# SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B > WeVisOne-VL-2B scores 61 (C) to SenseNova-Loop-9B-Preview's 46.8 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b - Markdown: https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md (~9,700 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 WeVisOne-VL-2B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself. - SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json - 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 - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### SenseNova-Loop-9B-Preview (D) Good for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today Watch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests ### WeVisOne-VL-2B (C) Good for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on: - Reach, 70 against 40 - Risk, 55 against 50 - Agent interface, 70 against 60 Watch for: The pixel decoder ships as a pickle file ## Why the edge ### Reach: WeVisOne-VL-2B +30 - SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub 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, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40. - 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. ### Agent interface: WeVisOne-VL-2B +10 - SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60. - 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. ### Risk: WeVisOne-VL-2B +5 - SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the 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. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50. - 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. ## Score by category | Category | Weight | SenseNova-Loop-9B-Preview | WeVisOne-VL-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 40 | 70 | WeVisOne-VL-2B +30 | | Risk | 20% (20 this run) | 50 | 55 | WeVisOne-VL-2B +5 | | Agent interface | 10% (10 this run) | 60 | 70 | WeVisOne-VL-2B +10 | | Documentation and upkeep | 10% (10 this run) | 44 | 48 | WeVisOne-VL-2B +4 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **46.8 · D** | **61 · C** | | ## Facts side by side | Fact | SenseNova-Loop-9B-Preview | WeVisOne-VL-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | SenseTime (SenseNova) | Tencent (WeChat Vision) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-11-02 | 2026-10-28 | | Privacy policy last updated | no document linked | no document linked | ## Verdicts **SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet. **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. ## Before you call either ### SenseNova-Loop-9B-Preview 1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it 2. Pin transformers 5.18.0, the version the card checked 3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size 4. Pass enable_thinking=False to apply_chat_template for a direct answer 5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser 6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API ### WeVisOne-VL-2B 1. Use Tencent's package: `WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm")` then `model.predict(image, task=..., query=...)` 2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting 3. A None answer means the target isn't in the image. Treat it as a result, not an error 4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's 5. Send one image a turn. Video grids are rejected by the decoder 6. The card says it isn't for identifying individuals or for high-impact decisions on its own ## Questions ### Which is better for AI agents, SenseNova-Loop-9B-Preview or WeVisOne-VL-2B? WeVisOne-VL-2B scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "sensenova-loop-9b-preview", "b": "wevisone-vl-2b"}`. 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[LFM3-VL-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.md) - [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md) - [Ling-3.0-tiny vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-sensenova-loop-9b-preview.md) - [Ling-3.0-tiny vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md) - [Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview.md) - [Ling-3.1-flash-1019 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.md) - [LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md) - [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md) - [MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.md) - [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) - [MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.md) - [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md) - [MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview.md) - [MiniCPM-o 4.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md) - [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md) - [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md) - [MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.md) - [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) - [MiniCPM5-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-sensenova-loop-9b-preview.md) - [MiniCPM5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.md) - [MiniMax-M2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.md) - [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md) - [MiniMax-M2.7 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.md) - [MiniMax-M2.7 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.md) - [MiniMax-M3.1-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-sensenova-loop-9b-preview.md) - [MiniMax-M3.1-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.md) - 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[NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-sensenova-loop-9b-preview.md) - [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b.md) - [Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-sensenova-loop-9b-preview.md) - [Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b.md) - [Nemotron 3 Nano 30B A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-sensenova-loop-9b-preview.md) - [Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b.md) - [Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview.md) - [Nemotron 3 Nano 4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.md) - [Nemotron 3 Nano Omni 30B A3B Reasoning vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-sensenova-loop-9b-preview.md) - [Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b.md) - [Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview.md) - [Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.md) - [Nemotron 3 Ultra 550B A55B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-sensenova-loop-9b-preview.md) - [Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b.md) - 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[Ornith-1.5-35B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-sensenova-loop-9b-preview.md) - [Ornith-1.5-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b.md) - [Ornith-1.5-397B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview.md) - [Ornith-1.5-397B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.md) - [Ornith-1.5-9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview.md) - [Ornith-1.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.md) - [OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.md) - [OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.md) - 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[Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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