Introduction

In the rapidly evolving landscape of artificial intelligence, the development of powerful local models has become a game-changer for individuals and organizations seeking to harness the potential of AI without relying on cloud-based services. Among the most promising contenders in this arena are the MiniMax M1 and DeepSeek R1.0528 models, both of which have garnered significant attention for their impressive capabilities and performance. This article aims to provide an in-depth analysis of these two models, exploring their strengths, weaknesses, and potential applications.

MiniMax M1: A Powerhouse for Long Context Handling

The MiniMax M1 is a 456B A46B MoE (Mixture of Experts) model that has garnered attention for its exceptional performance in handling long-context scenarios. While it may not outperform the larger DeepSeek R1.0528 in certain benchmarks, the MiniMax M1 shines when it comes to tasks that require processing and understanding extensive amounts of information. This is evident in its impressive results on the OpenAI-MRCR benchmark, where it outperforms even GPT-4.1 at 128k and delivers similar performance at 1M context length.

MiniMax M1 is a 456B A46B MoE model that's a bit behind in benchmarks compared to the larger DeepSeek R1.0528 (671B) that has less active params (37B). It's often better or tied with the original R1, except for SimpleQA where it's significantly behind. The interesting thing is that it scores way better in the long context benchmark OpenAI-MRCR, delivering better results than GPT4.1 at 128k and similar at 1M context. This benchmark is just a "Needle in Haystack" variant though - a low score means the model is bad at long context, while a high score doesn't necessarily mean it's good at making something out of the information in the long context. In the more realistic LongBench-v2 it makes the 3rd place, right after the Gemini models, which also scored quite well in fiction.liveBench.

While the OpenAI-MRCR benchmark is a valuable indicator of long-context performance, it is important to note that a high score does not necessarily translate to superior performance in more realistic scenarios. As Chromix_ points out, the MiniMax M1 secures the third place in the more comprehensive LongBench-v2 benchmark, trailing behind the Gemini models, which also performed well in the fiction.liveBench evaluation.

DeepSeek R1.0528: A Versatile Powerhouse

The DeepSeek R1.0528, on the other hand, is a 671B model with 37B active parameters, making it a formidable contender in the local AI space. While it may not outperform the MiniMax M1 in specific long-context benchmarks, the DeepSeek R1.0528 is a more well-rounded model that excels across a broader range of tasks and benchmarks.

DeepSeek R1.0528 (671B) that has less active params (37B). It's often better or tied with the original R1, except for SimpleQA where it's significantly behind.

While the DeepSeek R1.0528 may not excel in specific long-context benchmarks like the OpenAI-MRCR, its versatility and strong performance across a wide range of tasks make it a compelling choice for users seeking a well-rounded local AI solution. Its ability to handle diverse scenarios and adapt to various use cases is a testament to the model's robustness and potential for real-world applications.

Practical Considerations: VRAM and Inference Speed

Beyond benchmarks and performance metrics, it is crucial to consider practical aspects such as VRAM requirements and inference speed when evaluating local AI models. The MiniMax M1, with its larger size, may pose challenges for users with limited VRAM resources, potentially hindering its adoption on lower-end systems. On the other hand, the DeepSeek R1.0528's smaller active parameter count could make it more accessible to a broader range of users.

from my benchmarks here [https://www.reddit.com/r/LocalLLaMA/comments/1kooyfx/llamacpp\_benchmarks\_on\_72gb\_vram\_setup\_2x\_3090\_2x/] Qwen2.5-72B-Instruct-Q6\_K - 9.14 t/s (fully in VRAM) Llama-4-Scout-17B-16E-Instruct-Q8\_0 - 15.1 t/s (larger model with -ot trick) Qwen3-235B-A22B-Q3\_K\_M - 10.41 t/s (even larger model, -ot trick again, I have slow RAM)

As highlighted by jacek2023's benchmarks, the inference speed can vary significantly depending on the model size, hardware configuration, and optimization techniques employed. While larger models like the MiniMax M1 may offer superior performance in certain scenarios, they may also require more powerful hardware and specialized techniques to achieve optimal inference speeds.

Exploring Real-World Applications

While benchmarks and performance metrics provide valuable insights into the capabilities of these models, it is equally important to consider their potential real-world applications. The MiniMax M1's strength in long-context scenarios could make it a valuable asset for tasks such as legal document analysis, scientific research, or any scenario where processing and understanding extensive amounts of information is crucial.

Don't trust benchmark JPEGs but be open to trying new things. If GGUFs show up I'm going to spin up a Lambda cloud instance and test this out on a bunch of my side projects and report back

As EmPips aptly points out, while benchmarks provide valuable insights, the true test of a model's capabilities lies in its real-world performance and application. By testing these models on actual projects and use cases, users can gain a deeper understanding of their strengths, weaknesses, and suitability for specific tasks.

Conclusion

In the rapidly evolving landscape of local AI models, the MiniMax M1 and DeepSeek R1.0528 stand out as two formidable contenders, each with its own unique strengths and potential applications. While the MiniMax M1 excels in long-context scenarios, the DeepSeek R1.0528 offers a more well-rounded performance across a broader range of tasks. Ultimately, the choice between these models will depend on the specific requirements and constraints of the user, including factors such as VRAM availability, inference speed, and the nature of the tasks at hand.As the AI community continues to push the boundaries of what's possible, it is essential to approach these models with an open mind and a willingness to explore their capabilities in real-world scenarios. By embracing a spirit of experimentation and collaboration, we can unlock the full potential of these powerful tools and pave the way for even more remarkable advancements in the field of artificial intelligence.