The open-weights model from MiniMax, built on linear-attention mixture-of-experts. Currently running in the Berges deep-thinking layer.
MiniMax is an AI lab headquartered in Singapore. The MiniMax model family is known for fast generation and large effective context, built on a linear-attention mixture-of-experts architecture that handles long inputs efficiently.
Berges AI runs MiniMax in the deep-thinking layer alongside Kimi and DeepSeek-Pro. You can pick it explicitly from the sidebar if you want to see how the same prompt plays out across the three.
The weights are open and the architecture is documented publicly.
Linear attention scales better with context length than standard attention. The practical effect is quicker responses on long-form generation.
MiniMax handles long inputs efficiently, so it does well on tasks where you're feeding the model a lot of material to reason over.
Strong on Chinese-English work and reasonable across other major languages.
MiniMax is headquartered in Singapore. The model weights are open and the company publishes details about its architecture publicly.
On Berges AI you don't have to. The cascade handles it. If you do pick manually, MiniMax tends to be faster on long-form generation, while Kimi is stronger on long-context reasoning and DeepSeek on math and code.
The weights are open. The training data is generally not, which is typical for the open-weights category. You can audit and run the model, just not retrain it from scratch with the same data.