# DeepSeek-V3.2 vs Ling-3.1-flash-1019 (slim)

> DeepSeek-V3.2 scores 58.6 (C) 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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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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DeepSeek-V3.2 scores 58.6 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 1 of 4 scored categories. Ling-3.1-flash-1019 leads on 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
- 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
- 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 Risk, 50 against 45.
- 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 34.

| Category | DeepSeek-V3.2 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 60 |
| Risk (20%) | 50 | 45 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 34 | 56 |

| Fact (where they differ) | DeepSeek-V3.2 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | DeepSeek | inclusionAI (Ant Group) |
| Last release | 2025-12-01 | 2026-11-04 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Ling-3.1-flash-1019 +22: 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-…); 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…)
- Risk, DeepSeek-V3.2 +5: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); 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…)