Models New
Meituan releases LongCat-2.5, a 1.6-trillion-parameter multimodal model for long agent and coding tasks, reportedly trained on Chinese chips
Published:
Meituan's LongCat team has released LongCat-2.5, a native multimodal model for long-horizon agent tasks. It has 1.6 trillion total parameters, with about 48 billion active per token, and builds on LongCat-2.0 with stronger visual understanding and agentic coding.
The team says it trained on more long-horizon trajectories, used large-scale reinforcement learning on complex codebases and full-project builds, and turned real agent failures into verifiable training tasks. Images are represented as multi-level discrete tokens, so text and vision share one token format.
The LongCat API changelog lists the release on 2 November and says the model works with agent harnesses such as Claude Code, Codex and Hermes. The Economic Observer reports that training ran entirely on domestic Chinese compute, and that Meituan's own results place it between Claude Opus 4.8 and Claude Opus 5 on several benchmarks.
Caveat. Benchmark placings are Meituan's own results, reported by the Economic Observer; this journal did not see an independent evaluation. The blog post is dated 1 November while the API changelog and Chinese press give 2 November. The sources read do not say whether weights are released.
Sources