{
  "data": {
    "a": {
      "slug": "exaone-4-5-33b",
      "name": "EXAONE 4.5 33B",
      "vendor": "LG AI Research",
      "vendorUrl": "https://www.lgresearch.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "EXAONE 4.5 33B is LG AI Research's open-weight vision-language model, 34B parameters, with tool calling, switchable reasoning and a 256K context, strong on documents and Korean. Its licence allows research and education only, with LG's own GGUF from 18 GB.",
      "url": "https://www.anchorterminal.com/tools/exaone-4-5-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json",
      "dns": "exaone-4-5-33b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
      "license": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account or token. None of the four repositories is gated, though the licence says downloading is agreeing to it (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download for research and education. The EXAONE AI Model License Agreement 1.2 - NC prohibits commercial use of the model, its derivatives and its output without a separate agreement with LG. Running it yourself takes 20.6 GB at IQ4_XS with its projector, up to 68.7 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/LG-AI-EXAONE/EXAONE-4.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "documents",
        "korean",
        "reasoning",
        "tool-calling",
        "long-context",
        "non-commercial",
        "safetensors",
        "gguf",
        "awq",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 51,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
        "bestFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "strengths": [
          "A complete card, down to a knowledge cut-off",
          "GGUF, AWQ and FP8 builds from LG",
          "Released vLLM, Transformers and llama.cpp run it with no repository code",
          "Tool calling and reasoning control documented with parsers",
          "A 256K context, served on one large GPU"
        ],
        "weaknesses": [
          "Research and education only. No commercial use of the model or its output",
          "LG can amend the licence at any time, and it's revocable",
          "No building models that compete with LG's",
          "No reply from LG on the repository's discussions",
          "Not changed since the summer",
          "No safety results or training data sources on the card"
        ],
        "agentNotes": [
          "Don't use it, or anything it writes, commercially. The licence covers the output too",
          "Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`",
          "Set `enable_thinking` false for a direct answer. Thinking is on by default",
          "Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean",
          "For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input",
          "Its knowledge stops at December 2024"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"vllm\u003e=0.33.0\" \"transformers\u003e=5.8.0\"\nvllm serve LGAI-EXAONE/EXAONE-4.5-33B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. The card recommends temperature 1.0,\n# top_p 0.95 and presence_penalty 1.5, and enable_thinking false for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/exaone-4-5-33b"
      },
      "weights": {
        "trainedBy": "LG AI Research",
        "params": "34B",
        "context": 262144,
        "goodFor": "Research on document understanding, Korean and agent work with images, where non-commercial terms are acceptable.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "EXAONE-4.5-33B-GGUF IQ4_XS",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 20.6,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "18.03 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22.62,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "20.05 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-AWQ",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-AWQ",
            "who": "publisher",
            "precision": "awq",
            "sizeGB": 22.75,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's 4-bit AWQ build"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q8_0",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 37.71,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "35.14 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-FP8",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 41.31,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's FP8 build"
          },
          {
            "name": "EXAONE-4.5-33B (BF16 safetensors)",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 68.7,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ],
            "note": "Two shards"
          }
        ],
        "run": "llama-server -hf LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M --jinja",
        "runBuild": "EXAONE-4.5-33B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "LG AI Research",
        "family": "EXAONE 4",
        "url": "https://www.anchorterminal.com/open-weights/#lg-ai-research",
        "others": [
          {
            "name": "EXAONE-4.0.1-32B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B"
          },
          {
            "name": "EXAONE-4.0-1.2B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "LG Management Development Institute Co., Ltd., the licensor named in the model's licence. The card names LG AI Research",
        "domain": "lgresearch.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card and the Hub organisation name LG AI Research, and the licence names LG Management Development Institute Co., Ltd. as licensor. How the two relate wasn't read first-hand.",
          "www.lgresearch.ai's robots.txt allows reading. Its home page gave little text and its about page a 404, so no people or ownership facts were taken from it.",
          "The domain's registration date wasn't looked up. www.lgresearch.ai has no security.txt (404 on 10 November 2026).",
          "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 8 commits from 9 April to 6 July 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.json"
    },
    "answer": "EXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach.",
    "b": {
      "slug": "spark-x2-6-4b",
      "name": "Spark-X2.6-4B",
      "vendor": "SparkLLM (XHToken)",
      "vendorUrl": "https://huggingface.co/XHToken",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Spark-X2.6-4B is SparkLLM's open-weight 4B text model, Spark-X2.5-4B's architecture with new post-training, a 1M-token context and three thinking levels, on Hugging Face with FP8, INT8, INT4 and GGUF builds under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/spark-x2-6-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json",
      "dns": "spark-x2-6-4b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XHToken/Spark-X2.6-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XHToken/Spark-X2.6-4B"
        },
        {
          "registry": "huggingface",
          "name": "XHToken/Spark-X2.6-4B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "XHToken/Spark-X2.6-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "XHToken/Spark-X2.6-4B-INT8"
        },
        {
          "registry": "huggingface",
          "name": "XHToken/Spark-X2.6-4B-INT4"
        },
        {
          "registry": "ollama",
          "name": "SparkLLM/Spark-X2.6-4B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the six X2.6 repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Nothing is hosted by SparkLLM or by an inference provider listed on Hugging Face. Running it yourself costs the hardware, 8.24 GB for the BF16 files and 3.19 GB for SparkLLM's Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/XHToken/Spark-X2.6-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "long-context",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 46,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.",
        "bestFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.",
        "strengths": [
          "SparkLLM's Q4_K_M GGUF fits in about 4 GB, and FP8, INT8, INT4 and Q8_0 builds come from the publisher",
          "Plain Apache 2.0 on its own base, with no gate or account",
          "1,048,576 tokens of context with sliding-window layers that keep the cache small",
          "Released support in llama.cpp, SGLang and Ollama, with SGLang's own spark25 tool parser",
          "Thinking that turns off per request or runs at low or high effort, and several tool calls a turn",
          "A private security reporting route in SparkLLM's GitHub"
        ],
        "weaknesses": [
          "No safety results and no agent or prompt-injection guidance",
          "Transformers and vLLM need code from SparkLLM's repositories",
          "Tool format and structured output aren't documented, and the template uses its own arg_key format",
          "The card has no limitations, cut-off, architecture description or training data, and leans on X2.5's",
          "No independent results yet, and every published score is SparkLLM's own chart"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)",
          "For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs",
          "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low",
          "SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1",
          "The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list",
          "The template raises an error if tool_call arguments are a JSON string, so pass them as an object",
          "Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 44,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072\n# or, with SGLang 0.5.22 or later:\npython -m sglang.launch_server --model-path XHToken/Spark-X2.6-4B \\\n  --tool-call-parser spark25 --reasoning-parser qwen3 --context-length 131072",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"XHToken/Spark-X2.6-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/spark-x2-6-4b"
      },
      "weights": {
        "trainedBy": "SparkLLM (XHToken)",
        "builtOn": "XHToken/Spark-X2.6-4B-Base (SparkLLM's own checkpoint, Apache 2.0, declared as the base in the card's metadata). The card's text says X2.6 builds on Spark-X2.5-4B with improved post-training. The Base repository's card is a cut-down copy of this card that calls its files the post-trained model",
        "params": "4.11B",
        "context": 1048576,
        "goodFor": "Small dense text model for coding, tool use and agent loops on a laptop or small GPU, with a 1M-token context and thinking that is off, low or high per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Spark-X2.6-4B-FP8",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 4.47,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "compressed-tensors float-quantized"
          },
          {
            "name": "Spark-X2.6-4B-INT8",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT8",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 4.47,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "compressed-tensors int-quantized, 8-bit weights and activations"
          },
          {
            "name": "Spark-X2.6-4B-INT4",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 3.43,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "compressed-tensors pack-quantized, 4-bit weights in groups of 32 on the attention and MLP projections, the rest in BF16"
          },
          {
            "name": "Spark-X2.6-4B-Q4_K_M.gguf",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.19,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ],
            "note": "The smallest build at 4-bit or better. 0.59 GB larger than X2.5's Q4_K_M"
          },
          {
            "name": "Spark-X2.6-4B-Q8_0.gguf",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 4.38,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ]
          },
          {
            "name": "Spark-X2.6-4B",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.24,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "SGLang"
            ],
            "note": "Transformers loads it only with trust_remote_code (modeling_spark.py), and vLLM only with SparkLLM's out-of-tree plugin"
          },
          {
            "name": "Spark-X2.6-4B.gguf",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.23,
            "memoryGB": 12,
            "format": "gguf"
          },
          {
            "name": "ollama SparkLLM/Spark-X2.6-4B:latest",
            "url": "https://ollama.com/SparkLLM/Spark-X2.6-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.2,
            "memoryGB": 12,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "SparkLLM's own namespace on Ollama, not Ollama's library. The only tag is 8.2 GB (digest 44ca4d700630), so it is the unquantised build; needs Ollama 0.34.1 or later. Size as Ollama shows it"
          }
        ],
        "run": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072",
        "runBuild": "Spark-X2.6-4B-Q8_0.gguf",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "SparkLLM (XHToken)",
        "family": "Spark-X2 (X2.5, X2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#sparkllm",
        "others": [
          {
            "name": "Spark-X2.6-4B-Base",
            "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-Base",
            "params": "4B (Hub safetensors count 4.11B)"
          },
          {
            "name": "Spark-X2.5-4B-Base",
            "url": "https://huggingface.co/XHToken/Spark-X2.5-4B-Base",
            "params": "4B (Hub safetensors count 4.11B)"
          },
          {
            "name": "Spark-X2.5-1.7B-Base",
            "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B-Base",
            "params": "1.7B (Hub safetensors count 1.71B)"
          },
          {
            "name": "Spark-X2.5-4B",
            "url": "https://huggingface.co/XHToken/Spark-X2.5-4B",
            "params": "4B (Hub safetensors count 4.11B)"
          },
          {
            "name": "Spark-X2.5-1.7B",
            "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B",
            "params": "1.7B (Hub safetensors count 1.71B)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "XHToken (the licence's copyright line); the Hugging Face organisation's name is SparkLLM",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation XHToken (fullname SparkLLM, 18 members) isn't verified. No company website was found on the card or the organisation page; the card links Slack, Discord, YouTube, dev.to, Bluesky, X, Zhihu and WeChat, and SparkLLM's GitHub repositories.",
          "SparkLLM's GitHub organisation XHToken documents its Hugging Face repositories by name (Spark-MLX-LLM's README), and the X2.6 card points to Spark-plugin, Spark-MLX-LLM and XHToken/LlamaFactory there, which ties the two together.",
          "XHToken/community on GitHub has a SECURITY.md with a private advisory route and a security email, which Spark-plugin's SECURITY.md points to.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commits from 19 and 20 October 2026 are uploads titled only \"Upload Spark-X2.6-4B model files\"."
        ],
        "score": 44
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "reach",
        "leads": "spark-x2-6-4b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.",
          "b": "Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it."
        },
        "scores": {
          "a": 65,
          "b": 85
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "exaone-4-5-33b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "LG AI Research",
        "b": "SparkLLM (XHToken)",
        "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": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-06",
        "b": "2026-10-20",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "EXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach.",
        "question": "Which is better for AI agents, EXAONE 4.5 33B or Spark-X2.6-4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "slug": "exaone-4-5-33b",
        "watchFor": "Research and education only. No commercial use of the model or its output"
      },
      {
        "aheadOn": [
          "Reach, 85 against 65"
        ],
        "also": null,
        "goodFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.",
        "slug": "spark-x2-6-4b",
        "watchFor": "No safety results and no agent or prompt-injection guidance"
      }
    ],
    "history": {
      "exaone-4-5-33b": null,
      "spark-x2-6-4b": [
        {
          "causes": [
            "evidence"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.json",
        "title": "Agents-A1-4B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.json",
        "title": "Agents-A1-4B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.json",
        "title": "Apertus v1.5 8B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.json",
        "title": "Apertus v1.5 8B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.json",
        "title": "Apodex-1.1-mini vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.json",
        "title": "Apodex-1.1-mini vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json",
        "title": "Arete-27B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.json",
        "title": "Arete-27B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b.json",
        "title": "BigBang-v1 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
        "title": "Cola2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-spark-x2-6-4b.json",
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        "title": "Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.json",
        "title": "Qwen3.8-Flash-Next vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b.json",
        "title": "Qwen3-Coder-Next vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-2B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-32B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json",
        "title": "Recursive Flint vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json",
        "title": "Ripple-65B Preview vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json",
        "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json",
        "title": "SmolLM3-3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json",
        "title": "Spark-X2.6-4B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json",
        "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json",
        "title": "Spark-X2.6-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "spark-x2-6-4b",
        "exaone-4-5-33b": 65,
        "key": "reach",
        "name": "Reach",
        "spark-x2-6-4b": 85,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "exaone-4-5-33b": 50,
        "key": "risk",
        "name": "Risk",
        "spark-x2-6-4b": 50,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 85,
        "key": "interface",
        "name": "Agent interface",
        "spark-x2-6-4b": 70,
        "weight": 10
      },
      {
        "by": 1,
        "edge": "spark-x2-6-4b",
        "exaone-4-5-33b": 50,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "spark-x2-6-4b": 51,
        "weight": 10
      }
    ],
    "summary": "EXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "exaone-4-5-33b": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
      "spark-x2-6-4b": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data."
    }
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  "links": {
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  "markdown": "EXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach. Both do open-weight models you run yourself.\n\n- EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json\n- Spark-X2.6-4B: grade C, 61/100, rank #56 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### EXAONE 4.5 33B (C)\n\nGood for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights\n\nAhead on:\n- Agent interface, 85 against 70\n\nWatch for: Research and education only. No commercial use of the model or its output\n\n### Spark-X2.6-4B (C)\n\nGood for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.\n\nAhead on:\n- Reach, 85 against 65\n\nWatch for: No safety results and no agent or prompt-injection guidance\n\n\n## Why the edge\n\n### Reach: Spark-X2.6-4B +20\n\n- EXAONE 4.5 33B (65): Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.\n- Spark-X2.6-4B (85): Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it.\n\n### Agent interface: EXAONE 4.5 33B +15\n\n- EXAONE 4.5 33B (85): Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Spark-X2.6-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | Spark-X2.6-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Spark-X2.6-4B +20 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 85 | 70 | EXAONE 4.5 33B +15 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 51 | Spark-X2.6-4B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | Spark-X2.6-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | SparkLLM (XHToken) |\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 | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-10-20 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### EXAONE 4.5 33B\n\nNo score change since it was listed.\n\n### Spark-X2.6-4B\n\n- no change on 3 November 2026: new evidence\n\n## Verdicts\n\n**EXAONE 4.5 33B.** A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.\n\n**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.\n\n## Before you call either\n\n### EXAONE 4.5 33B\n\n1. Don't use it, or anything it writes, commercially. The licence covers the output too\n2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`\n3. Set `enable_thinking` false for a direct answer. Thinking is on by default\n4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean\n5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input\n6. Its knowledge stops at December 2024\n\n### Spark-X2.6-4B\n\n1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)\n2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low\n4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1\n5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list\n6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object\n7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights\n\n## Questions\n\n### Which is better for AI agents, EXAONE 4.5 33B or Spark-X2.6-4B?\n\nEXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"spark-x2-6-4b\"}`. From a terminal: `anchor compare exaone-4-5-33b spark-x2-6-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n\n## Other comparisons with EXAONE 4.5 33B or Spark-X2.6-4B\n\n- [Agents-A1-4B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.md)\n- [Agents-A1-4B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.md)\n- [Apertus v1.5 8B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.md)\n- [Apertus v1.5 8B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.md)\n- [Apodex-1.1-mini vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.md)\n- [Apodex-1.1-mini vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.md)\n- [Arete-27B vs EXAONE 4.5 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    "description": "EXAONE 4.5 33B and Spark-X2.6-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "EXAONE 4.5 33B vs Spark-X2.6-4B for AI agents (2026) | Anchor Terminal",
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    "updated": "2026-11-10",
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