Meituan has released the weights for LongCat-2.5 under the MIT License. The native multimodal MoE is built for long-horizon coding and agent tasks, with 1.6T total parameters and about 48B activated per token.
🤖 https://modelscope.ai/models/meituan-longcat/LongCat-2.5
- Stronger coding agents. LongCat-2.5 improves substantially over LongCat-2.0 on DeepSWE and Terminal-Bench v4.0 in Meituan’s tests.
- Visual coding. The model processes image and text tokens together, so it can use a screenshot as a reference when writing webpage code.
- 3D modeling through code. The model can turn a text description into a Blender script and leads the models in Meituan’s 3DCodeBench Text-to-3D comparison.
- Training for longer tasks. Agentic RL uses real engineering tasks and reproducible agent failures. LongCat-2.5 also posts the highest BrowseComp score in Meituan’s reported comparison.
- INT4 inference on NPUs. With quantized KV cache, W4A4 nearly doubles decode throughput over W8A8KV16 in Meituan’s stress test.