# Darwin-36B-Opus vs GLM-4.5-Air (slim)

> Darwin-36B-Opus scores 61 (C) to GLM-4.5-Air's 59.1 (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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Darwin-36B-Opus scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface. 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-4.5-Air: C 59.1, rank #62 of 100 open-weight models · https://www.anchorterminal.com/tools/glm-4-5-air.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 65; Documentation and upkeep, 51 against 38.
- GLM-4.5-Air, good for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support. Ahead on Risk, 65 against 55; Agent interface, 85 against 70.

| Category | Darwin-36B-Opus | GLM-4.5-Air |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 65 |
| Risk (20%) | 55 | 65 |
| Agent interface (10%) | 70 | 85 |
| Documentation and upkeep (10%) | 51 | 38 |

| Fact (where they differ) | Darwin-36B-Opus | GLM-4.5-Air |
| --- | --- | --- |
| Vendor | FINAL-Bench (VIDRAFT) | Z.ai (Zhipu AI) |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-04-23 | 2025-08-11 |
| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |