Xiaohongshu AI Lab, dots.studio, has open-sourced the dots3-note preview model, according to PANews. The model uses a 280-billion-parameter Mixture-of-Experts architecture with 16 billion activated parameters and offers a 512,000-token context window. It also supports multimodal understanding across text, vision, and audio, while introducing a reinforcement learning method called TEMPO for long-horizon agent training.
Model weights are now available on Hugging Face, and the API has been integrated with OpenRouter. A chart shared by SemiAnalysis shows dots3-note scoring 75.1 on Terminal-Bench 2.1, which is 4.9 points higher than the best-performing U.S. open-weight model shown in that chart. At the same time, SemiAnalysis said its team is still testing the model in everyday use to judge real-world quality and check whether it may have been overly optimized for benchmark performance.
Xiaohongshu AI Lab, dots.studio, has open-sourced the dots3-note preview model, PANews reported on Aug. 15.
The model uses a 280-billion-parameter Mixture-of-Experts, or MoE, architecture, with 16 billion activated parameters. It provides a 512,000-token context window and supports multimodal understanding across text, vision, and audio. dots.studio also introduced a reinforcement learning method called TEMPO for long-horizon agent training.
The model weights have been released on Hugging Face, and its API has been connected to OpenRouter.
A chart shared by SemiAnalysis shows dots3-note scored 75.1 on Terminal-Bench 2.1. That is 4.9 points above the best-performing U.S. open-weight model shown in the same chart.
SemiAnalysis also said its team is still testing the model in daily use to assess its practical quality and determine whether the benchmark result reflects over-optimization for the evaluation.
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