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

Mach-Mind-4-Flash activates 3 billion parameters per token and reaches its results without scaling pretraining compute, using multi-track reinforcement learning (RL), multi-teacher distillation and a token-efficiency stage.

- Published: 2026-07-18T05:46:36.130Z
- Canonical: https://polylog.news/ai/2026-07-18/li-auto-s-35b-mixture-of-experts-model-claims-100b-class-per
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Polylog editors](https://polylog.news), [arXiv](https://arxiv.org/abs/2607.09375)

The AI division of Chinese electric-vehicle maker Li Auto (Lixiang) published a [technical report on Mach-Mind-4-Flash](https://t.me/ai_machinelearning_big_data/10546), 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](https://arxiv.org/abs/2607.09375).

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.

## What this means

If small activated-parameter MoE models can be raised to larger-model behavior with post-training alone, the advantage shifts from compute-rich pretraining firms toward teams with strong RL environment design and distillation pipelines, and the beneficiaries are deployers who want cheap-to-serve models. It also strengthens the non-US open-weight ecosystem, since another capable Chinese model with a small serving cost is exactly what compute-constrained governments and enterprises standardize on.

## What to watch

- Independent evaluations of Mach-Mind-4-Flash on public agentic and reasoning benchmarks against a stated 100B baseline, which will confirm or undermine the parity claim.
- Whether more non-lab industrial firms publish frontier-adjacent models, signaling that post-training know-how is diffusing beyond the dedicated labs.