# LFM2.5-2.6B vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to LFM2.5-2.6B's 50.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.md (~9,350 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b
- 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)

WeVisOne-VL-4B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LFM2.5-2.6B: D 50.3, rank #146 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- LFM2.5-2.6B, good for Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold Ahead on Reach, 90 against 70; Agent interface, 75 against 70; Documentation and upkeep, 57 against 48.
- WeVisOne-VL-4B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Risk, 55 against 50.

| Category | LFM2.5-2.6B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 57 | 48 |

| Fact (where they differ) | LFM2.5-2.6B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Liquid AI | Tencent (WeChat Vision) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Reach, LFM2.5-2.6B +20: LFM2.5-2.6B 90 (Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare.); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Documentation and upkeep, LFM2.5-2.6B +9: LFM2.5-2.6B 57 (The model card earns 17, with everything but a knowledge cut-off.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, WeVisOne-VL-4B +5: LFM2.5-2.6B 50 (File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts.); WeVisOne-VL-4B 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, LFM2.5-2.6B +5: LFM2.5-2.6B 75 (The chat template earns 20 for chat_template.jinja in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)