# DeepSeek-V3.2 vs Inkling 2 Small (slim)

> Inkling 2 Small and DeepSeek-V3.2 score within a point of each other for open-weight models you run yourself, 58.8 and 58.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-2-small.md (~9,800 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-2-small.json · canonical https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-2-small
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-12
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Inkling 2 Small and DeepSeek-V3.2 score within a point of each other on agent readiness, 58.8 (C) and 58.6 (C). DeepSeek-V3.2 leads on reach. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #103 of 161 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.min.md
- Inkling 2 Small: C 58.8, rank #102 of 161 open-weight models · https://www.anchorterminal.com/tools/inkling-2-small.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.
- Inkling 2 Small, good for Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image. Ahead on Risk, 65 against 50; Agent interface, 85 against 60; Documentation and upkeep, 67 against 34.

| Category | DeepSeek-V3.2 | Inkling 2 Small |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 40 |
| Risk (20%) | 50 | 65 |
| Agent interface (10%) | 60 | 85 |
| Documentation and upkeep (10%) | 34 | 67 |

| Fact (where they differ) | DeepSeek-V3.2 | Inkling 2 Small |
| --- | --- | --- |
| Vendor | DeepSeek | Thinking Machines Lab |
| Licence | MIT | Apache-2.0 |
| Last release | 2025-12-01 | 2026-11-12 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Inkling 2 Small +33: 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-…); Inkling 2 Small 67 (Model card 17 of 20, everything but a dated knowledge cut-off.)
- Agent interface, Inkling 2 Small +25: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); Inkling 2 Small 85 (Chat template 20, a Jinja file in the repository.)
- 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.); Inkling 2 Small 40 (Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line.)
- Risk, Inkling 2 Small +15: 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.); Inkling 2 Small 65 (File safety 25, safetensors only and no repository code to run.)