Xiaomi MiMo v2.6 Pro: New best open-weight LLM with simple design
Xiaomi’s new MiMo-V2.6 Pro is “simply” the best (for now). Despite its simple architecture design it’s currently No.1 in the open-weight benchmarks (weighted average). With “simple,” I mean a classic Grouped Query Attention (GQA) with Sliding Window Attention (SWA) at a tiny 128-token window size.
- ▪Xiaomi’s new MiMo-V2.6 Pro is “simply” the best (for now).
- ▪Despite its simple architecture design it’s currently No.1 in the open-weight benchmarks (weighted average).
- ▪With “simple,” I mean a classic Grouped Query Attention (GQA) with Sliding Window Attention (SWA) at a tiny 128-token window size.
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| Original publisher | Sebastian Raschka, PhD |
| Canonical URL | https://sebastianraschka.com/blog/2026/mimo-v2-6-pro-architecture-training-notes.html |
| Publication time | Tue, 22 Sep 2026 14:21:34 +0000 |
| Retrieval time | 2026-09-22T14:23:51.577Z |
| Last seen | 2026-09-22T14:23:51.577Z |
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Xiaomi’s new MiMo-V2.6 Pro is “simply” the best (for now). Despite its simple architecture design it’s currently No.1 in the open-weight benchmarks (weighted average). With “simple,” I mean a classic Grouped Query Attention (GQA) with Sliding Window Attention (SWA) at a tiny 128-token window size. So, that underlines one of the points I’ve been trying to make in recent months: most of the progress still comes from the data and post-training recipe improvements. Fancy attention variants are just mostly efficiency tweaks. What are some of the training data improvements and recipe improvements? The MiMo team shared a pretty detailed technical report.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Sebastian Raschka, PhD.
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