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Alibaba's Qwen3.8-Max introduces a 2.4-trillion parameter MoE architecture with open weights scheduled for release next week, offering significant scaling potential but requiring multi-node datacenter infrastructure due to its size. The model supports a massive 1M-token context window and includes specific pricing tiers that heavily incentivize cached inputs over fresh prompts to manage serving costs effectively. While the hosted API is immediately deployable via standard integration methods, developers should evaluate whether their use cases require full-scale MoE deployment or if smaller checkpoints like Qwen3.8-27B are sufficient for on-premise GPU hardware.

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Alibaba Qwen Releases Qwen3.8-Max: A 2.4 Trillion Parameter MoE Model and the Most Capable One in the Qwen Family to Date

MarkTechPostAugust 3, 20263 min read

Alibaba's Qwen team moved Qwen3.8-Max from preview to general availability, with published per-token pricing and open weights due next week. The 2.4T parameter MoE model accepts text, image and video input across a 1M-token context. No benchmark table has been published. The post Alibaba Qwen Releases Qwen3.8-Max: A 2.4 Trillion Parameter MoE Model and the Most Capable One in the Qwen Family to Date appeared first on MarkTechPost.

Alibaba’s Qwen team has made Qwen3.8-Max broadly available and confirmed that its open weights ship next week. A second checkpoint, Qwen3.8-27B, is also going open-weights. Qwen3.8-Max is a 2.4-trillion-parameter mixture-of-experts model. It accepts text, image and video as input and returns text. Yes, but the deployable surface depends on which artifact you are applying. The hosted API is deployable today by…

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