# GLM-4.7-Flash vs Ling-3.1-flash-1019 (slim)

> GLM-4.7-Flash scores 63 (B) to Ling-3.1-flash-1019's 54.8 (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 Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 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
- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.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 60; Risk, 55 against 45; Agent interface, 85 against 60.
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding Ahead on Documentation and upkeep, 56 against 28.

| Category | GLM-4.7-Flash | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 60 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 28 | 56 |

| Fact (where they differ) | GLM-4.7-Flash | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | Z.ai (Zhipu AI) | inclusionAI (Ant Group) |
| Last release | 2026-01-29 | 2026-11-04 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, Ling-3.1-flash-1019 +28: 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.); Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…)
- Agent interface, GLM-4.7-Flash +25: 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…); Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.)
- Reach, GLM-4.7-Flash +20: 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.); Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.)
- Risk, GLM-4.7-Flash +10: GLM-4.7-Flash 55 (File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code.); Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…)