MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning
  Attention
We introduce MiniMax-M1, the world's first open-weight, large-scale
hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid
Mixture-of-Experts (MoE) architecture combined with a lightning attention
mechanism. The model is developed based on our previous MiniMax-Text-01 model,
which contains a total of 456 billion parameters with 45.9 billion parameters
activated per token. The M1 model natively supports a context length of 1
million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning
attention mechanism in MiniMax-M1 enables efficient scaling of test-time
compute. These properties make M1 particularly suitable for complex tasks that
require processing long inputs and thinking extensively. MiniMax-M1 is trained
using large-scale reinforcement learning (RL) on diverse problems including
sandbox-based, real-world software engineering environments. In addition to
M1's inherent efficiency advantage for RL training, we propose CISPO, a novel
RL algorithm to further enhance RL efficiency. CISPO clips importance sampling
weights rather than token updates, outperforming other competitive RL variants.
Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on
512 H800 GPUs to complete in only three weeks, with a rental cost of just
$534,700. We release two versions of MiniMax-M1 models with 40K and 80K
thinking budgets respectively, where the 40K model represents an intermediate
phase of the 80K training. Experiments on standard benchmarks show that our
models are comparable or superior to strong open-weight models such as the
original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex
software engineering, tool utilization, and long-context tasks. We publicly
release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.
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Aonian Li
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Zijun Sun
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning
  Attention
We introduce MiniMax-M1, the world's first open-weight, large-scale
hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid
Mixture-of-Experts (MoE) architecture combined with a lightning attention
mechanism. The model is developed based on our previous MiniMax-Text-01 model,
which contains a total of 456 billion parameters with 45.9 billion parameters
activated per token. The M1 model natively supports a context length of 1
million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning
attention mechanism in MiniMax-M1 enables efficient scaling of test-time
compute. These properties make M1 particularly suitable for complex tasks that
require processing long inputs and thinking extensively. MiniMax-M1 is trained
using large-scale reinforcement learning (RL) on diverse problems including
sandbox-based, real-world software engineering environments. In addition to
M1's inherent efficiency advantage for RL training, we propose CISPO, a novel
RL algorithm to further enhance RL efficiency. CISPO clips importance sampling
weights rather than token updates, outperforming other competitive RL variants.
Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on
512 H800 GPUs to complete in only three weeks, with a rental cost of just
$534,700. We release two versions of MiniMax-M1 models with 40K and 80K
thinking budgets respectively, where the 40K model represents an intermediate
phase of the 80K training. Experiments on standard benchmarks show that our
models are comparable or superior to strong open-weight models such as the
original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex
software engineering, tool utilization, and long-context tasks. We publicly
release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.
A hybrid-attention reasoning model called MiniMax-M1, featuring a Mixture-of-Experts architecture and lightning attention mechanism, is introduced for efficient long-input processing and reinforcement learning.
Mixture-of-Experts (MoE) lightning attention mechanism reinforcement learning (RL) CISPO importance sampling weights token updates