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According to Beating AI news, Alibaba's Qianwen has released Qwen3.8-Flash and simultaneously open-sourced the model weights for Qwen3.8-Flash-Next. The open-source version includes a 'Next' that signifies it is the first to utilize the next-generation Qwen4 architecture. This is a multimodal MoE model with a main model containing 125 billion parameters, while the activated parameters are only 6 billion. Additionally, it has introduced a 51 billion N-gram embedding, which acts as a large 'lookup memory,' using more storage for less computation. In official evaluations, SWE-bench Pro scored 9.1 points higher than Claude Opus4.6, JobBench nearly 20 points higher; AndroidWorld scored 22.5 points higher, and MathVision scored 25.1 points higher. The model natively supports 262K context and can be expanded to 1M. The pricing is also very competitive. The production version is simultaneously launched on Qwen Cloud, which defaults to support 1M context and tool calls. The cost is 1 yuan for every million tokens input and 3 yuan for output. Officials claim that Qwen3.8-Flash achieves capabilities close to Qwen3.7-Plus, but with training costs of only about 1/9. Qianwen aims to provide flagship model capabilities at a lower cost with less computation.

According to Beating AI news, Alibaba Qianwen has released Qwen3.8-Flash and simultaneously open-sourced the model weights for Qwen3.8-Flash-Next. The open-source version includes a 'Next', indicating it is the first to utilize the next-generation Qwen4 architecture. This is a multimodal MoE model with a main model containing 125 billion parameters, while the activated parameters are only 6 billion. Additionally, it features a 51 billion N-gram embedding, which acts as a large 'lookup memory', trading more storage for less computation. In official evaluations, SWE-bench Pro scored 9.1 points higher than Claude Opus4.6, JobBench was nearly 20 points higher; AndroidWorld was 22.5 points higher, and MathVision was 25.1 points higher. The model natively supports 262K context and can be expanded to 1M. The pricing is also very competitive. The production version is simultaneously launched on Qwen Cloud, which defaults to support 1M context and tool invocation. The cost is 1 yuan per million tokens for input and 3 yuan for output. Officials claim that Qwen3.8-Flash achieves capabilities close to Qwen3.7-Plus, but with training costs of only about 1/9. Qianwen aims to provide flagship model capabilities at a lower cost with less computation.

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