{
  "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": "Carbon-X scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface.",
    "b": {
      "slug": "lfm2-5-2-6b",
      "name": "LFM2.5-2.6B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json",
      "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM2.5-2.6B-MLX"
        },
        {
          "registry": "ollama",
          "name": "LiquidAI/lfm2.5-2.6b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://docs.liquid.ai/lfm/models/lfm25-2.6b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "self-hosted"
      ],
      "lastRelease": "2026-09-22",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 51.5,
        "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": 1201,
        "categoryRank": 73,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 13,
          "interface": 75,
          "reach": 95,
          "risk": 50,
          "upkeep": 57
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.",
        "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold",
        "strengths": [
          "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added",
          "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang",
          "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context",
          "Safetensors and GGUF only, no repository code, a checksum on every file and no gate",
          "Builds at many precisions from Liquid itself, refreshed in September 2026"
        ],
        "weaknesses": [
          "Commercial use isn't licensed for organisations with $10M or more of annual revenue",
          "Thinking is always on, with no way to turn it off or budget it",
          "No safety evaluation, agent guidance or security contact",
          "No structured-output documentation and no knowledge cut-off on the card",
          "Liquid answered 2 of 16 discussions"
        ],
        "agentNotes": [
          "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`",
          "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately",
          "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set",
          "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead",
          "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought",
          "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true",
          "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 51.5
          }
        ],
        "editorialScores": {
          "capability": 13,
          "interface": 75,
          "reach": 95,
          "risk": 50,
          "upkeep": 57
        },
        "provenanceScore": 55,
        "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": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm2-5-2-6b"
      },
      "sameCompany": [
        "liquid-d1"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM2.5-2.6B-Base",
        "params": "2.69B",
        "context": 131072,
        "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "LFM2.5-2.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 5.4,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          },
          {
            "name": "LFM2.5-2.6B-GGUF Q4_0",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 1.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026"
          },
          {
            "name": "LFM2.5-2.6B-MLX 4bit",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.6,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository"
          },
          {
            "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m",
            "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.7,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1",
        "runBuild": "LFM2.5-2.6B-GGUF Q4_0",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM2 (LFM2 and LFM2.5)",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM2.5-24B-A2B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)"
          },
          {
            "name": "LFM2.5-24B-A2B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)"
          },
          {
            "name": "LFM2.5-VL-3B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B",
            "params": "3B (Hub safetensors count 3.12B)"
          },
          {
            "name": "LFM2.5-2.6B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base",
            "params": "2.6B (Hub safetensors count 2.70B)"
          },
          {
            "name": "LFM2.5-Encoder-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-Encoder-230M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Prompt-Router",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Policy-Linter",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Diffusion",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-Spellchecker",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker",
            "params": "350M (Hub safetensors count 357M)"
          },
          {
            "name": "LFM2.5-230M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-230M",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-230M-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base",
            "params": "230M (Hub safetensors count 230M)"
          },
          {
            "name": "LFM2.5-Encoder-350M-PII-Detector",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector",
            "params": "350M (Hub safetensors count 355M)"
          },
          {
            "name": "LFM2.5-8B-A1B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)"
          },
          {
            "name": "LFM2.5-8B-A1B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)"
          },
          {
            "name": "LFM2.5-VL-450M-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract",
            "params": "450M (Hub safetensors count 449M)"
          },
          {
            "name": "LFM2.5-VL-1.6B-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract",
            "params": "1.6B (Hub safetensors count 1.60B)"
          },
          {
            "name": "LFM2.5-Audio-1.5B-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2.5-1.2B-JP-202606",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Longevity",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2-1.2B-Longevity",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-ColBERT-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M",
            "params": "350M (Hub safetensors count 353M)"
          },
          {
            "name": "LFM2.5-Embedding-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-VL-450M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M",
            "params": "450M (Hub safetensors count 449M)"
          },
          {
            "name": "LFM2.5-350M-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2.5-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-24B-A2B",
            "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B",
            "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)"
          },
          {
            "name": "LFM2.5-1.2B-Thinking",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-1.2B-Instruct",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2.5-VL-1.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B",
            "params": "1.6B (Hub safetensors count 1.60B)"
          },
          {
            "name": "LFM2.5-1.2B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Transcript",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2.5-1.2B-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-2.6B-Exp",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2.5-Audio-1.5B",
            "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2-ColBERT-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M",
            "params": "350M (Hub safetensors count 353M)"
          },
          {
            "name": "LFM2-VL-3B",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B",
            "params": "3B (Hub safetensors count 3.00B)"
          },
          {
            "name": "LFM2-8B-A1B",
            "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B",
            "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)"
          },
          {
            "name": "LFM2-350M-PII-Extract-JP",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-2.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2-2.6B",
            "params": "2.6B (Hub safetensors count 2.57B)"
          },
          {
            "name": "LFM2-350M-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-350M-ENJP-MT",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B-Tool",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-1.2B-RAG",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-Audio-1.5B",
            "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B",
            "params": "1.5B (Hub safetensors count 1.47B)"
          },
          {
            "name": "LFM2-350M-Math",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B-Extract",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract",
            "params": "1.2B (Hub safetensors count 1.17B)"
          },
          {
            "name": "LFM2-VL-450M",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M",
            "params": "450M (Hub safetensors count 451M)"
          },
          {
            "name": "LFM2-VL-1.6B",
            "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B",
            "params": "1.6B (Hub safetensors count 1.58B)"
          },
          {
            "name": "LFM2-700M",
            "url": "https://huggingface.co/LiquidAI/LFM2-700M",
            "params": "700M (Hub safetensors count 742M)"
          },
          {
            "name": "LFM2-350M",
            "url": "https://huggingface.co/LiquidAI/LFM2-350M",
            "params": "350M (Hub safetensors count 354M)"
          },
          {
            "name": "LFM2-1.2B",
            "url": "https://huggingface.co/LiquidAI/LFM2-1.2B",
            "params": "1.2B (Hub safetensors count 1.17B)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026.",
          "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.",
          "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.",
          "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json",
      "live": {
        "slug": "lfm2-5-2-6b",
        "securityTxt": {
          "url": "https://liquid.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-23T15:23:40.527073239Z"
        },
        "llmsTxt": {
          "url": "https://docs.liquid.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-23T14:46:30.697020068Z"
        },
        "updatedAt": "2026-10-23T15:23:40.527073239Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Hugging Face (HuggingFaceBio)",
        "b": "Liquid 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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-22",
        "b": "2026-09-22",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Carbon-X scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface.",
        "question": "Which is better for AI agents, Carbon-X or LFM2.5-2.6B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 50",
          "Documentation and upkeep, 77 against 57"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Reach, 95 against 90",
          "Agent interface, 75 against 35"
        ],
        "also": null,
        "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold",
        "slug": "lfm2-5-2-6b",
        "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.json",
        "title": "Agents-A1-4B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json",
        "title": "Agents-A1-4B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b"
      },
      {
        "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-lfm2-5-2-6b.json",
        "title": "Apertus v1.5 8B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x.json",
        "title": "Apodex-1.1-mini vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json",
        "title": "Apodex-1.1-mini vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x.json",
        "title": "BigBang-v1 vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json",
        "title": "BigBang-v1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b.json",
        "title": "Carbon-X vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus.json",
        "title": "Carbon-X vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2.json",
        "title": "Carbon-X vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash.json",
        "title": "Carbon-X vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.json",
        "title": "Carbon-X vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Carbon-X vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-12b-it.json",
        "title": "Carbon-X vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it.json",
        "title": "Carbon-X vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it.json",
        "title": "Carbon-X vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.json",
        "title": "Carbon-X vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it.json",
        "title": "Carbon-X vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air.json",
        "title": "Carbon-X vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash.json",
        "title": "Carbon-X vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3.json",
        "title": "Carbon-X vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.json",
        "title": "Carbon-X vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-120b.json",
        "title": "Carbon-X vs gpt-oss-120b",
        "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",
        "title": "Carbon-X vs gpt-oss-20b",
        "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",
        "title": "Carbon-X vs Granite 4.2 3B",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-hy3.json",
        "title": "Carbon-X vs Hy3",
        "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",
        "title": "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",
        "title": "Carbon-X vs Kimi K3",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.json",
        "title": "Carbon-X vs Kolibri-1",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-m-1.json",
        "title": "Carbon-X vs Laguna M.1",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.json",
        "title": "Carbon-X vs Laguna S 2.1",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1.json",
        "title": "Carbon-X vs Laguna XS 2.1",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-0-tiny.json",
        "title": "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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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-o-4-5.json",
        "title": "Carbon-X vs MiniCPM-o 4.5",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-minicpm5-2b.json",
        "title": "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",
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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",
        "title": "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",
        "title": "Carbon-X vs Nemotron 3 Nano 4B",
        "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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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.json",
        "title": "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",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-397b.json",
        "title": "Carbon-X vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-9b.json",
        "title": "Carbon-X vs Ornith-1.5-9B",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Carbon-X vs Qwen3-30B-A3B-Instruct-2507",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-4b-instruct-2507.json",
        "title": "Carbon-X vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-0-8b.json",
        "title": "Carbon-X vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-122b-a10b.json",
        "title": "Carbon-X vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-2b.json",
        "title": "Carbon-X vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-397b-a17b.json",
        "title": "Carbon-X vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-397b-a17b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-4b.json",
        "title": "Carbon-X vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b.json",
        "title": "Carbon-X vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-27b.json",
        "title": "Carbon-X vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-35b-a3b.json",
        "title": "Carbon-X vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-27b.json",
        "title": "Carbon-X vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next.json",
        "title": "Carbon-X vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Carbon-X vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next.json",
        "title": "Carbon-X vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Carbon-X vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-2b-instruct.json",
        "title": "Carbon-X vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Carbon-X vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct.json",
        "title": "Carbon-X vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct.json",
        "title": "Carbon-X vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct.json",
        "title": "Carbon-X vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.json",
        "title": "Carbon-X vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.json",
        "title": "Carbon-X vs Spark-X2.6-4B",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Carbon-X vs Swift 1.5 Qwen3.8-27B",
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Carbon-X vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Carbon-X vs ThinkingCap (Qwen3.8 27B)",
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.json",
        "title": "GLM-4.7-Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json",
        "title": "GLM-5.3-Flash vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json",
        "title": "GLM-5.3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json",
        "title": "gpt-oss-120b vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.json",
        "title": "gpt-oss-20b vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json",
        "title": "Granite 4.2 3B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json",
        "title": "Hy3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json",
        "title": "Inkling-Small vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json",
        "title": "Inkling vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json",
        "title": "Kimi K3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.json",
        "title": "Kolibri-1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.json",
        "title": "Laguna M.1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.json",
        "title": "Laguna S 2.1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.json",
        "title": "Laguna XS 2.1 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.json",
        "title": "LFM2.5-2.6B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json",
        "title": "LFM2.5-2.6B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.json",
        "title": "LFM2.5-2.6B vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.json",
        "title": "LFM2.5-2.6B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.json",
        "title": "LFM2.5-2.6B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.json",
        "title": "LFM2.5-2.6B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json",
        "title": "LFM2.5-2.6B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json",
        "title": "LFM2.5-2.6B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.json",
        "title": "LFM2.5-2.6B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.json",
        "title": "LFM2.5-2.6B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json",
        "title": "LFM2.5-2.6B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.json",
        "title": "LFM2.5-2.6B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.json",
        "title": "LFM2.5-2.6B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json",
        "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.json",
        "title": "LFM2.5-2.6B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.json",
        "title": "LFM2.5-2.6B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.json",
        "title": "LFM2.5-2.6B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.json",
        "title": "LFM2.5-2.6B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json",
        "title": "LFM2.5-2.6B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json",
        "title": "LFM2.5-2.6B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "carbon-x": 90,
        "edge": "lfm2-5-2-6b",
        "key": "reach",
        "lfm2-5-2-6b": 95,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "carbon-x": 65,
        "edge": "carbon-x",
        "key": "risk",
        "lfm2-5-2-6b": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 40,
        "carbon-x": 35,
        "edge": "lfm2-5-2-6b",
        "key": "interface",
        "lfm2-5-2-6b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 20,
        "carbon-x": 77,
        "edge": "carbon-x",
        "key": "upkeep",
        "lfm2-5-2-6b": 57,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Carbon-X scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface. 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.",
      "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b",
    "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.min.md"
  },
  "markdown": "Carbon-X scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface. 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- LFM2.5-2.6B: grade D, 51.5/100, rank #94 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.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- Risk, 65 against 50\n- Documentation and upkeep, 77 against 57\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: Not clinically validated and no wet-lab validation reported\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 90\n- Agent interface, 75 against 35\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n\n## Score by category\n\n| Category | Weight | Carbon-X | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 95 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 65 | 50 | Carbon-X +15 |\n| Agent interface | 10% (10 this run) | 35 | 75 | LFM2.5-2.6B +40 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 57 | Carbon-X +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | Carbon-X | LFM2.5-2.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Hugging Face (HuggingFaceBio) | Liquid 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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-22 | 2026-09-22 |\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) | 2.5/5 (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**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\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### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Carbon-X or LFM2.5-2.6B?\n\nCarbon-X scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"carbon-x\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare carbon-x lfm2-5-2-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n\n## Other comparisons with Carbon-X or LFM2.5-2.6B\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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs Carbon-X](https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x.md)\n- [BigBang-v1 vs 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