# DeepSeek-V3.2 vs Kimi K3.1 Pro (slim)

> DeepSeek-V3.2 scores 58.6 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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DeepSeek-V3.2 scores 58.6 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 1 of 4 scored categories. Kimi K3.1 Pro leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.min.md
- Kimi K3.1 Pro: D 48.6, rank #151 of 160 open-weight models · https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider Ahead on Reach, 60 against 40.
- Kimi K3.1 Pro, good for Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API Ahead on Agent interface, 65 against 60; Documentation and upkeep, 51 against 34.

| Category | DeepSeek-V3.2 | Kimi K3.1 Pro |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 40 |
| Risk (20%) | 50 | 50 |
| Agent interface (10%) | 60 | 65 |
| Documentation and upkeep (10%) | 34 | 51 |

| Fact (where they differ) | DeepSeek-V3.2 | Kimi K3.1 Pro |
| --- | --- | --- |
| Vendor | DeepSeek | Moonshot AI |
| Licence | MIT | Kimi K3.1 Pro License (modified MIT) |
| Last release | 2025-12-01 | 2026-11-05 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Reach, DeepSeek-V3.2 +20: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added.); Kimi K3.1 Pro 40 (Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB.)
- Documentation and upkeep, Kimi K3.1 Pro +17: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); Kimi K3.1 Pro 51 (Model card 11, no limitations, languages or cut-off.)
- Agent interface, Kimi K3.1 Pro +5: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); Kimi K3.1 Pro 65 (Chat template 10, the format is only code to run.)