MiniMax AI Releases MiniMax-M1: A 456B Parameter Hybrid Model for Long-Context and Reinforcement Learning RL Tasks

Understanding the Target Audience

The target audience for MiniMax AI’s release of MiniMax-M1 includes AI researchers, data scientists, software engineers, and business leaders in technology. These individuals are typically well-versed in AI and machine learning concepts and are looking for scalable solutions to complex problems.

Pain Points: The audience often struggles with the limitations of existing AI models, particularly in handling long-context reasoning and the associated computational costs. They seek efficient models that can deliver results without excessive resource consumption.

Goals: Their primary goals include improving AI performance in real-world applications, enhancing reasoning capabilities, and reducing operational costs associated with AI deployments.

Interests: This audience is interested in advancements in AI architectures, particularly those that can manage long input sequences and improve reinforcement learning efficiency.

Communication Preferences: They prefer concise, technical content that includes data-driven insights, peer-reviewed research, and practical applications of AI technologies.

The Challenge of Long-Context Reasoning in AI Models

Large reasoning models are designed not only to understand language but also to process multi-step tasks requiring prolonged attention spans and contextual comprehension. As expectations from AI evolve, particularly in real-world and software development contexts, researchers have pursued architectures capable of managing longer inputs and maintaining coherent reasoning chains without incurring high computational costs.

Computational Constraints with Traditional Transformers

The main challenge in expanding reasoning capabilities lies in the substantial computational load associated with longer generation lengths. Traditional transformer-based models utilize a softmax attention mechanism, which scales quadratically with input size, limiting their efficiency in handling long input sequences or extended reasoning chains. This issue is critical in real-time interactions or cost-sensitive applications where inference expenses are significant.

Existing Alternatives and Their Limitations

Various methods have been explored to address these challenges, including sparse attention and linear attention variants. Some teams have tested state-space models and recurrent networks as alternatives to traditional attention structures. However, these innovations have seen limited adoption in competitive reasoning models due to architectural complexity or scalability issues in real-world deployments. Even large-scale systems like Tencent’s Hunyuan-T1, which employs a novel Mamba architecture, remain closed-source, limiting broader research engagement and validation.

Introduction of MiniMax-M1: A Scalable Open-Weight Model

MiniMax AI has introduced MiniMax-M1, an open-weight, large-scale reasoning model that combines a mixture of experts architecture with efficient attention mechanisms. Evolving from the MiniMax-Text-01 model, MiniMax-M1 features 456 billion parameters, with 45.9 billion activated per token. It supports context lengths of up to 1 million tokens—eight times the capacity of DeepSeek R1. This model addresses computational scalability at inference time, consuming only 25% of the FLOPs required by DeepSeek R1 at a 100,000 token generation length. It was trained using large-scale reinforcement learning across a diverse range of tasks, from mathematics and coding to software engineering, marking a significant shift toward practical, long-context AI models.

Hybrid-Attention with Lightning Attention and Softmax Blocks

To optimize its architecture, MiniMax-M1 employs a hybrid attention scheme where every seventh transformer block utilizes traditional softmax attention, followed by six blocks using lightning attention. This approach significantly reduces computational complexity while maintaining performance. The lightning attention is I/O-aware, adapted from linear attention, making it particularly effective at scaling reasoning lengths to hundreds of thousands of tokens. For reinforcement learning efficiency, the researchers introduced a novel algorithm called CISPO. Unlike traditional methods that clip token updates, CISPO clips importance sampling weights, enabling stable training and consistent token contributions, even during off-policy updates.

The CISPO Algorithm and RL Training Efficiency

The CISPO algorithm has been crucial in overcoming training instability in hybrid architectures. In comparative studies against the Qwen2.5-32B baseline, CISPO achieved a 2x speedup over DAPO. This allowed the full reinforcement learning cycle for MiniMax-M1 to be completed in just three weeks using 512 H800 GPUs, with a rental cost of approximately $534,700. The model was trained on a diverse dataset comprising 41 logic tasks generated via the SynLogic framework and real-world software engineering environments derived from the SWE bench, utilizing execution-based rewards to guide performance and resulting in stronger outcomes in practical coding tasks.

Benchmark Results and Comparative Performance

MiniMax-M1 delivered impressive benchmark results. Compared to DeepSeek-R1 and Qwen3-235B, it excelled in software engineering, long-context processing, and agentic tool use. Although it lagged behind the latest DeepSeek-R1-0528 in math and coding contests, it outperformed both OpenAI o3 and Claude 4 Opus in long-context understanding benchmarks. Furthermore, it surpassed Gemini 2.5 Pro in the TAU-Bench agent tool use evaluation.

Conclusion: A Scalable and Transparent Model for Long-Context AI

MiniMax-M1 represents a significant advancement by providing both transparency and scalability. By addressing the dual challenges of inference efficiency and training complexity, the research team at MiniMax AI has set a new standard for open-weight reasoning models. This development not only resolves compute constraints but also introduces practical methods for scaling language model intelligence into real-world applications.

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