# Cola2 vs Mistral Large 4 (slim)

> Mistral Large 4 scores 54.6 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.md (~9,000 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.json · canonical https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4
- 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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Mistral Large 4 scores 54.6 (C) on agent readiness against Cola2's 33.9 (F), and leads in every scored category. 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
- Mistral Large 4: C 54.6, rank #128 of 160 open-weight models · https://www.anchorterminal.com/tools/mistral-large-4.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study
- Mistral Large 4, good for Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API. Ahead on Reach, 50 against 25; Risk, 75 against 40; Agent interface, 95 against 20; Documentation and upkeep, 64 against 58.

| Category | Cola2 | Mistral Large 4 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 50 |
| Risk (20%) | 40 | 75 |
| Agent interface (10%) | 20 | 95 |
| Documentation and upkeep (10%) | 58 | 64 |

| Fact (where they differ) | Cola2 | Mistral Large 4 |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | Mistral AI |
| Licence | None stated for the weights (the code is Apache 2.0) | Mistral Ecosystem Open-Weight License (modified MIT) |
| Last release | 2026-11-06 | 2026-10-29 |

Why the edge:
- Agent interface, Mistral Large 4 +75: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); Mistral Large 4 95 (The chat template is in the repository (20).)
- Risk, Mistral Large 4 +35: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Mistral Large 4 75 (File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template.)
- Reach, Mistral Large 4 +25: 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.); Mistral Large 4 50 (Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB.)
- Documentation and upkeep, Mistral Large 4 +6: 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…); Mistral Large 4 64 (The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14).)