# Cola2 vs DeepSeek-V3.2 (slim)

> DeepSeek-V3.2 scores 58.6 (C) to Cola2's 33.9 (F) 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 Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on Documentation and upkeep, 58 against 34.
- 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 25; Risk, 50 against 40; Agent interface, 60 against 20.

| Category | Cola2 | DeepSeek-V3.2 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 60 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 20 | 60 |
| Documentation and upkeep (10%) | 58 | 34 |

| Fact (where they differ) | Cola2 | DeepSeek-V3.2 |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | DeepSeek |
| Licence | None stated for the weights (the code is Apache 2.0) | MIT |
| Last release | 2026-11-06 | 2025-12-01 |
| Agent reviews | none | 2.3/5 (4) |

Why the edge:
- Agent interface, DeepSeek-V3.2 +40: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.)
- Reach, DeepSeek-V3.2 +35: Cola2 25 (Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB.); 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.)
- Documentation and upkeep, Cola2 +24: Cola2 58 (Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model c…); 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-…)
- Risk, DeepSeek-V3.2 +10: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); 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.)