# LFM3-Audio-2B vs WeVisOne-VL-4B (slim)

> LFM3-Audio-2B and WeVisOne-VL-4B 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/lfm3-audio-2b-vs-wevisone-vl-4b.md (~9,200 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/lfm3-audio-2b-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)

LFM3-Audio-2B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk. Both do open-weight models you run yourself.

- LFM3-Audio-2B: C 61, rank #70 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-audio-2b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- LFM3-Audio-2B, good for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model Ahead on Reach, 80 against 70; Agent interface, 85 against 70.
- 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 40.

| Category | LFM3-Audio-2B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 52 | 48 |

| Fact (where they differ) | LFM3-Audio-2B | 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 |

Why the edge:
- Risk, WeVisOne-VL-4B +15: LFM3-Audio-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); 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, LFM3-Audio-2B +15: LFM3-Audio-2B 85 (Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, LFM3-Audio-2B +10: LFM3-Audio-2B 80 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line.); 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.)