{
  "data": {
    "a": {
      "slug": "apertus-v1-5-8b",
      "name": "Apertus v1.5 8B",
      "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)",
      "vendorUrl": "https://www.swiss-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.",
      "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json",
      "dns": "apertus-v1-5-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
      "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "swiss-ai/Apertus-v1.5-8B"
        }
      ],
      "auth": "none",
      "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (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/swiss-ai/Apertus-v1.5-8B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "multilingual",
        "open-data",
        "audio-input",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gated",
        "self-hosted"
      ],
      "lastRelease": "2026-10-14",
      "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": 1518,
        "categoryRank": 30,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 40,
          "risk": 75,
          "upkeep": 84
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
        "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "strengths": [
          "Open training data, with every dataset listed by version and licence and the post-training data published",
          "Safety benchmarks reported for the released 8B, plus outside evaluators given early access",
          "Tool format documented by the publisher, with a parser named and several calls a turn",
          "Thinking that turns on per request, and 262,144 tokens of context",
          "Safetensors only, with a checksum on every file",
          "Swiss AI answered every question in the repository's discussions"
        ],
        "weaknesses": [
          "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers",
          "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits",
          "Gated download that collects contact details, under a use policy you must pass on when you redistribute",
          "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B",
          "No structured output documentation and no knowledge cut-off"
        ],
        "agentNotes": [
          "Accept the terms on the card and set HF_TOKEN before downloading",
          "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet",
          "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools",
          "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously",
          "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames",
          "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32",
          "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 40,
          "risk": 75,
          "upkeep": 84
        },
        "provenanceScore": 45,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n  --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser apertus",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/apertus-v1-5-8b"
      },
      "weights": {
        "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)",
        "params": "8.9B",
        "context": 262144,
        "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.",
        "runtimes": [],
        "builds": [
          {
            "name": "Apertus-v1.5-8B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (Swiss AI fork)",
              "Transformers (Swiss AI fork)"
            ],
            "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI"
          }
        ],
        "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus",
        "runBuild": "Apertus-v1.5-8B",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)",
        "family": "Apertus (v1 2509, v1.1, v1.5)",
        "url": "https://www.anchorterminal.com/open-weights/#swiss-ai",
        "others": [
          {
            "name": "Apertus-v1.5-8B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base",
            "params": "8B (Hub safetensors count 8.84B)"
          },
          {
            "name": "Apertus-v1.5-70B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base",
            "params": "70B (Hub safetensors count 71.94B)"
          },
          {
            "name": "Apertus-v1.5-8B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-DPO",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-70B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.1-1.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-v1.1-4B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct",
            "params": "0.5B (Hub safetensors count 573M)"
          },
          {
            "name": "Apertus-v1.1-4B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B",
            "params": "0.5B (Hub safetensors count 438M)"
          },
          {
            "name": "Apertus-v1.1-1.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-8B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          },
          {
            "name": "Apertus-70B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-70B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-8B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)",
        "domain": "swiss-ai.org",
        "domainRegistered": "2023-08-10",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.",
          "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.",
          "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.",
          "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure."
        ],
        "score": 45
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json",
      "live": {
        "slug": "apertus-v1-5-8b",
        "securityTxt": {
          "url": "https://swiss-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:09:00.762955628Z"
        },
        "domain": {
          "domain": "swiss-ai.org",
          "registered": "2023-08-10",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/swiss-ai.org",
          "checkedAt": "2026-11-08T14:53:29.32312593Z"
        },
        "updatedAt": "2026-11-10T16:09:00.762955628Z"
      }
    },
    "answer": "Apertus v1.5 8B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.",
    "b": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).",
      "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 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "edges": [
      {
        "by": 38,
        "category": "upkeep",
        "leads": "apertus-v1-5-8b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.",
          "b": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46."
        },
        "scores": {
          "a": 84,
          "b": 46
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "apertus-v1-5-8b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.",
          "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60."
        },
        "scores": {
          "a": 75,
          "b": 60
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "apertus-v1-5-8b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Swiss AI's report documents how tools are declared and returned and the card names vLLM's apertus parser. Trained tool calling 20, trained on verified tool use with several calls a turn, though parallel calls are weaker on the 8B and tools don't work with thinking on. Structured output 0, not documented. Reasoning control 10, thinking turns on per request and the card says where the reasoning comes back. Context 10, 262,144 tokens trained.",
          "b": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "apertus-v1-5-8b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.",
          "b": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35."
        },
        "scores": {
          "a": 40,
          "b": 35
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "b": "Meituan (LongCat)",
        "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-14",
        "b": "2026-11-10",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Apertus v1.5 8B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Apertus v1.5 8B or LongCat-2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 40 against 35",
          "Risk, 75 against 60",
          "Agent interface, 85 against 75",
          "Documentation and upkeep, 84 against 46"
        ],
        "also": null,
        "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "slug": "apertus-v1-5-8b",
        "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json",
        "title": "Agents-A1-4B vs Apertus v1.5 8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json",
        "title": "Agents-A1-4B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json",
        "title": "Apertus v1.5 8B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json",
        "title": "Apertus v1.5 8B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json",
        "title": "Apertus v1.5 8B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json",
        "title": "Apertus v1.5 8B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.json",
        "title": "Apertus v1.5 8B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json",
        "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json",
        "title": "Apertus v1.5 8B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json",
        "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.json",
        "title": "Apertus v1.5 8B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.json",
        "title": "Apertus v1.5 8B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json",
        "title": "Apertus v1.5 8B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json",
        "title": "Apertus v1.5 8B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json",
        "title": "Apertus v1.5 8B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3"
      },
      {
        "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-gpt-oss-120b.json",
        "title": "Apertus v1.5 8B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.json",
        "title": "Apertus v1.5 8B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json",
        "title": "Apertus v1.5 8B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.json",
        "title": "Apertus v1.5 8B vs Inkling",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json",
        "title": "Apertus v1.5 8B vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.json",
        "title": "Apertus v1.5 8B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.json",
        "title": "Apertus v1.5 8B vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json",
        "title": "Apertus v1.5 8B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.json",
        "title": "Apertus v1.5 8B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json",
        "title": "Apertus v1.5 8B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1"
      },
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.json",
        "title": "LongCat-2.5 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-30b-a3b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-4b-instruct-2507"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b.json",
        "title": "LongCat-2.5 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b"
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        "title": "LongCat-2.5 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LongCat-2.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json",
        "title": "LongCat-2.5 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.json",
        "title": "LongCat-2.5 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.json",
        "title": "LongCat-2.5 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.json",
        "title": "LongCat-2.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.json",
        "title": "LongCat-2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json",
        "title": "LongCat-2.5 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json",
        "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.json",
        "title": "LongCat-2.5 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "apertus-v1-5-8b": 40,
        "by": 5,
        "edge": "apertus-v1-5-8b",
        "key": "reach",
        "longcat-2-5": 35,
        "name": "Reach",
        "weight": 25
      },
      {
        "apertus-v1-5-8b": 75,
        "by": 15,
        "edge": "apertus-v1-5-8b",
        "key": "risk",
        "longcat-2-5": 60,
        "name": "Risk",
        "weight": 20
      },
      {
        "apertus-v1-5-8b": 85,
        "by": 10,
        "edge": "apertus-v1-5-8b",
        "key": "interface",
        "longcat-2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "apertus-v1-5-8b": 84,
        "by": 38,
        "edge": "apertus-v1-5-8b",
        "key": "upkeep",
        "longcat-2-5": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Apertus v1.5 8B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5",
    "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.min.md"
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  "markdown": "Apertus v1.5 8B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #40 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Reach, 40 against 35\n- Risk, 75 against 60\n- Agent interface, 85 against 75\n- Documentation and upkeep, 84 against 46\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Documentation and upkeep: Apertus v1.5 8B +38\n\n- Apertus v1.5 8B (84): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Risk: Apertus v1.5 8B +15\n\n- Apertus v1.5 8B (75): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n\n### Agent interface: Apertus v1.5 8B +10\n\n- Apertus v1.5 8B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Swiss AI's report documents how tools are declared and returned and the card names vLLM's apertus parser. Trained tool calling 20, trained on verified tool use with several calls a turn, though parallel calls are weaker on the 8B and tools don't work with thinking on. Structured output 0, not documented. Reasoning control 10, thinking turns on per request and the card says where the reasoning comes back. Context 10, 262,144 tokens trained.\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n\n### Reach: Apertus v1.5 8B +5\n\n- Apertus v1.5 8B (40): Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 35 | Apertus v1.5 8B +5 |\n| Risk | 20% (20 this run) | 75 | 60 | Apertus v1.5 8B +15 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Apertus v1.5 8B +10 |\n| Documentation and upkeep | 10% (10 this run) | 84 | 46 | Apertus v1.5 8B +38 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Meituan (LongCat) |\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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-11-10 |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n## Before you call either\n\n### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or LongCat-2.5?\n\nApertus v1.5 8B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"longcat-2-5\"}`. 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