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Chinese AI startup MiniMax, perhaps best known in the West for its hit realistic AI video model Hailuo, has released its latest large language model, MiniMax-M1 — and in great news for enterprises and developers, it’s completely open source under an Apache 2.0 license, meaning businesses can take it and use it for commercial applications and modify it to their liking without restriction or payment.
M1 is an open-weight offering that sets new standards in long-context reasoning, agentic tool use, and efficient compute performance. It’s available today on the AI code sharing community Hugging Face and Microsoft’s rival code sharing community GitHub, the first release of what the company dubbed as “MiniMaxWeek” from its social account on X — with further product announcements expected.
MiniMax-M1 distinguishes itself with a context window of 1 million input tokens and up to 80,000 tokens in output, positioning it as one of the most expansive models available for long-context reasoning tasks.
The “context window” in large language models (LLMs) refers to the maximum number of tokens the model can process at one time — including both input and output. Tokens are the basic units of text, which may include entire words, parts of words, punctuation marks, or code symbols. These tokens are converted into numerical vectors that the model uses to represent and manipulate meaning through its parameters (weights and biases). They are, in essence, the LLM’s native language.
For comparison, OpenAI’s GPT-4o has a context window of only 128,000 tokens — enough to exchange about a novel’s worth of information between the user and the model in a single back and forth interaction. At 1 million tokens, MiniMax-M1 could exchange a small collection or book series’ worth of information. Google Gemini 2.5 Pro offers a token context upper limit of 1 million, as well, with a reported 2 million window in the works.
But M1 has another trick up its sleeve: it’s been trained using reinforcement learning in an innovative, resourceful, highly efficient technique. The model is trained using a hybrid Mixture-of-Experts (MoE) architecture with a lightning attention mechanism designed to reduce inference costs.
According to the technical report, MiniMax-M1 consumes only 25% of the floating point operations (FLOPs) required by DeepSeek R1 at a generation length of 100,000 tokens.
Architecture and variants
The model comes in two variants—MiniMax-M1-40k and MiniMax-M1-80k—referring to their “thinking budgets” or output lengths.
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