{
  "data": {
    "a": {
      "slug": "cosmos-reason2-2b",
      "name": "Cosmos-Reason2-2B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://github.com/nvidia-cosmos/cosmos-reason2",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Cosmos-Reason2-2B is NVIDIA's open-weight vision language model for physical AI and robotics, a 2.4B-parameter fine-tune of Qwen3-VL-2B-Instruct that reasons over video and images. The repository is gated behind a click-through licence.",
      "url": "https://www.anchorterminal.com/tools/cosmos-reason2-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json",
      "repo": "https://huggingface.co/nvidia/Cosmos-Reason2-2B",
      "license": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/Cosmos-Reason2-2B"
        }
      ],
      "auth": "none",
      "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account and token and accepting the NVIDIA Open Model License on the card, which Hugging Face approves automatically. The card, the file list and the licence 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 the NVIDIA Open Model License, with a contact for a custom licence. Running it costs the hardware, 4.9 GB of BF16 safetensors by the Hub's file list; NVIDIA states a minimum of 24 GB of GPU memory (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://github.com/nvidia-cosmos/cosmos-reason2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "vision",
        "video",
        "reasoning",
        "robotics",
        "physical-ai",
        "fine-tune",
        "safetensors",
        "gated",
        "self-hosted"
      ],
      "lastRelease": "2026-03-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.5,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 50,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 65,
          "risk": 60,
          "upkeep": 39
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.",
        "bestFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces",
        "strengths": [
          "4.9 GB of BF16 safetensors with no remote code, loaded by Transformers' own Qwen3-VL class",
          "Runs on released Transformers, vLLM, SGLang and llama.cpp through the Qwen3-VL architecture",
          "NVIDIA's prompt files give JSON formats for bounding boxes, timed events and robot trajectories",
          "Reasoning is prompted with a documented think and answer format and parsed by vLLM's qwen3 parser",
          "Commercial use allowed, files in NVIDIA's own organisation with hashes"
        ],
        "weaknesses": [
          "Gated behind a click-through licence that needs a Hugging Face account",
          "The licence is revocable, can be updated by NVIDIA and ends if a guardrail is bypassed",
          "NVIDIA says the repository is no longer under active development and points to Cosmos 3",
          "No quantised build from NVIDIA, no hosted endpoint for the 2B, and no NVIDIA reply in its discussions",
          "No documented tool calling, no safety results and no training data sizes or cut-off"
        ],
        "agentNotes": [
          "Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading",
          "Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises",
          "For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from",
          "Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off",
          "For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply",
          "Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself",
          "Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.5
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 65,
          "risk": 60,
          "upkeep": 39
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# accept the licence on the Hugging Face card first, then set HF_TOKEN\nvllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 \\\n  --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nvidia/Cosmos-Reason2-2B\", \"max_tokens\": 4096, \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"file:///path/to/frame.jpg\"}}, {\"type\": \"text\", \"text\": \"Is it safe to turn right?\"}]}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/cosmos-reason2-2b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "parakeet-tdt-0-6b-v3",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "Qwen/Qwen3-VL-2B-Instruct (fine-tune, Apache 2.0)",
        "params": "2.44B",
        "context": 262144,
        "goodFor": "Small vision language model for physical AI, reasoning over video and images for robots, driving and smart spaces, with point and box outputs.",
        "runtimes": [
          "Transformers",
          "vLLM",
          "SGLang",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Cosmos-Reason2-2B",
            "url": "https://huggingface.co/nvidia/Cosmos-Reason2-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.9,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Gated (click-through). NVIDIA states a minimum of 24 GB of GPU memory, more than the file size suggests, presumably for video input"
          }
        ],
        "run": "vllm serve nvidia/Cosmos-Reason2-2B --allowed-local-media-path \"$(pwd)/media\" --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3 --port 8000",
        "runBuild": "Cosmos-Reason2-2B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Cosmos-Reason2",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Cosmos-Reason2-8B",
            "url": "https://huggingface.co/nvidia/Cosmos-Reason2-8B",
            "params": "8B (8.77B per the safetensors count)"
          },
          {
            "name": "Cosmos-Reason2-32B",
            "url": "https://huggingface.co/nvidia/Cosmos-Reason2-32B",
            "params": "32B (33.36B per the safetensors count)"
          }
        ],
        "olderCount": 43,
        "unrestrictedCopies": 3
      },
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/nvidia-cosmos/cosmos-reason2/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA as model developer and the licence names NVIDIA Corporation. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.",
          "Nothing is hosted for this listing, so the NVIDIA Open Model License stands in for terms of service and no privacy policy is scored.",
          "The Hugging Face commit history is behind the gate, so the changelog is the GitHub repository's, whose README carries dated news entries from 19 December 2025 to 1 June 2026.",
          "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/cosmos-reason2-2b.json"
    },
    "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.",
    "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": 1197,
        "categoryRank": 71,
        "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"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "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": "NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under 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": "2026-03-10",
        "b": "2026-09-22",
        "name": "Last release"
      },
      {
        "a": "",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Cosmos-Reason2-2B or LFM2.5-2.6B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 60 against 50"
        ],
        "also": null,
        "goodFor": "Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces",
        "slug": "cosmos-reason2-2b",
        "watchFor": "Gated behind a click-through licence that needs a Hugging Face account"
      },
      {
        "aheadOn": [
          "Reach, 95 against 65",
          "Agent interface, 75 against 65",
          "Documentation and upkeep, 57 against 39"
        ],
        "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": [
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        "title": "Agents-A1-4B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b"
      },
      {
        "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-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-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"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.json",
        "title": "Apodex-1.1-mini vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b"
      },
      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b.json",
        "title": "BigBang-v1 vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-darwin-36b-opus.json",
        "title": "Cosmos-Reason2-2B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-darwin-36b-opus"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731.json",
        "title": "Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-diffusiongemma-26b-a4b-it"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-12b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.json",
        "title": "Cosmos-Reason2-2B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.json",
        "title": "Cosmos-Reason2-2B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3.json",
        "title": "Cosmos-Reason2-2B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.json",
        "title": "Cosmos-Reason2-2B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-120b.json",
        "title": "Cosmos-Reason2-2B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-120b"
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        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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-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": 30,
        "cosmos-reason2-2b": 65,
        "edge": "lfm2-5-2-6b",
        "key": "reach",
        "lfm2-5-2-6b": 95,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "cosmos-reason2-2b": 60,
        "edge": "cosmos-reason2-2b",
        "key": "risk",
        "lfm2-5-2-6b": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "cosmos-reason2-2b": 65,
        "edge": "lfm2-5-2-6b",
        "key": "interface",
        "lfm2-5-2-6b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 18,
        "cosmos-reason2-2b": 39,
        "edge": "lfm2-5-2-6b",
        "key": "upkeep",
        "lfm2-5-2-6b": 57,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "cosmos-reason2-2b": "A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.",
      "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/cosmos-reason2-2b-vs-lfm2-5-2-6b",
    "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.min.md"
  },
  "markdown": "Cosmos-Reason2-2B scores 59.5 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cosmos-Reason2-2B: grade C, 59.5/100, rank #59 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cosmos-reason2-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json\n- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Cosmos-Reason2-2B (C)\n\nGood for: Agents that need a small local model to reason over camera footage and images for robotics, driving or smart spaces\n\nAhead on:\n- Risk, 60 against 50\n\nWatch for: Gated behind a click-through licence that needs a Hugging Face account\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 65\n- Agent interface, 75 against 65\n- Documentation and upkeep, 57 against 39\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 | Cosmos-Reason2-2B | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 95 | LFM2.5-2.6B +30 |\n| Risk | 20% (20 this run) | 60 | 50 | Cosmos-Reason2-2B +10 |\n| Agent interface | 10% (10 this run) | 65 | 75 | LFM2.5-2.6B +10 |\n| Documentation and upkeep | 10% (10 this run) | 39 | 57 | LFM2.5-2.6B +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.5 · C** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | Cosmos-Reason2-2B | LFM2.5-2.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | 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 | NVIDIA Open Model License (custom, commercial use allowed, not an OSI licence); built on Qwen3-VL-2B-Instruct under 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 | 2026-03-10 | 2026-09-22 |\n| Terms last updated |  | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.5/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Cosmos-Reason2-2B.** A small local cousin for reasoning over video and images in robotics, driving and warehouse scenes, returning points, boxes and timed events as JSON. The 4.9 GB file runs in Transformers or vLLM, though NVIDIA asks for 24 GB of GPU memory. Development has moved to Cosmos 3, and the gated licence is revocable.\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 $10M of revenue need another licence. There are no safety results or security contact.\n\n## Before you call either\n\n### Cosmos-Reason2-2B\n\n1. Accept the licence on the Hugging Face card with a signed-in account and set HF_TOKEN before downloading\n2. Serve it with `vllm serve nvidia/Cosmos-Reason2-2B --max-model-len 16384 --media-io-kwargs '{\"video\": {\"num_frames\": -1}}' --reasoning-parser qwen3`, keeping max-model-len between 8,192 and 16,384 as NVIDIA advises\n3. For local video or images, make a folder for them (`mkdir -p media`) and pass `--allowed-local-media-path \"$(pwd)/media\"`, so the server can read only that folder, not everything under the directory you start it from\n4. Sample video at 4 fps and allow at least 4,096 output tokens so the reasoning isn't cut off\n5. For reasoning, add the card's instruction to answer with a think block and then the answer; leave it out for a direct reply\n6. Use NVIDIA's prompt files (prompts/*.yaml in nvidia-cosmos/cosmos-reason2) when you want JSON boxes, events or trajectories, and validate the JSON yourself\n7. Check its plans before a robot acts on them, since NVIDIA's own safety card warns of collision and manipulation risks\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, Cosmos-Reason2-2B or LFM2.5-2.6B?\n\nCosmos-Reason2-2B scores 59.5 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cosmos-reason2-2b\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare cosmos-reason2-2b lfm2-5-2-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cosmos-reason2-2b.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n\n## Other comparisons with Cosmos-Reason2-2B or LFM2.5-2.6B\n\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.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 Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.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 Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.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 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