# Kimi K3.1 Pro vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.md (~8,850 tokens) · this version ~630 tokens · JSON https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/kimi-k3-1-pro-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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WeVisOne-VL-2B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Kimi K3.1 Pro: D 48.6, rank #151 of 160 open-weight models · https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Kimi K3.1 Pro, good for Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API
- 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 Ahead on Reach, 70 against 40; Risk, 55 against 50; Agent interface, 70 against 65.

| Category | Kimi K3.1 Pro | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 40 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 51 | 48 |

| Fact (where they differ) | Kimi K3.1 Pro | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Moonshot AI | Tencent (WeChat Vision) |
| Licence | Kimi K3.1 Pro License (modified MIT) | Apache-2.0 |
| Last release | 2026-11-05 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Reach, WeVisOne-VL-2B +30: Kimi K3.1 Pro 40 (Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB.); 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, WeVisOne-VL-2B +5: Kimi K3.1 Pro 50 (File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script.); 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, WeVisOne-VL-2B +5: Kimi K3.1 Pro 65 (Chat template 10, the format is only code to run.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)