# MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B (slim)

> MiMo-V2.6 Distill Qwen 9B and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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MiMo-V2.6 Distill Qwen 9B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep. Both do open-weight models you run yourself.

- MiMo-V2.6 Distill Qwen 9B: C 61, rank #47 of 160 open-weight models · https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- MiMo-V2.6 Distill Qwen 9B, good for Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB. Ahead on Reach, 80 against 70; Risk, 65 against 55.
- WeVisOne-VL-4B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Documentation and upkeep, 48 against 40.

| Category | MiMo-V2.6 Distill Qwen 9B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 40 | 48 |

| Fact (where they differ) | MiMo-V2.6 Distill Qwen 9B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Xiaomi (MiMo team) | Tencent (WeChat Vision) |
| Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | Apache-2.0 |
| Last release | 2026-09-21 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Reach, MiMo-V2.6 Distill Qwen 9B +10: MiMo-V2.6 Distill Qwen 9B 80 (Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB.); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Risk, MiMo-V2.6 Distill Qwen 9B +10: MiMo-V2.6 Distill Qwen 9B 65 (File safety 25, safetensors only and no repository code runs.); WeVisOne-VL-4B 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, WeVisOne-VL-4B +8: MiMo-V2.6 Distill Qwen 9B 40 (Model card 5 of 20, intended use and parameters only.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)