Meituan Releases LongCat-2.5 Large Model, 1.6 Trillion Parameters Trained Entirely on Domestic Compute
On November 2, Meituan released its new-generation large model LongCat-2.5, a native multimodal model designed for long-horizon agent tasks with 1.6 trillion total parameters. The model continues the research direction of the LongCat series and was trained entirely on domestic compute, making it the largest training task completed on domestic compute to date.
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The original text provides the model's parameter scale and the source of its training compute, allowing readers to understand the current magnitude of large-model training supported by domestic compute.
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Meituan releases LongCat-2.5 large model, achieving a new breakthrough in "domestic chips and domestic models"
Author of this article: Xu Mi
2026-11-02 15:23
Introduction: On November 2, Meituan released a new-generation large model, LongCat-2.5. It is a native multimodal model designed for long-horizon agent tasks, with 1.6 trillion total parameters. LongCat-2.5 continues the previous LongC
On November 2, Meituan released a new-generation large model, LongCat-2.5. It is a native multimodal model designed for long-horizon agent tasks, with 1.6 trillion total parameters. LongCat-2.5 continues the R&D direction of the previous LongCat series models and was trained entirely on domestic computing power. It is currently the largest training task completed on domestic computing power.
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