# SmolLM3-3B vs WeVisOne-VL-2B (slim)

> SmolLM3-3B and WeVisOne-VL-2B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.md (~9,300 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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SmolLM3-3B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.

- SmolLM3-3B: C 61, rank #55 of 160 open-weight models · https://www.anchorterminal.com/tools/smollm3-3b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- SmolLM3-3B, good for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device Ahead on Reach, 85 against 70; Risk, 65 against 55; Agent interface, 80 against 70; Documentation and upkeep, 74 against 48.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence

| Category | SmolLM3-3B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 80 | 70 |
| Documentation and upkeep (10%) | 74 | 48 |

| Fact (where they differ) | SmolLM3-3B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Hugging Face | Tencent (WeChat Vision) |
| Last release | 2025-09-10 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, SmolLM3-3B +26: SmolLM3-3B 74 (Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the ch…); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, SmolLM3-3B +15: SmolLM3-3B 85 (Memory 30, the smallest 4-bit build Hugging Face publishes is its own ONNX q4f16 at 2.1 GB, about 2.5 GB with the fifth added, and the full BF16 files that the…); 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.)
- Risk, SmolLM3-3B +10: SmolLM3-3B 65 (File safety 25, safetensors only and nothing from the repository runs on load or to build prompts.); 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.)
- Agent interface, SmolLM3-3B +10: SmolLM3-3B 80 (Chat template 20, a Jinja file in the repository.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)