# Kolibri-1 vs WeVisOne-VL-2B (slim)

> Kolibri-1 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/kolibri-1-vs-wevisone-vl-2b.md (~9,400 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/kolibri-1-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)

Kolibri-1 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on reach. Both do open-weight models you run yourself.

- Kolibri-1: C 61, rank #44 of 160 open-weight models · https://www.anchorterminal.com/tools/kolibri-1.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Kolibri-1, good for German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters. Ahead on Risk, 65 against 55; Agent interface, 90 against 70; Documentation and upkeep, 70 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 Ahead on Reach, 70 against 65.

| Category | Kolibri-1 | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 90 | 70 |
| Documentation and upkeep (10%) | 70 | 48 |

| Fact (where they differ) | Kolibri-1 | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Aleph Alpha | Tencent (WeChat Vision) |
| Last release | 2026-10-19 | 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, Kolibri-1 +22: Kolibri-1 70 (The model card earns 20 with all seven items.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, Kolibri-1 +20: Kolibri-1 90 (The chat template earns 20 as Jinja in tokenizer_config.json.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, Kolibri-1 +10: Kolibri-1 65 (File safety earns 25 for safetensors only, with no repository code run on load.); 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.)
- Reach, WeVisOne-VL-2B +5: Kolibri-1 65 (Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only 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.)