# GLM-4.7-Flash vs WeVisOne-VL-4B (slim)

> GLM-4.7-Flash scores 63 (B) to WeVisOne-VL-4B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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GLM-4.7-Flash scores 63 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 2 of 4 scored categories. WeVisOne-VL-4B leads on documentation and upkeep. Both do open-weight models you run yourself.

- GLM-4.7-Flash: B 63, rank #26 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-4-7-flash.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- GLM-4.7-Flash, good for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime 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 Documentation and upkeep, 48 against 28.

| Category | GLM-4.7-Flash | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 28 | 48 |

| Fact (where they differ) | GLM-4.7-Flash | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Z.ai (Zhipu AI) | Tencent (WeChat Vision) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-01-29 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-4B +20: GLM-4.7-Flash 28 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, GLM-4.7-Flash +15: GLM-4.7-Flash 85 (A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, sa…); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, GLM-4.7-Flash +10: GLM-4.7-Flash 80 (Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB.); 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.)