{
  "data": {
    "a": {
      "slug": "ling-3-1-flash-1019",
      "name": "Ling-3.1-flash-1019",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json",
      "dns": "ling-3-1-flash-1019.grade.anchorterminal.com",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.1-flash-1019"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "long-context",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.8,
        "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": 94,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.",
        "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "strengths": [
          "Plain MIT, ungated, with a hash for every shard",
          "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture",
          "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook",
          "A training-content summary that names source categories, the size band and a collection date",
          "Updated a week before this check"
        ],
        "weaknesses": [
          "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it",
          "The card says 1M tokens of context where the files give 262,144",
          "The tool-call format is only in the template",
          "No safety results, no agent guidance and no security contact",
          "No inference provider mapped for this release, only for the earlier one",
          "Neither question on the repository has a reply"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows",
          "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`",
          "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook",
          "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook",
          "Use temperature 1.0, top_p 0.95 and top_k 20, per the card",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 54.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "provenanceScore": 61,
        "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": "# with SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n  --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n  --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-1-flash-1019"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "lbs-27b",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)",
        "params": "560B (25B active)",
        "context": 262144,
        "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 357.97,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Ling-3.1-flash-1019",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 573.78,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN",
        "runBuild": "Ling-3.1-flash-1019",
        "checked": "2026-11-10",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ling 3",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ling-3.0-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-Fin",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-VL",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL",
            "params": "124B total, 5.5B active"
          },
          {
            "name": "Ling-3.0-flash-dspark",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark",
            "params": "1.36B (draft)"
          },
          {
            "name": "Ling-3.0-flash-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe",
            "params": "5.18M (probe)"
          },
          {
            "name": "Ling-3.0-tiny",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "params": "7.9B total, 1.3B active",
            "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny"
          },
          {
            "name": "Ling-3.0-tiny-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe",
            "params": "3.22M (probe)"
          },
          {
            "name": "Ling-3.1-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash",
            "params": "560B total, 25B active"
          },
          {
            "name": "Ling-3.1-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.",
          "Nothing is hosted by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.",
          "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json"
    },
    "answer": "Spark-X2.6-4B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
    "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": 25,
        "category": "reach",
        "leads": "spark-x2-6-4b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.",
          "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": 60,
          "b": 85
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "spark-x2-6-4b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "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": 60,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "spark-x2-6-4b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.",
          "b": "File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories."
        },
        "scores": {
          "a": 45,
          "b": 50
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "ling-3-1-flash-1019",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.",
          "b": "Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context."
        },
        "scores": {
          "a": 56,
          "b": 51
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "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": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-04",
        "b": "2026-10-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Spark-X2.6-4B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Ling-3.1-flash-1019 or Spark-X2.6-4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 56 against 51"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "slug": "ling-3-1-flash-1019",
        "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it"
      },
      {
        "aheadOn": [
          "Reach, 85 against 60",
          "Risk, 50 against 45",
          "Agent interface, 70 against 60"
        ],
        "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": {
      "ling-3-1-flash-1019": 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-ling-3-1-flash-1019.json",
        "title": "Agents-A1-4B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019"
      },
      {
        "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-ling-3-1-flash-1019.json",
        "title": "Apertus v1.5 8B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019"
      },
      {
        "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-ling-3-1-flash-1019.json",
        "title": "Apodex-1.1-mini vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019"
      },
      {
        "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-ling-3-1-flash-1019.json",
        "title": "Arete-27B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019"
      },
      {
        "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-ling-3-1-flash-1019.json",
        "title": "BigBang-v1 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.json",
        "title": "BigBang-v1 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.json",
        "title": "Carbon-X vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.json",
        "title": "Carbon-X vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019"
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        "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"
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      {
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      {
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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-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-1-flash-1019.json",
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        "title": "MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-V 4.7 1B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b.json",
        "title": "MiniCPM5-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.7 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M3.1-Flash vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b.json",
        "title": "Mistral Large 4 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.json",
        "title": "Muse Glimmer 30B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b.json",
        "title": "Nanbeige4.2-3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.json",
        "title": "Nanbeige4.3-33B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano 4B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b.json",
        "title": "North Mini Code 1.0 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json",
        "title": "occamy-1.0 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.json",
        "title": "Olmo 3 7B Think vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-122B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-397B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-spark-x2-6-4b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-spark-x2-6-4b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-0.8B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-122B-A10B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-397B-A17B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-4B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-spark-x2-6-4b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.6-27B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.6-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.8-27B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json",
        "title": "Spark-X2.6-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json",
        "title": "Spark-X2.6-4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b"
      },
      {
        "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"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "spark-x2-6-4b",
        "key": "reach",
        "ling-3-1-flash-1019": 60,
        "name": "Reach",
        "spark-x2-6-4b": 85,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "spark-x2-6-4b",
        "key": "risk",
        "ling-3-1-flash-1019": 45,
        "name": "Risk",
        "spark-x2-6-4b": 50,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "spark-x2-6-4b",
        "key": "interface",
        "ling-3-1-flash-1019": 60,
        "name": "Agent interface",
        "spark-x2-6-4b": 70,
        "weight": 10
      },
      {
        "by": 5,
        "edge": "ling-3-1-flash-1019",
        "key": "upkeep",
        "ling-3-1-flash-1019": 56,
        "name": "Documentation and upkeep",
        "spark-x2-6-4b": 51,
        "weight": 10
      }
    ],
    "summary": "Spark-X2.6-4B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ling-3-1-flash-1019": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has 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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b",
    "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "Spark-X2.6-4B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.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### Ling-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nAhead on:\n- Documentation and upkeep, 56 against 51\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\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 60\n- Risk, 50 against 45\n- Agent interface, 70 against 60\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 +25\n\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\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: Spark-X2.6-4B +10\n\n- Ling-3.1-flash-1019 (60): Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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### Risk: Spark-X2.6-4B +5\n\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n- Spark-X2.6-4B (50): File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.\n\n### Documentation and upkeep: Ling-3.1-flash-1019 +5\n\n- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.\n- Spark-X2.6-4B (51): Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context.\n\n## Score by category\n\n| Category | Weight | Ling-3.1-flash-1019 | Spark-X2.6-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 85 | Spark-X2.6-4B +25 |\n| Risk | 20% (20 this run) | 45 | 50 | Spark-X2.6-4B +5 |\n| Agent interface | 10% (10 this run) | 60 | 70 | Spark-X2.6-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 51 | Ling-3.1-flash-1019 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.1-flash-1019 | Spark-X2.6-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-04 | 2026-10-20 |\n| Terms last updated | no document linked | 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### Ling-3.1-flash-1019\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**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has 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### Ling-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\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, Ling-3.1-flash-1019 or Spark-X2.6-4B?\n\nSpark-X2.6-4B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-1-flash-1019\", \"b\": \"spark-x2-6-4b\"}`. From a terminal: `anchor compare ling-3-1-flash-1019 spark-x2-6-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n\n## Other comparisons with Ling-3.1-flash-1019 or Spark-X2.6-4B\n\n- [Agents-A1-4B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.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 Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.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 Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.md)\n- [Apodex-1.1-mini vs 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