MiMo-V2.6 just dropped, and Xiaomi’s Fuli Luo is already teasing the next architecture: MiMo-V3🔥 The core of it, HySparse2, targets three bottlenecks in agentic inference: prefill cost, KV cache size, and long-context retrieval. Compared with MiMo-V2.6’s Hybrid SWA architecture at 1M tokens: • 5.02× lower prefill FLOPs • 4.5× smaller KV Cache • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL The HySparse2 paper comes from Xiaomi’s LLM-Core team, with Fuli Luo as corresponding author and Team Lead. Interestingly, its references also include DeepSeek-V2, V3.2, V4, and V4.1-Flash, along with OpenAI’s GPT-4.1 evaluation work and the gpt-oss model card.

MiMo 2.6 Pro vs Flash👇 Pro (left): 10M tokens, $1.41, 4,927s Flash (right): 6.9M tokens, $0.15, 4,090s Pro costs nearly 10× more, but the night scene, lighting, and overall atmosphere are noticeably richer.

MiMo-V2.6-Flash is available for free for a week on OpenCode! if your looking for a way to test this model out... you can now for free which is pretty amazing let me know what yall cook with it

MiMo-V2.6-Pro-RL - 1.02T Total and 42B Activated parameters - Only 1 point below GPT 5.6 Sol Max in capabilities & intelligence - Fits on 8x RTX PRO 6000 (that's the cost of GPT 5.6 Sol Max "frontier intelligence" now) They also released MiMo-V2.6-Flash-RL (309B/15B) btw

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 advances in intelligence over its predecessor, MiMo-V2.5-Pro (Intelligence Index: 26). Despite the improvement, it retains the same attractive pricing at $0.435 per 1M input tokens (with a 99% cache-hit discount) and $0.87 per 1M output tokens. This makes MiMo-V2.6-Pro one of the most cost-efficient models to deploy. MiMo-V2.6-Pro is an MoE model with 1.02T total parameters and 42B active parameters. Stay tuned for additional analysis of the model. Check out MiMo-V2.6-Pro full benchmarking breakdown here:

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MiMo-V2-Pro & Omni & TTS is out. Our first full-stack model family built truly for the Agent era. I call this a quiet ambush — not because we planned it, but because the shift from Chat to Agent paradigm happened so fast, even we barely believed it. Somewhere in between was a process that was thrilling, painful, and fascinating all at once. The 1T base model started training months ago. The original goal was long-context reasoning efficiency. Hybrid Attention carries real innovation, without overreaching — and it turns out to be exactly the right foundation for the Agent era. 1M context window. MTP inference for ultra-low latency and cost. These architectural decisions weren't trendy. They were a structural advantage we built before we needed it. What changed everything was experiencing a complex agentic scaffold — what I'd call orchestrated Context — for the first time. I was shocked on day one. I tried to convince the team to use it. That didn't work. So I gave a hard mandate: anyone on MiMo Team with fewer than 100 conversations tomorrow can quit. It worked. Once the team's imagination was ignited by what agentic systems could do, that imagination converted directly into research velocity. People ask why we move so fast. I saw it firsthand building DeepSeek R1. My honest summary: — Backbone and Infra research has long cycles. You need strategic conviction a year before it pays off. — Posttrain agility is a different muscle: product intuition driving evaluation, iteration cycles compressed, paradigm shifts caught early. — And the constant: curiosity, sharp technical instinct, decisive execution, full commitment — and something that's easy to underestimate: a genuine love for the world you're building for. We will open-source — when the models are stable enough to deserve it. From Beijing, very late, not quite awake.