Xiaomi MiMo-V2.6 Pro vs Visual Jev (Qwen3-VL 8B Backbone)
A direct, empirical evaluation of Xiaomi MiMo-V2.6 Pro (Xiaomi AI Lab (Fuli Luo)) against Visual Jev (Qwen3-VL 8B Backbone) (Independent AI Systems Lab (Guanxu Yu & Yuhang Yao)).
Xiaomi MiMo-V2.6 Pro
Developed by Xiaomi AI Lab (Fuli Luo) · 1.02 Trillion (Sparse MoE) parameters
High-accuracy autonomous coding (71.9% DeepSWE), cybersecurity defense (94.0% CyberGym), multimodal document reasoning, and cost-effective agentic workflows.
Visual Jev (Qwen3-VL 8B Backbone)
Developed by Independent AI Systems Lab (Guanxu Yu & Yuhang Yao) · 8.2 Billion Vision-Language Parameters parameters
Sub-20ms multi-query image verification, automated rejection sampling in generative diffusion pipelines (FLUX / SD3), robotics perception routing, and high-frequency visual software inspection.
| Metric | Xiaomi MiMo-V2.6 Pro | Visual Jev (Qwen3-VL 8B Backbone) |
|---|---|---|
| SWE-bench Verified | 71.9% | 62.1% |
| MMLU-Pro | 89.2% | 86.4% |
| MATH-500 | 93.8% | 89.2% |
| Context Window | 128,000 tokens | 32,768 tokens (Visual prefix + batched question suffixes) |
| Input Pricing (per 1M) | $0.10 | $0.05 |
| Output Pricing (per 1M) | $0.20 | $0.10 |