Xiaomi MiMo-V2.6: A New Peak for Open-Source Multimodal AI

Xiaomi has launched the MiMo-V2.6 series, featuring the Pro and Flash models, which now lead the open-source market in intelligence and efficiency. Developed through scaled reinforcement learning and built in public, the models excel in complex tasks like 3D world-building, music composition, and mathematical formalization. The entire suite, including training code and environments, is open-sourced to foster further exploration in AI self-improvement.

Key Points

• MiMo-V2.6-Pro has surpassed Kimi K3 and Qwen3.8 Max to become the highest-performing open-source model on the Artificial Analysis Intelligence Index.
• The models were developed using scaled reinforcement learning across 30 steps and 750k trajectories, resulting in significant gains in coding and general workflow benchmarks.
• Xiaomi has fully open-sourced the models, technical reports, training environments, and RL code to encourage community reproduction and verification.
• New capabilities include 'Vibe World' for 3D modeling and game development, robotic embodied simulation, and automated high-quality video and music production.
• The series introduces MiMo-V2.6-Pro-UltraSpeed, which delivers 20x faster output for time-sensitive workflows while keeping costs consistent with previous versions.

Sentiment

The overall sentiment is positive but analytical. Hacker News largely agrees that MiMo-V2.6 is a noteworthy open-model release and especially praises Xiaomi's transparency around training, while reserving judgment on the strongest benchmark claims and emphasizing that real-world performance, openness definitions, and trust questions still need scrutiny.

In Agreement

• Xiaomi's live training dashboard and detailed technical report are unusually useful and should become a norm for open-model releases.
• Open weights, training code, environments, and methodology can accelerate community learning and make the release more valuable than a simple benchmark announcement.
• MiMo-V2.6 appears competitive on price and capability, with some early users finding it strong at detailed coding tasks and corner-case detection.
• Chinese AI labs are setting a strong example on openness, cost, and practical execution compared with more closed Western frontier labs.
• The dashboard exposes meaningful RL training details such as rollout staleness, model divergence, and dynamic sampling, making it a rare educational artifact.

Opposed

• Benchmark rankings are not enough to prove real-world model quality because models can optimize for tests while underperforming on practical tasks.
• The release may not satisfy stricter open-source expectations if important pieces such as training data remain unavailable or unclear.
• Some early hands-on reports describe the model as overplanning, generating excessive reasoning, making poor architecture choices, or getting stuck in loops.
• Trust in Xiaomi is mixed because commenters cite ads, bloatware, repair restrictions, account problems, and broader concerns about large technology companies.
• Ideas for decentralized or community-funded training face unresolved problems around malicious contributions, data provenance, copyright, and contributor compensation.