{
  "data": {
    "a": {
      "slug": "gemma-4-e2b-it",
      "name": "Gemma 4 E2B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-e2b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json",
      "dns": "gemma-4-e2b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-E2B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:e2b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 11.4 GB at BF16, 5.7 GB at SFP8, 2.9 GB at Q4_0, 1.1 GB for the mobile build and 0.84 GB for the mobile build text only to load the weights per Google (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://gemma.ai/models/gemma-4/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "on-device",
        "multimodal",
        "audio",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.4,
        "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": 70,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 19,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.",
        "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 5.2 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser",
          "131,072 tokens of native context and thinking that turns on or off per request",
          "Takes audio as well as images and video frames"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "No hosted version, since the Gemini API serves only the two largest sizes",
          "Training data described only by type and cut-off",
          "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks",
          "Chat-template reports since June wait without a reply"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio",
          "With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.4
          }
        ],
        "editorialScores": {
          "capability": 19,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:e2b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:e2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-e2b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-31b-it",
        "gemma-4-e4b-it"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-E2B",
        "params": "5.1B",
        "context": 131072,
        "goodFor": "Small on-device assistant and agent model, 2.3B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-E2B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-q4_0-gguf/blob/main/gemma-4-E2B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 4.34,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 3.35 GB model and the 0.99 GB projector (gemma-4-E2B-it-mmproj.gguf) for image and audio input"
          },
          {
            "name": "gemma-4-E2B-it-qat-mobile-ct",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-ct",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 2.7,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format"
          },
          {
            "name": "gemma-4-E2B-it-qat-mobile-transformers",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-transformers",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 2.49,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM"
          },
          {
            "name": "ollama gemma4:e2b",
            "url": "https://ollama.com/library/gemma4:e2b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 4.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights (3.5 GB) with a BF16 vision projector (987 MB) and a Q8_0 draft model (98 MB) (digest b37049369adf), the llama.cpp variant. gemma4:e2b-it-qat (4.3 GB) is Ollama's copy of Google's Q4_0 QAT build"
          },
          {
            "name": "lmstudio google/gemma-4-e2b",
            "url": "https://lmstudio.ai/models/google/gemma-4-e2b",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 4.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E2B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio"
          },
          {
            "name": "gemma-4-E2B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 8.35,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 10.28,
            "memoryGB": 16,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072",
        "runBuild": "gemma-4-E2B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.json",
      "live": {
        "slug": "gemma-4-e2b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:43:14.713838946Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
      }
    },
    "answer": "WeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.",
    "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": 27,
        "category": "upkeep",
        "leads": "gemma-4-e2b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.",
          "b": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48."
        },
        "scores": {
          "a": 75,
          "b": 48
        }
      },
      {
        "by": 25,
        "category": "reach",
        "leads": "gemma-4-e2b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.",
          "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": 95,
          "b": 70
        }
      },
      {
        "by": 15,
        "category": "risk",
        "leads": "gemma-4-e2b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.",
          "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": 70,
          "b": 55
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "gemma-4-e2b-it",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.",
          "b": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "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": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "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": "3.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "WeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Gemma 4 E2B IT or WeVisOne-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 70",
          "Risk, 70 against 55",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 75 against 48"
        ],
        "also": null,
        "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "slug": "gemma-4-e2b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": null,
        "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": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-2b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b.json",
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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-4-7-flash-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b.json",
        "title": "GLM-5.3 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b.json",
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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/gpt-oss-20b-vs-wevisone-vl-2b.json",
        "title": "gpt-oss-20b vs WeVisOne-VL-2B",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b"
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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/inkling-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-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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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b"
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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-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json",
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        "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-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-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.json",
        "title": "LBS-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.json",
        "title": "LFM3-1.7B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-Audio-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-Omni-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.json",
        "title": "Ling-3.0-tiny vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b"
      },
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json",
        "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.json",
        "title": "Nanbeige4.3-33B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.json",
        "title": "North Mini Code 1.0 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-397B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-9B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-9B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.6-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.8-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json",
        "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Coder-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b"
      },
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json",
        "title": "Recursive Flint vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json",
        "title": "SmolLM3-3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b"
      },
      {
        "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/step-3-7-flash-vs-wevisone-vl-2b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json",
        "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 95,
        "key": "reach",
        "name": "Reach",
        "weight": 25,
        "wevisone-vl-2b": 70
      },
      {
        "by": 15,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 70,
        "key": "risk",
        "name": "Risk",
        "weight": 20,
        "wevisone-vl-2b": 55
      },
      {
        "by": 15,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10,
        "wevisone-vl-2b": 70
      },
      {
        "by": 27,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 75,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10,
        "wevisone-vl-2b": 48
      }
    ],
    "summary": "WeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-e2b-it": "A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "html": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  },
  "markdown": "WeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Gemma 4 E2B IT: grade C, 60.4/100, rank #88 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.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### Gemma 4 E2B IT (C)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 70\n- Risk, 70 against 55\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 48\n\nWatch for: No safety evaluation figures published\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\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Documentation and upkeep: Gemma 4 E2B IT +27\n\n- Gemma 4 E2B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.\n- WeVisOne-VL-2B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Reach: Gemma 4 E2B IT +25\n\n- Gemma 4 E2B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.\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: Gemma 4 E2B IT +15\n\n- Gemma 4 E2B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.\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### Agent interface: Gemma 4 E2B IT +15\n\n- Gemma 4 E2B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.\n- WeVisOne-VL-2B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | Gemma 4 E2B IT | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 70 | Gemma 4 E2B IT +25 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 E2B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E2B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 48 | Gemma 4 E2B IT +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.4 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E2B IT | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | 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 | yes | no |\n| Last release | 2026-11-03 | 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 | 3.5/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 E2B IT.** A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.\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### Gemma 4 E2B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\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, Gemma 4 E2B IT or WeVisOne-VL-2B?\n\nWeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e2b-it\", \"b\": \"wevisone-vl-2b\"}`. 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