Li Auto's 35B Mixture-of-Experts Model Claims 100B-Class Performance From Post-Training Alone

The AI division of Chinese electric-vehicle maker Li Auto (Lixiang) published a technical report on Mach-Mind-4-Flash, a Mixture-of-Experts (MoE) model with 35 billion total parameters and roughly 3 billion activated per token. The central claim is that through post-training optimization alone, without additional pretraining compute, the model reaches performance on par with or above 100-billion-parameter-class systems.

The method, as described in the report, is a unified RL pipeline that orchestrates multiple expert tracks, a multi-teacher distillation stage, and a dedicated token-efficiency stage, plus scalable agentic interaction environments for large-scale reinforcement learning aimed at real-world application tasks.

The framing matters as much as the numbers. An automaker's AI group, not a dedicated frontier lab, is arguing that the marginal capability now comes from post-training and environment design rather than from raw scale. That claim awaits independent reproduction on named benchmarks, and "100B-class" is not a fixed standard, depending heavily on which baseline and which tasks are chosen. What is concrete is another downloadable Chinese model built to achieve frontier-adjacent behavior with a small number of activated parameters.