# GLM-5.3-Flash vs Ling-3.0-tiny (slim)

> GLM-5.3-Flash scores 65.1 (B) to Ling-3.0-tiny's 55 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-21
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GLM-5.3-Flash scores 65.1 (B) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 4 of 5 scored categories. Ling-3.0-tiny leads on reach. Both do open-weight models you run yourself.

- GLM-5.3-Flash: B 65.1, rank #16 of 100 open-weight models · https://www.anchorterminal.com/tools/glm-5-3-flash.min.md
- Ling-3.0-tiny: C 55, rank #81 of 100 open-weight models · https://www.anchorterminal.com/tools/ling-3-0-tiny.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 Capability, 75 against 31; Risk, 55 against 50; Agent interface, 70 against 60.
- Ling-3.0-tiny, good for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on Reach, 90 against 60.

| Category | GLM-5.3-Flash | Ling-3.0-tiny |
| --- | --- | --- |
| Capability (35%) | 75 | 31 |
| Reach (25%) | 60 | 90 |
| Risk (20%) | 55 | 50 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 58 | 56 |

| Fact (where they differ) | GLM-5.3-Flash | Ling-3.0-tiny |
| --- | --- | --- |
| Vendor | Z.ai | inclusionAI (Ant Group) |
| Last release | 2026-08-26 | 2026-08-31 |