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, with 1.6 trillion total parameters and approximately 48 billion parameters activated per token, continuing the feature of LongCat-2.0 in being trained entirely on domestic compute. Meituan says the model is a native multimodal model for long-horizon agent tasks, strengthening native visual understanding and agentic coding capabilities on the basis of LongCat-2.0, and supporting large-scale long-horizon RL by optimizing long-context training on domestic compute along with W4A4 inference, training-inference numerical consistency, and long-context rollout optimization. Meituan says its results on multiple mainstream benchmark evaluations fall between those of Opus 4.8 and Opus 5, released by Anthropic in May and July of this year.
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Meituan's LongCat-2.5 continues training entirely on domestic compute and provides benchmark comparisons with Opus 4.8 and Opus 5, offering a view of where large models trained on domestic compute actually stand.
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2026-11-03 07:10:35 Source: Caixin.com Author: Yang Zirui Responsible Editor: Qu Yunxu
By Caixin Yang Zirui
November 03, 2026 07:10
Meituan said LongCat-2.5 scored between Opus 4.8 and Opus 5 on multiple mainstream benchmark evaluations; the latter two models were released by Anthropic in May and July this year
Meituan's LongCat large model team said in an announcement that the new model further strengthens native visual understanding and agentic coding capabilities on the basis of LongCat-2.0. Image: Visual China
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[Caixin.com] On November 2, Meituan (03690.HK) released its new-generation large model LongCat-2.5. LongCat-2.5 continues the feature of the previous-generation model LongCat-2.0 of being trained entirely on domestic computing power. Meituan said LongCat-2.5 is a native multimodal model for long-horizon agent tasks, with 1.6 trillion total parameters and about 48 billion parameters activated per token.
Meituan's LongCat large model team said in an announcement that the new model further strengthens native visual understanding and agentic coding capabilities on the basis of LongCat-2.0. These upgrades stem from the team's optimization of long-context training and W4A4 inference on domestic computing power, and through training-inference numerical consistency and long-context rollout optimization, they support the efficient operation of large-scale long-horizon RL (reinforcement learning).
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