ANALYSIS A consumer-electronics company just matched the benchmark ceiling that the most expensive proprietary labs occupy, and it did so for a reported training cost that rounds to a rounding error on their R&D budgets. Xiaomi's MiMo-V2.6-Pro scored 46 on the Artificial Analysis Intelligence Index, tying xAI's Grok 4.7 and topping every other open-weight model on the leaderboard3,5,7. The RL phase that produced that result cost $2.62 million and ran for less than six days6. For closed-model labs whose commercial moats rest on capability gaps, the math just got uncomfortable.
Why it matters
The release redraws two boundaries at once. First, the open-weight frontier now sits at the same Intelligence Index score as a leading proprietary system. Second, the cost to reach that score has dropped to a figure accessible to well-funded university labs, not just hyperscalers. Xiaomi reported the full RL run consumed roughly 750,000 trajectories across 30 steps, burning 3.5 to 3.7 billion tokens per step, for a total spend of $2.62 million on Pro and $850,000 on Flash2. ◆ Those figures, if reproducible by third parties, compress the timeline in which any motivated team can field a frontier-class model under an MIT license.
The big picture
MiMo-V2.6-Pro is a sparse mixture-of-experts model: 1.02 trillion total parameters, roughly 42 billion active. It ships with a 1-million-token context window and native multimodal capabilities. Xiaomi claims performance "on par with Opus 5 and GPT-5.6 Sol across most agent benchmarks"4. On DeepSWE v1.1, the vendor-reported score is 71.9%, trailing Claude Opus 5 at 74.0%. On the Artificial Analysis Intelligence Index v4.3.2, MiMo-V2.6-Pro sits at 46, ahead of GLM-5.3 at 45 and Kimi K3 at 44, and level with Grok 4.7 (xHigh). Leading closed-source models scored 53 on the same index.
The model is available on Hugging Face under an MIT license, alongside RL code and more than 7,000 task environments. ANALYSIS That packaging matters as much as the weights themselves: it gives external teams the scaffolding to replicate or extend Xiaomi's RL recipe rather than merely run inference.
The team behind the release is led by Fuli Luo, formerly of DeepSeek1. Luo's group livestreamed the final RL training runs before the release, an unusual degree of transparency that Artificial Analysis flagged publicly. ◆ The move positions Xiaomi not just as a model builder but as an open-research institution, a brand play that a hardware company can afford in ways a pure-play API vendor cannot.
Between the lines
Xiaomi's technical report details a pipeline built on fully asynchronous Group Relative Policy Optimization at 1,568 prompts with 16 rollouts per step. The RL training mixed coding, general agent, visual, and cybersecurity tasks. A post-RL distillation stage called MOPD2 follows the mixed RL run. Reward-hacking defenses span reward design, adversarial evaluation, anomaly detection, and cross-checking between verifiers.
ANALYSIS The architecture choices reveal a deliberate bet on sparse activation. With only 42 billion parameters active out of 1.02 trillion total, inference costs stay low even as the model's representational capacity scales. Xiaomi's API pricing reflects that efficiency: MiMo-V2.6-Pro is listed at $0.435 per million cache-miss input tokens and $0.87 per million output tokens. Flash drops to $0.14 and $0.28 respectively.
MiMo-V2.6-Pro's Artificial Analysis score of 46 puts it ahead of xAI's Grok 4.6, currently at 44, and Google's Gemini 3.8 Flash on the same ranking. ◆ The gap between that 46 and the top closed-source score of 53 on the same index is real, and Xiaomi's own benchmark tables acknowledge it. But the competitive pressure runs in one direction: closed labs must now justify premium pricing against an MIT-licensed alternative that matches or exceeds several proprietary peers.
What's next
Xiaomi's UltraSpeed variant, which the company says delivers up to 20x faster output at the same quality, is in an early-access program running for one more week. Artificial Analysis measured 129.7 output tokens per second and a 2.17-second time to first token through Xiaomi's API. Xiaomi reported vendor-side DeepSWE v1.1 lifts from 48.8 to 65.7 for Flash and from 58.4 to 72.6 for Pro, though those gains remain unconfirmed by third parties. ◆ Independent reproduction will be the real test; Xiaomi has published the environments and code to make that possible. The weights are live, the license is MIT, and the cost figure is on the record. What closed labs do with that information over the next quarter will say more about the durability of proprietary moats than any benchmark table.