{
  "data": {
    "a": {
      "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"
    },
    "answer": "Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk.",
    "b": {
      "slug": "wevisone-vl-2b",
      "name": "WeVisOne-VL-2B",
      "vendor": "Tencent (WeChat Vision)",
      "vendorUrl": "https://www.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "WeVisOne-VL-2B is an open-weight perception model of 2.4B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/wevisone-vl-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json",
      "dns": "wevisone-vl-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/WeVisOne-VL-2B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/WeVisOne-VL-2B"
        }
      ],
      "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 under Apache 2.0. Running it yourself costs the hardware, 4.88 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/Tencent/WeVisOne-VL",
      "capabilities": [
        "model.open-weights",
        "docs.ocr"
      ],
      "tags": [
        "open-weights",
        "model",
        "vision",
        "grounding",
        "object-detection",
        "segmentation",
        "matting",
        "ocr",
        "counting",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-10-28",
      "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": 65,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.",
        "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence",
        "strengths": [
          "Seven perception tasks in one model, from boxes to alpha mattes",
          "Returns None for a target that isn't in the image",
          "Apache 2.0, ungated",
          "Each task's input and output is documented",
          "4.88 GB in BF16, so it fits a 8 GB machine"
        ],
        "weaknesses": [
          "The pixel decoder ships as a pickle file",
          "Masks and mattes need Tencent's package from GitHub",
          "No quantised build from Tencent",
          "One image and one turn at a time, no video",
          "No reply from Tencent on the repository's discussions",
          "No context length, languages, safety results or data sources on the card"
        ],
        "agentNotes": [
          "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`",
          "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting",
          "A None answer means the target isn't in the image. Treat it as a result, not an error",
          "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's",
          "Send one image a turn. Video grids are rejected by the decoder",
          "The card says it isn't for identifying individuals or for high-impact decisions on its own"
        ],
        "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": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "provenanceScore": 68,
        "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"transformers\") or backend=\"vllm\"",
        "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-2B"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/wevisone-vl-2b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy-memory-30b-a3b",
        "hy3",
        "wevisone-vl-4b"
      ],
      "weights": {
        "trainedBy": "Tencent WeChat Vision",
        "params": "2.4B",
        "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 8 GB machine.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "WeVisOne-VL-2B (BF16 safetensors)",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.88,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package"
          }
        ],
        "run": "vllm serve tencent/WeVisOne-VL-2B",
        "runBuild": "WeVisOne-VL-2B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "WeVisOne-VL",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "others": [
          {
            "name": "WeVisOne-VL-4B",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-4B",
            "params": "4.8B (4,830,581,248 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/wevisone-vl-4b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/WeVisOne-VL-2B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.",
          "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.",
          "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.json"
    },
    "edges": [
      {
        "by": 15,
        "category": "reach",
        "leads": "spark-x2-6-4b",
        "name": "Reach",
        "reasons": {
          "a": "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.",
          "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "wevisone-vl-2b",
        "name": "Risk",
        "reasons": {
          "a": "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.",
          "b": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55."
        },
        "scores": {
          "a": 50,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "SparkLLM (XHToken)",
        "b": "Tencent (WeChat Vision)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-10-28",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk.",
        "question": "Which is better for AI agents, Spark-X2.6-4B or WeVisOne-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 70"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Risk, 55 against 50"
        ],
        "also": null,
        "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence",
        "slug": "wevisone-vl-2b",
        "watchFor": "The pixel decoder ships as a pickle file"
      }
    ],
    "history": {
      "spark-x2-6-4b": [
        {
          "causes": [
            "evidence"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "wevisone-vl-2b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "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/agents-a1-4b-vs-wevisone-vl-2b.json",
        "title": "Agents-A1-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b"
      },
      {
        "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/apertus-v1-5-8b-vs-wevisone-vl-2b.json",
        "title": "Apertus v1.5 8B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b"
      },
      {
        "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/apodex-1-1-mini-vs-wevisone-vl-2b.json",
        "title": "Apodex-1.1-mini vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b"
      },
      {
        "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/arete-27b-vs-wevisone-vl-2b.json",
        "title": "Arete-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b"
      },
      {
        "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/bigbang-v1-vs-wevisone-vl-2b.json",
        "title": "BigBang-v1 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b"
      },
      {
        "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/carbon-x-vs-wevisone-vl-2b.json",
        "title": "Carbon-X vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b.json",
        "title": "Cola2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json",
        "title": "Cola2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.json",
        "title": "Cosmos-Reason2-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.json",
        "title": "Cosmos-Reason2-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.json",
        "title": "Darwin-36B-Opus vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.json",
        "title": "Darwin-36B-Opus vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b.json",
        "title": "DeepSeek-V3.2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.json",
        "title": "DeepSeek-V3.2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.json",
        "title": "DeepSeek V4.1 Flash vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.json",
        "title": "DeepSeek V4.1 Flash vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-spark-x2-6-4b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.json",
        "title": "Devstral Small 2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.json",
        "title": "Devstral Small 2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b.json",
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      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.json",
        "title": "EXAONE 4.5 33B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b"
      },
      {
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json",
        "title": "Laguna M.1 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.json",
        "title": "LBS-27B vs Spark-X2.6-4B",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b"
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        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b"
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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"
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        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2"
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json",
        "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B",
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        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "spark-x2-6-4b",
        "key": "reach",
        "name": "Reach",
        "spark-x2-6-4b": 85,
        "weight": 25,
        "wevisone-vl-2b": 70
      },
      {
        "by": 5,
        "edge": "wevisone-vl-2b",
        "key": "risk",
        "name": "Risk",
        "spark-x2-6-4b": 50,
        "weight": 20,
        "wevisone-vl-2b": 55
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "spark-x2-6-4b": 70,
        "weight": 10,
        "wevisone-vl-2b": 70
      },
      {
        "by": 3,
        "edge": "spark-x2-6-4b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "spark-x2-6-4b": 51,
        "weight": 10,
        "wevisone-vl-2b": 48
      }
    ],
    "summary": "Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "wevisone-vl-2b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results."
    }
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  "markdown": "Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. Both do open-weight models you run yourself.\n\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- WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.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### 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 70\n\nWatch for: No safety results and no agent or prompt-injection guidance\n\n### WeVisOne-VL-2B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Risk, 55 against 50\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Reach: Spark-X2.6-4B +15\n\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- WeVisOne-VL-2B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Risk: WeVisOne-VL-2B +5\n\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- WeVisOne-VL-2B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | Spark-X2.6-4B | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 70 | Spark-X2.6-4B +15 |\n| Risk | 20% (20 this run) | 50 | 55 | WeVisOne-VL-2B +5 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 51 | 48 | Spark-X2.6-4B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Spark-X2.6-4B | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | SparkLLM (XHToken) | Tencent (WeChat Vision) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-28 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Spark-X2.6-4B\n\n- no change on 3 November 2026: new evidence\n\n### WeVisOne-VL-2B\n\nNo score change since it was listed.\n\n## Verdicts\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**WeVisOne-VL-2B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\n\n## Before you call either\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### WeVisOne-VL-2B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, Spark-X2.6-4B or WeVisOne-VL-2B?\n\nSpark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"spark-x2-6-4b\", \"b\": \"wevisone-vl-2b\"}`. From a terminal: `anchor compare spark-x2-6-4b wevisone-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json\n\n## Other comparisons with Spark-X2.6-4B or WeVisOne-VL-2B\n\n- [Agents-A1-4B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.md)\n- [Agents-A1-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.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- [Apertus v1.5 8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.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- [Apodex-1.1-mini vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.md)\n- [Arete-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.md)\n- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)\n- [BigBang-v1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.md)\n- [BigBang-v1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.md)\n- [Carbon-X vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.md)\n- [Carbon-X vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b.md)\n- [Cola2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b.md)\n- [Cola2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.md)\n- [Cosmos-Reason2-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.md)\n- [Darwin-36B-Opus vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.md)\n- [DeepSeek-V3.2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.md)\n- [DeepSeek V4.1 Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.md)\n- [DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-spark-x2-6-4b.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b.md)\n- [Devstral Small 2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.md)\n- [Devstral Small 2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.md)\n- [DiffusionGemma 26B A4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b.md)\n- [EXAONE 4.5 33B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.md)\n- [Gemma 4 12B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 26B A4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 31B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 E2B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 E4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b.md)\n- [GLM-4.5-Air vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b.md)\n- [GLM-4.5-Air vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-2b.md)\n- [GLM-4.7-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b.md)\n- [GLM-5.3-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b.md)\n- [GLM-5.3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.md)\n- [GLM-5.3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b.md)\n- [gpt-oss-120b vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.md)\n- [gpt-oss-120b vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b.md)\n- [gpt-oss-20b vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-spark-x2-6-4b.md)\n- [gpt-oss-20b vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 30B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b.md)\n- [Granite 4.2 30B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.md)\n- [Granite 4.2 3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 8B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-spark-x2-6-4b.md)\n- [Granite 4.2 8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b.md)\n- [Hy-Memory-30B-A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.md)\n- [Hy3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.md)\n- [Hy3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.md)\n- [Inkling-Small vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.md)\n- [Inkling-Small vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.md)\n- [Inkling vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.md)\n- [Inkling vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.md)\n- [Kimi K3.1 Pro vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.md)\n- [Kimi K3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.md)\n- [Kimi K3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.md)\n- [Kolibri-1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.md)\n- [Kolibri-1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.md)\n- [Kronos vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.md)\n- [Kronos vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.md)\n- [Laguna M.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.md)\n- [Laguna M.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.md)\n- [Laguna S 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.md)\n- [Laguna S 2.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.md)\n- [Laguna XS 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.md)\n- [Laguna XS 2.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b.md)\n- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.md)\n- [LBS-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.md)\n- [LFM2.5-2.6B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.md)\n- [LFM2.5-2.6B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b.md)\n- [LFM3-1.7B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-spark-x2-6-4b.md)\n- [LFM3-1.7B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.md)\n- [LFM3-Audio-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-spark-x2-6-4b.md)\n- [LFM3-Audio-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.md)\n- [LFM3-Omni-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b.md)\n- [LFM3-Omni-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.md)\n- [LFM3-VL-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.md)\n- [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md)\n- [Ling-3.0-tiny vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md)\n- [Ling-3.1-flash-1019 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.md)\n- [Ling-3.1-flash-1019 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.md)\n- [LongCat-2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.md)\n- [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-spark-x2-6-4b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6-Flash-RL vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.md)\n- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md)\n- [MiniCPM-o 4.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 1B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-spark-x2-6-4b.md)\n- [MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.md)\n- [MiniCPM5-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b.md)\n- [MiniCPM5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.7 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.7 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.md)\n- [MiniMax-M3.1-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b.md)\n- [MiniMax-M3.1-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.md)\n- 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