Qwen releases 2.4T-parameter open MoE with 95B active

Qwen, the AI group inside Alibaba, has published Qwen3.8-2.4T-A95B on Hugging Face — a text and reasoning model that stands as the team's largest open Mixture-of-Experts release to date. The headline figures are in the name: roughly 2.4 trillion total parameters, with about 95 billion active for any given token.

That sparse design is the whole point. Rather than firing every parameter on every forward pass, an MoE routes each token to a small subset of specialized "experts." The result is a model with the knowledge capacity of a very large network but the per-token compute closer to a mid-sized dense model. In practice, the 95B active count is what determines inference cost, while the 2.4T total sets the ceiling on what the model can store.

Why it matters

• Scale meets openness. Frontier-scale MoE models have largely stayed behind closed APIs; shipping weights at this size continues Qwen's push to keep serious capability in the open ecosystem.
• Reasoning focus. The model is tagged for both general text and reasoning, signaling it's aimed at the harder problem-solving workloads that increasingly define the top tier.
• Deployment realism. A 2.4T-parameter checkpoint is not something most teams will run casually; expect adoption to concentrate among well-resourced labs and inference providers.

One caveat worth flagging: the release carries an "other" license rather than a standard permissive one, so teams should read the terms on the model page before building on it. As always with a fresh drop, independent benchmarks and real-world testing will determine how the raw parameter counts translate into everyday performance.