-3b"
      ],
      "weights": {
        "trainedBy": "Hugging Face with Zhongguancun Academy and TIGEM / University of Naples Federico II",
        "builtOn": "Qwen/Qwen3-14B (vocabulary extension, mid-training, supervised fine-tune and GRPO; base Apache 2.0, declared in the card's metadata)",
        "params": "14.8B",
        "context": 40960,
        "goodFor": "A DNA specialist another agent calls, for variant-effect questions, regulatory regions and next-base prediction on raw DNA kept on local hardware.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Carbon-X-GGUF Q4_K_M",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 9.64,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "carbon-x-q4_k_m.gguf, 9,636,318,080 bytes. Embedding and output tensors stay at Q8_0. The publisher's own runs put it lowest of the builds on FrontierVEP (67.8) and NextBase (38.6)"
          },
          {
            "name": "Carbon-X-GGUF Q6_K",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 12.56,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "carbon-x-q6_k.gguf, 12,555,946,880 bytes"
          },
          {
            "name": "Carbon-X-GGUF Q5_K_M",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 11.05,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "carbon-x-q5_k_m.gguf, 11,051,895,680 bytes"
          },
          {
            "name": "Carbon-X-GGUF Q8_0",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 15.76,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "carbon-x-q8_0.gguf, 15,755,742,080 bytes, the build the publisher recommends"
          },
          {
            "name": "Carbon-X",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 29.64,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ],
            "note": "Needs trust_remote_code for the custom HybridDNATokenizer, and Transformers v5 or later. The card says it fits one 80 GB GPU"
          },
          {
            "name": "Carbon-X-GGUF BF16",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 29.65,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "carbon-x-bf16.gguf, 29,651,012,480 bytes. llama.cpp b11742 or later"
          }
        ],
        "run": "llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 --port 8080",
        "runBuild": "Carbon-X-GGUF Q8_0",
        "checked": "2026-10-23",
        "minMemoryGB": 12
      },
      "family": {
        "lab": "Hugging Face (Carbon)",
        "family": "Carbon",
        "url": "https://www.anchorterminal.com/open-weights/#huggingface-bio",
        "others": [
          {
            "name": "Carbon-A-1.2B",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-A-1.2B",
            "params": "1.2B"
          },
          {
            "name": "Carbon-8B",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-8B",
            "params": "8.3B"
          },
          {
            "name": "Carbon-3B",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-3B",
            "params": "3.5B"
          },
          {
            "name": "Carbon-500M",
            "url": "https://huggingface.co/HuggingFaceBio/Carbon-500M",
            "params": "0.5B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Hugging Face (the HuggingFaceBio organisation, named Hugging Face Biology Research on the Hub)",
        "domain": "huggingface.co",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "valid",
        "checked": "2026-10-23",
        "notes": [
          "The HuggingFaceBio organisation isn't verified on the Hub. It has 27 members, Hugging Face staff among them, and the huggingface/carbon GitHub repository links its Carbon models.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "huggingface.co/.well-known/security.txt names a security contact and expires in 2030.",
          "No changelog. The main repository's history shows one commit on 22 October 2026; the merged pull requests show earlier commits on 21 and 22 October, so the history was squashed. Registration of huggingface.co wasn't checked."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/carbon-x.json",
      "live": {
        "slug": "carbon-x",
        "securityTxt": {
          "url": "https://huggingface.co/.well-known/security.txt",
          "state": "valid",
          "expires": "2030-07-01T08:42:00.000Z",
          "checkedAt": "2026-10-23T15:25:11.273582159Z"
        },
        "updatedAt": "2026-10-23T15:25:11.273582159Z"
      }
    },
    "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep.",
    "b": {
      "slug": "glm-4-7-flash",
      "name": "GLM-4.7-Flash",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-4.7-Flash is Z.ai's open-weight mixture-of-experts reasoning model, 30B parameters with about 3B active per token, for English and Chinese, published on Hugging Face in BF16 under the MIT licence.",
      "url": "https://www.anchorterminal.com/tools/glm-4-7-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json",
      "repo": "https://huggingface.co/zai-org/GLM-4.7-Flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.7-Flash"
        },
        {
          "registry": "ollama",
          "name": "glm-4.7-flash"
        }
      ],
      "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 the MIT licence named on the card. Running it yourself costs the hardware, 62.5 GB for the BF16 files and 19 GB for Ollama's default build. Z.ai's own API also serves GLM-4.7-Flash, which its docs describe as completely free (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-4.7-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-01-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63,
        "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": 1201,
        "categoryRank": 21,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.",
        "bestFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "strengths": [
          "MIT weights with no use policy, downloadable with no account",
          "Model code in released vLLM, SGLang, Transformers and llama.cpp, and a local Ollama build",
          "Ollama's 19 GB build fits a 24 GB GPU, and BF16 fits our reference machine",
          "Z.ai documents the glm47 tool parser, OpenAI-style tool declarations and turning thinking off per request",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard"
        ],
        "weaknesses": [
          "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January",
          "No quantised build from Z.ai and no LICENSE file in the repository",
          "No safety evaluation, agent-use guidance or security contact",
          "The card leaves out intended use, limitations, context length, knowledge cut-off and training data",
          "config.json and tokenizer_config.json disagree on context length"
        ],
        "agentNotes": [
          "On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`",
          "Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build",
          "Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text",
          "Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026",
          "Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default",
          "Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench",
          "Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000"
        ],
        "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": 63
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run glm-4.7-flash\n# or, per Z.ai's card:\nvllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 \\\n  --enable-auto-tool-choice --served-model-name glm-4.7-flash",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-4.7-flash\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-4-7-flash"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-5-3",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "30B",
        "active": "3B",
        "context": 202752,
        "goodFor": "Small mixture-of-experts reasoning model for English and Chinese with tool calling, a local coding and agent model.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama glm-4.7-flash:latest",
            "url": "https://ollama.com/library/glm-4.7-flash:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "GLM-4.7-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash",
        "runBuild": "GLM-4.7-Flash",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-4",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-4-9B",
            "url": "https://huggingface.co/zai-org/glm-4-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat-1M",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m",
            "params": "9B"
          },
          {
            "name": "GLM-4V-9B",
            "url": "https://huggingface.co/zai-org/glm-4v-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-4-32B-Base-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-Z1-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-Rumination-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4.1V-9B-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base",
            "params": "9B"
          },
          {
            "name": "GLM-4.1V-9B-Thinking",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking",
            "params": "9B"
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          {
            "name": "GLM-4.5-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Base",
            "params": "355B-A32B"
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          {
            "name": "GLM-4.5",
            "url": "https://huggingface.co/zai-org/GLM-4.5",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5-Air-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base",
            "params": "106B-A12B"
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          {
            "name": "GLM-4.5-Air",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air",
            "params": "106B-A12B",
            "listing": "https://www.anchorterminal.com/tools/glm-4-5-air"
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          {
            "name": "GLM-4.5V",
            "url": "https://huggingface.co/zai-org/GLM-4.5V",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.6",
            "url": "https://huggingface.co/zai-org/GLM-4.6",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.6V",
            "url": "https://huggingface.co/zai-org/GLM-4.6V",
            "params": "106B"
          },
          {
            "name": "GLM-4.6V-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash",
            "params": "9B"
          },
          {
            "name": "GLM-4.7",
            "url": "https://huggingface.co/zai-org/GLM-4.7",
            "params": "355B-A32B"
          }
        ],
        "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)",
        "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-4.7-Flash/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' card names no legal entity and the repository has no LICENSE file.",
          "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 12 commits from 19 to 29 January 2026, mostly card and benchmark updates.",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
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        "score": 68
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      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json",
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          "url": "https://z.ai/.well-known/security.txt",
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          "checkedAt": "2026-10-23T15:25:20.361208872Z"
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        "pages": [
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            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main",
            "kind": "changelog",
            "status": 200,
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            "changedAt": "2026-10-23T01:50:21.599191768Z",
            "fingerprint": "41bb21c48069"
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        "updatedAt": "2026-10-23T15:25:20.361208872Z"
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    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Hugging Face (HuggingFaceBio)",
        "b": "Z.ai (Zhipu AI)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-22",
        "b": "2026-01-29",
        "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"
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      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
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        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
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        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
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        "a": "3/5 (4)",
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        "name": "Agent reviews"
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    "faq": [
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        "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep.",
        "question": "Which is better for AI agents, Carbon-X or GLM-4.7-Flash?"
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          "Risk, 65 against 55",
          "Documentation and upkeep, 77 against 28"
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        "also": null,
        "goodFor": "Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.",
        "slug": "carbon-x",
        "watchFor": "Not clinically validated and no wet-lab validation reported"
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      {
        "aheadOn": [
          "Agent interface, 85 against 35"
        ],
        "also": null,
        "goodFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "slug": "glm-4-7-flash",
        "watchFor": "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January"
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      "name": "Open-weight models you run yourself"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-12b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-120b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-120b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-hy3.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-hy3"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling.json",
        "title": "Carbon-X vs Inkling",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small.json",
        "title": "Carbon-X vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-m-1.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-distill-qwen-9b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5.json",
        "title": "Carbon-X vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7.json",
        "title": "Carbon-X vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-muse-glimmer-30b.json",
        "title": "Carbon-X vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-muse-glimmer-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b.json",
        "title": "Carbon-X vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-ultra-550b-a55b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-olmo-3-7b-think.json",
        "title": "Carbon-X vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-olmo-3-7b-think"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.json",
        "title": "GLM-4.7-Flash vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507.json",
        "title": "GLM-4.7-Flash vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b.json",
        "title": "GLM-4.7-Flash vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b.json",
        "title": "GLM-4.7-Flash vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b.json",
        "title": "GLM-4.7-Flash vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next.json",
        "title": "GLM-4.7-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-next.json",
        "title": "GLM-4.7-Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct.json",
        "title": "GLM-4.7-Flash vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2.json",
        "title": "GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.json",
        "title": "GLM-4.7-Flash vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b.json",
        "title": "GLM-4.7-Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.json",
        "title": "GLM-4.7-Flash vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b.json",
        "title": "GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "carbon-x": 90,
        "edge": "carbon-x",
        "glm-4-7-flash": 80,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "carbon-x": 65,
        "edge": "carbon-x",
        "glm-4-7-flash": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 50,
        "carbon-x": 35,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 49,
        "carbon-x": 77,
        "edge": "carbon-x",
        "glm-4-7-flash": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "carbon-x": "A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.",
      "glm-4-7-flash": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash",
    "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.min.md"
  },
  "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Carbon-X: grade C, 61/100, rank #38 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/carbon-x.md · JSON https://www.anchorterminal.com/api/v1/tools/carbon-x.json\n- GLM-4.7-Flash: grade B, 63/100, rank #27 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3023 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Carbon-X (C)\n\nGood for: Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.\n\nAhead on:\n- Reach, 90 against 80\n- Risk, 65 against 55\n- Documentation and upkeep, 77 against 28\n\nWatch for: Not clinically validated and no wet-lab validation reported\n\n### GLM-4.7-Flash (B)\n\nGood for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime\n\nAhead on:\n- Agent interface, 85 against 35\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\n\n\n## Score by category\n\n| Category | Weight | Carbon-X | GLM-4.7-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 80 | Carbon-X +10 |\n| Risk | 20% (20 this run) | 65 | 55 | Carbon-X +10 |\n| Agent interface | 10% (10 this run) | 35 | 85 | GLM-4.7-Flash +50 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 28 | Carbon-X +49 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **63 · B** | |\n\n## Facts side by side\n\n| Fact | Carbon-X | GLM-4.7-Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Hugging Face (HuggingFaceBio) | Z.ai (Zhipu AI) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-22 | 2026-01-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Carbon-X.** A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.\n\n**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.\n\n## Before you call either\n\n### Carbon-X\n\n1. Quickest start is `llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0`, on llama.cpp b11742 or later\n2. With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load\n3. Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used\n4. Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences\n5. Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token\n6. Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text\n7. Cap max_tokens for next-base questions, long DNA generation is slow and degrades\n\n### GLM-4.7-Flash\n\n1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`\n2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build\n3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text\n4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026\n5. Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default\n6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench\n7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000\n\n## Questions\n\n### Which is better for AI agents, Carbon-X or GLM-4.7-Flash?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Carbon-X's 61 (C), and leads in 1 of 4 scored categories. Carbon-X leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"carbon-x\", \"b\": \"glm-4-7-flash\"}`. From a terminal: `anchor compare carbon-x glm-4-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json\n\n## Other comparisons with Carbon-X or GLM-4.7-Flash\n\n- [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md)\n- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.md)\n- [Apertus v1.5 8B vs Carbon-X](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.md)\n- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)\n- [Apodex-1.1-mini vs Carbon-X](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x.md)\n- [Apodex-1.1-mini vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash.md)\n- [BigBang-v1 vs Carbon-X](https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x.md)\n- [BigBang-v1 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.md)\n- [Carbon-X vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b.md)\n- [Carbon-X vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus.md)\n- [Carbon-X vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2.md)\n- [Carbon-X vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash.md)\n- [Carbon-X vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.md)\n- [Carbon-X vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.md)\n- [Carbon-X vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-12b-it.md)\n- [Carbon-X vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.md)\n- [Carbon-X vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it.md)\n- [Carbon-X vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.md)\n- [Carbon-X vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it.md)\n- [Carbon-X vs GLM-4.5-Air](https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air.md)\n- [Carbon-X vs GLM-5.3](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3.md)\n- [Carbon-X vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.md)\n- [Carbon-X vs gpt-oss-120b](https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-120b.md)\n- [Carbon-X vs gpt-oss-20b](https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-20b.md)\n- [Carbon-X vs Granite 4.2 3B](https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-3b.md)\n- [Carbon-X vs Hy3](https://www.anchorterminal.com/compare/carbon-x-vs-hy3.md)\n- [Carbon-X vs Inkling](https://www.anchorterminal.com/compare/carbon-x-vs-inkling.md)\n- [Carbon-X vs Inkling-Small](https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small.md)\n- [Carbon-X vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Carbon-X vs Kimi K3](https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3.md)\n- [Carbon-X vs Kolibri-1](https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.md)\n- [Carbon-X vs Laguna M.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-m-1.md)\n- [Carbon-X vs Laguna S 2.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.md)\n- [Carbon-X vs Laguna XS 2.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1.md)\n- [Carbon-X vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.md)\n- [Carbon-X vs 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