LongCat-2.5 Open Weights vs Kimi K3.1 Pro: MIT Wins
On 10 November 2026 Meituan published the LongCat-2.5 open weights on Hugging Face under the MIT licence: a 1.6 trillion parameter mixture-of-experts model with about 48 billion parameters active per token, in a 3.56 TB repository. On licence terms it beats Kimi K3.1 Pro and Baidu's ERNIE 5.5 Flash Base, because neither revenue gates nor territory carve-outs apply. For most Indian developers and…
Meituan released the weights for its LongCat-2.5 open weights model on Hugging Face with an MIT license on November 10, 2026. This makes it the lowest-risk large open-weights base to build on this month, as it has no revenue gates or territory carve-outs. The model is 1.6 trillion parameters with about 48 billion parameters active per token, and it comes in a 3.56 TB repository.
For most Indian developers, it's recommended to rent the hosted API first at roughly ₹28 per million input tokens, and only self-host if already operating trillion-parameter MoE serving. The MIT license grants no rights to Meituan trademarks or patents, which is important for legal review. Benchmarks show that LongCat-2.5 is close to the leaders in Terminal Bench v2.1, but not ahead.
The model adds native visual input alongside agentic coding, and benchmarks are mostly in-house. The MIT license is more permissive than the licenses of Kimi K3.1 Pro and ERNIE 5.5 Flash Base, both of which have additional constraints. To download the LongCat-2.5 open weights, you can use the command "hf download hf://meituan-longcat/LongCat-2.5".
The model can be run on NPU platforms through SGLang-FluentLLM, but GPU users should wait for community support. Renting the API is recommended for better cost-effectiveness, especially for prototype, evaluation, and spiky traffic.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
