The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as reasoning, ML research workflows, software, engineering, and office work. The open weights release of MiniMax M2.7 is now available through NVIDIA and across the open source inference ecosystem.
The MiniMax M2 series is a sparse mixture-of-experts (MoE) model family designed for efficiency and capability. The MoE design keeps inference costs low while preserving the full capacity of a 230B-parameter model. It uses multi-head causal self-attention enhanced with Rotary Position Embeddings (RoPE) and Query-Key Root Mean Square Normalization (QK RMSNorm) for stable training at scale. A top-k expert routing mechanism ensures that only the most relevant experts activate for any given input, keeping inference costs low despite the model’s large total parameter count. The result is an architecture tuned to excel at coding challenges and complex agentic tasks.
MiniMax M2.7 Modalities Language Total parameters 230B Active parameters 10B Activation rate 4.3% Input context length 200K Additional configuration information Experts 256 local experts Experts activated per token 8 Layers 62 Table 1. MiniMax M2.7, a text MoE model with 230B parameters, 10B active per token, 256 experts, and 200K context length
