# Darwin-36B-Opus vs GLM-5.3-Flash (slim)

> GLM-5.3-Flash scores 65.1 (B) to Darwin-36B-Opus'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 Darwin-36B-Opus's 61 (C), and leads in 1 of 4 scored categories. Darwin-36B-Opus leads on reach. Both do open-weight models you run yourself.

- Darwin-36B-Opus: C 61, rank #37 of 100 open-weight models · https://www.anchorterminal.com/tools/darwin-36b-opus.min.md
- 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
- Darwin-36B-Opus, good for Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required Ahead on Reach, 80 against 60.
- 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 51.

| Category | Darwin-36B-Opus | GLM-5.3-Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 60 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 51 | 58 |

| Fact (where they differ) | Darwin-36B-Opus | GLM-5.3-Flash |
| --- | --- | --- |
| Vendor | FINAL-Bench (VIDRAFT) | Z.ai |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-04-23 | 2026-08-26 |
| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |