# Cola2 vs DeepSeek V4.1 Flash (slim)

> DeepSeek V4.1 Flash scores 61.5 (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 V4.1 Flash scores 61.5 (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 V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-flash.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 48.
- DeepSeek V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on Reach, 60 against 25; Risk, 50 against 40; Agent interface, 75 against 20.

| Category | Cola2 | DeepSeek V4.1 Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 60 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 20 | 75 |
| Documentation and upkeep (10%) | 58 | 48 |

| Fact (where they differ) | Cola2 | DeepSeek V4.1 Flash |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | DeepSeek |
| Licence | None stated for the weights (the code is Apache 2.0) | MIT |
| Last release | 2026-11-06 | 2026-10-17 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Agent interface, DeepSeek V4.1 Flash +55: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…)
- Reach, DeepSeek V4.1 Flash +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 V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…)
- Risk, DeepSeek V4.1 Flash +10: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); DeepSeek V4.1 Flash 50 (The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja te…)
- Documentation and upkeep, Cola2 +10: 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 V4.1 Flash 48 (The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description wit…)