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

> Kimi K3 scores 62.4 (B) to WeVisOne-VL-2B's 61 (C) 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-vs-wevisone-vl-2b.md (~9,250 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/kimi-k3-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Kimi K3 scores 62.4 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-2B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Kimi K3: B 62.4, rank #30 of 160 open-weight models · https://www.anchorterminal.com/tools/kimi-k3.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, good for Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work Ahead on Risk, 65 against 55.
- 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 50; Agent interface, 70 against 65; Documentation and upkeep, 48 against 41.

| Category | Kimi K3 | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 41 | 48 |

| Fact (where they differ) | Kimi K3 | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Moonshot AI | Tencent (WeChat Vision) |
| Licence | Kimi K3 License (modified MIT) | Apache-2.0 |
| Last release | 2026-09-02 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Reach, WeVisOne-VL-2B +20: Kimi K3 50 (Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, abou…); 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, Kimi K3 +10: Kimi K3 65 (File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and build…); 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.)
- Documentation and upkeep, WeVisOne-VL-2B +7: Kimi K3 41 (The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, WeVisOne-VL-2B +5: Kimi K3 65 (The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)