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On August 26, market pricing indicated that expectations for a Federal Reserve rate hike next month have slightly increased. This follows the release of data from the U.S. government showing that the inflation indicator closely monitored by the Fed rose by 3.7% year-on-year in July, slightly above economists' expectations. Futures data revealed that after the announcement, the market estimated the probability of a Fed rate hike in September to be around 42%, up from approximately 36% before the data was released. (Jin Shi)

On August 26, market pricing indicated a slight increase in expectations for a Federal Reserve rate hike next month. This comes after the U.S. government released data showing that the inflation indicator closely monitored by the Fed rose 3.7% year-on-year in July, slightly above economists' expectations. Futures data indicated that following the release of this data, the market now estimates the probability of a rate hike by the Fed in September at around 42%, up from approximately 36% before the data was released. (Jin Shi)

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.