On September 22, the Xiaomi MiMo team released and open-sourced the MiMo-V2.6 series. The flagship MiMo-V2.6-Pro-RL — a sparse mixture-of-experts model with 1.02 trillion total parameters and 42 billion activated per token — scores 46 on Artificial Analysis's Intelligence Index, tying xAI's Grok 4.7 released the day before, and marking the highest open-weights result on that leaderboard.

Architecture: Trillion-Parameter Sparse MoE, 1M Context, Native Omnimodal

Per the official model card, MiMo-V2.6-Pro-RL has 1.02T total parameters with 42B active per token: 384 routed experts, 8 activated per token, a 70-layer backbone (60 sliding-window attention layers plus 10 global-attention layers), and a hidden size of 6144. Context length is 1M tokens, with native support for text, image, video, and audio input: a 681M-parameter MiMo ViT on the vision side, a 308M AudioTokenizer plus a 127M audio patch encoder on the audio side, and a 5-layer MTP speculative decoder for faster inference. Weights are available on Hugging Face under the MIT license, which permits commercial use (official model card: Hugging Face).

Benchmarks: Neck-and-Neck with Closed Frontiers on Agent Eval, a Generational Jump on Security

In the model card's comparison table, MiMo-V2.6-Pro-RL trades blows with closed-source flagships on agent benchmarks: 71.9 on DeepSWE v1.1, above Claude Fable 5's 70.0 and just below Claude Opus 5's 74.0 and GPT-5.6 Sol's 73.0; 53.1 on AutomationBench, ahead of Opus 5 (50.3), Sol (45.8), and Fable 5 (46.2); 31.6 on Agents' Last Exam, level with Opus 5; 89.9 on Terminal Bench 2.1; and 62.0 on JobBench, clearly above Sol's 45.4. The security track shows the sharpest jump: CyberGym leaped from V2.5 Pro's 40.0 to 94.0, and Xiaomi's own MiMo Cyber Bench went from 0.0 to 80.2.

For context, V2.5 Pro scored just 19.0 on DeepSWE v1.1 and 16.0 on AutomationBench — V2.6 is a generational leap of an order of magnitude on both the agent and security lines.

On third-party scoring, Artificial Analysis gives MiMo-V2.6-Pro a 46 on its Intelligence Index, the top open-weights result on its leaderboard, tied with Grok 4.7 (see Artificial Analysis). Xiaomi's own claim is 46.32 and "the strongest open-source model to date," surpassing Kimi K3 and Qwen3.8 Max — that's vendor framing, weight it accordingly.

Training: You Only RL Once, plus Groupwise Agentic Grading

The through-line of V2.6 is scaling reinforcement learning toward self-improvement. Xiaomi says training used "You Only RL Once": one mixed RL run spanning coding, general agents, visual, and cybersecurity tasks, so capabilities reinforce each other and transfer across harnesses. The substrate is fully asynchronous GRPO — 1,568 prompts with 16 rollouts each per step.

The more interesting part is the grading. Binary pass/fail cannot rank passing solutions, so MiMo introduces Groupwise Agentic Grading: offline, Groupwise Reward Synthesis (GRS) builds task-specific rubrics from contrasting rollouts within each group; online, Groupwise Advantage Redistribution (GAR) ranks passing trajectories and shifts advantage toward higher-quality solutions — effectively scaling the reward signal itself. The loop is kept honest with a self-correction cold start, environment hardening, adversarial screening, and verifier cross-checks against reward hacking, followed by multi-prefix multi-teacher on-policy distillation (MOPD2) after RL.

Flash: The 310B Sibling Is Where the Industry Impact Lands

The sibling MiMo-V2.6-Flash-RL has 310B total and 15B active parameters, yet holds most of the ground: 67.9 vs Pro's 71.9 on DeepSWE, 52.3 vs 53.1 on AutomationBench, 87.6 vs 89.9 on Terminal Bench 2.1 — generally retaining more than 90% of Pro's scores. In VentureBeat's coverage, Tim Dettmers, creator of bitsandbytes and a CMU professor, called Flash "the best model in the 300B to 550B class. Better than DeepSeek v4.1 and GLM 5.3 Flash." On API pricing, Pro runs $0.435 per million input tokens and $0.87 per million output tokens, while Flash costs $0.14 and $0.28 — roughly a third of the price.

When a trillion-parameter flagship's capability can be retained at 90%-plus by a 15B-active model at a third of the price, open-weight competition against closed APIs shifts from "flagship showdowns" down to "everyday calls." The top spot is the headline; Flash is the bill.