# GLM-5.3-Flash vs MiniCPM-V 4.7 1B (slim)

> GLM-5.3-Flash scores 65.1 (B) to MiniCPM-V 4.7 1B'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-5.3-Flash scores 65.1 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach. Both do open-weight models you run yourself.

- GLM-5.3-Flash: B 65.1, rank #18 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-5-3-flash.min.md
- MiniCPM-V 4.7 1B: C 61, rank #76 of 160 open-weight models · https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md
- GLM-5.3-Flash, good for Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on Documentation and upkeep, 58 against 48.
- MiniCPM-V 4.7 1B, good for Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model Ahead on Reach, 95 against 60.

| Category | GLM-5.3-Flash | MiniCPM-V 4.7 1B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 95 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 58 | 48 |

| Fact (where they differ) | GLM-5.3-Flash | MiniCPM-V 4.7 1B |
| --- | --- | --- |
| Vendor | Z.ai | OpenBMB |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-08-26 | 2026-10-30 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, MiniCPM-V 4.7 1B +35: GLM-5.3-Flash 60 (Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build…); MiniCPM-V 4.7 1B 95 (Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare.)
- Documentation and upkeep, GLM-5.3-Flash +10: GLM-5.3-Flash 58 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sourc…); MiniCPM-V 4.7 1B 48 (Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and p…)