Meituan Releases LongCat 2.5, a Multimodal Agentic Model With 1.6 Trillion Total Parameters, Compared With GPT and Claude
Meituan released LongCat-2.5, its new multimodal model with 1.6T total parameters and 49B active. LongCat-2.5鈥檚 compares with GPT-5.5, GPT-5.6-Sol, Claude-Opus-4.8, and Claude-Opus-5 and is now available on the APIs.
Context
A Chinese delivery giant shipping a model it says rivals top Western systems on agentic coding, trained on domestic chips, shows how crowded the frontier is getting.
Meituan, the Chinese food delivery and local services company, builds the LongCat family of models. LongCat 2.5 follows LongCat 2.0 and is described by Meituan as natively multimodal and built for long horizon agentic tasks. Meituan says it trained and serves the model on domestic AI accelerators.
Deep dive
TestingCatalog (@testingcatalog) posted on Nov 2 that Meituan released LongCat 2.5, a multimodal model with 1.6 trillion total parameters and 49 billion active, compared with GPT 5.5, GPT 5.6 Sol, Claude Opus 4.8 and Claude Opus 5, and now available on the APIs.
Meituan's own launch post and blog give the active count as approximately 48 billion per token, slightly lower than TestingCatalog's figure. Meituan describes LongCat 2.5 as a natively multimodal agentic model for long horizon tasks, with stronger visual understanding and agentic coding than LongCat 2.0. It turns images into discrete tokens so the same backbone handles text and vision, and it was trained with large scale reinforcement learning on long coding tasks. Meituan says it optimized long context training and 4 bit inference on domestic AI accelerators.
All benchmark numbers come from Meituan and are its own claims. Its table reports, for example, 88.4 on Terminal Bench v2.1, 91.9 on BrowseComp and 63.1 on Humanity's Last Exam with tools. The same table shows Claude Opus 5 ahead on several tests, including Terminal Bench v4.0, where Meituan lists 53.9 for Opus 5 against 38.5 for LongCat 2.5. Meituan notes that scores for other models marked in its table come from public reports, while others were run by Meituan and are indicative only.
Independent evaluations have not yet been published, so how LongCat 2.5 performs outside Meituan's own tests is still unknown.
What to expect next
Watch for independent benchmark results and leaderboard entries over the coming weeks, which will show whether Meituan's claims hold. Developers will test pricing and availability on its API, and whether open weights follow as with earlier LongCat models. The domestic chip training claims will also draw attention as US export controls continue. If the results hold up, LongCat 2.5 would add pressure on other Chinese labs and on Western providers to cut prices for agentic coding.