Chinese food-delivery giant Meituan has fully open-sourced its agentic coding model "LongCat-2.0" under the MIT license. The 1.6-trillion-parameter Mixture-of-Experts (MoE) model has been released with weights and inference code that anyone can freely use, modify, and deploy commercially.
Meituan · Open-Source Release
A 1.6-Trillion-Parameter Frontier Model — Trained Entirely on Chinese Chips
LongCat-2.0, the model that quietly topped OpenRouter as "Owl Alpha," is now fully open under the MIT license — weights and inference code included, with no restrictions on commercial use.
1.6T
Total parameters (MoE)
~48B
Active params at inference (33B–56B)
1M
Native context window (tokens)
~50k
Domestic AI ASIC cards — no NVIDIA
Agentic Coding: Open Weights vs. Closed Frontier
Benchmark scores (higher is better) — LongCat-2.0 vs. vendors' own reported figures.
SWE-bench Pro · real-world software engineering
Terminal-Bench 2.1 · terminal operations
77.3
SWE-bench Multilingual
What developers welcome
1M-token context — review whole codebases, long agent sessions
MIT license + public inference code = free local / in-house deployment
Native support for Claude Code, OpenClaw, Hermes agents
Frontier-class training done wholly on domestic chips
The caveats
Agent can fall into infinite loops or self-confusion
Heavy hardware needed: BF16 ~3.55TB, FP8 in the hundreds of GB
Not runnable on consumer hardware — clusters or API only
Some Chinese rivals (e.g. GLM) reportedly ahead in tests
Why it matters
Frontier-scale pre-training and large-scale inference completed on ~50,000 domestic ASIC cards — no NVIDIA, no rollbacks.
TRAINING SCALE
35T+ tokens · LongCat Sparse Attention (LSA) + N-gram Embedding
DEPLOYMENT
SGLang on GPU (16×H20, TP16/EP16) · Huawei Ascend NPU · FP8/BF16
NEXT TO WATCH
Cost efficiency, scalability, and agent stability in real-world use
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