# Mistral Large 4 vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Mistral Large 4's 54.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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WeVisOne-VL-2B scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Mistral Large 4: C 54.6, rank #128 of 160 open-weight models · https://www.anchorterminal.com/tools/mistral-large-4.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- 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 Risk, 75 against 55; Agent interface, 95 against 70; Documentation and upkeep, 64 against 48.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Reach, 70 against 50.

| Category | Mistral Large 4 | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 70 |
| Risk (20%) | 75 | 55 |
| Agent interface (10%) | 95 | 70 |
| Documentation and upkeep (10%) | 64 | 48 |

| Fact (where they differ) | Mistral Large 4 | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Mistral AI | Tencent (WeChat Vision) |
| Licence | Mistral Ecosystem Open-Weight License (modified MIT) | Apache-2.0 |
| Last release | 2026-10-29 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Agent interface, Mistral Large 4 +25: Mistral Large 4 95 (The chat template is in the repository (20).); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, WeVisOne-VL-2B +20: 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.); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)
- Risk, Mistral Large 4 +20: Mistral Large 4 75 (File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template.); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Documentation and upkeep, Mistral Large 4 +16: Mistral Large 4 64 (The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14).); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)