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MiMo-V2.6 is 'simply' the best (for now). Despite its simple architecture design it's currently No.1 in the open-weight benchmarks (weighted…
MiMo-V2.6 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 Att
MiMo-V2.6 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. Lots to carefully digest there, but in short, there are a few things that stood out: 1. An increase in agent tasks; also training across different harnesses (the average DeepSWE pass@1 accuracy on held-out harnesses improved from approximately 50% -> 66%). 2. Better reward signals: they replaced a simple correctness verifier with an agentic grader that looks at the execution traces as well. 3. Large RL batches (1,568 prompts × 16 rollouts = 25,088 trajectories) and 2.7–3.7 billion training tokens per update (unclear, though, what the predecessor used). MiMo-V2.6-Pro debuts as the top open weights model on the Artificial Analysis Intelligence Index (46). At $0.13 per Intelligence Index task, it lands on the Intelligence vs. Cost per Task Pareto frontier @Xiaomi has just released MiMo-V2.6-Pro, an open weights model with major ad…
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Source: Sebastian Raschka (X) | 2026-09-22