# Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.md (~10,450 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-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 Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Qwen3-Omni-30B-A3B-Instruct: E 41.8, rank #158 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Qwen3-Omni-30B-A3B-Instruct, good for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice
- 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 Reach, 70 against 55; Agent interface, 70 against 60; Documentation and upkeep, 48 against 26.

| Category | Qwen3-Omni-30B-A3B-Instruct | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 26 | 48 |

| Fact (where they differ) | Qwen3-Omni-30B-A3B-Instruct | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2025-09-22 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-4B +22: Qwen3-Omni-30B-A3B-Instruct 26 (Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-4B +15: Qwen3-Omni-30B-A3B-Instruct 55 (Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band.); 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.)
- Agent interface, WeVisOne-VL-4B +10: Qwen3-Omni-30B-A3B-Instruct 60 (Chat template 20, a Jinja file in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)