Comparison Between MiniMax-01 and DeepSeek-V3

Comparison Between MiniMax-01 and DeepSeek-V3

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Comparison table

Aspect
MiniMax-01
DeepSeek-V3
Model Architecture
Based on linear attention mechanism, using a hybrid architecture (Hybrid-Lightning), and integrating MoE architecture.
Based on Transformer architecture, using MLA and DeepSeekMoE architectures, and introducing auxiliary loss-independent load balancing strategies.
Parameter Scale
456 billion total parameters, 45.9 billion active parameters.
671 billion total parameters, 37 billion active parameters.
Training Data
14.8 trillion tokens, covering academic literature, books, web content, and programming code.
14.8 trillion tokens, covering high-quality, diverse text data, and optimizing the ratio of mathematical and programming samples.
Training Strategy
Employs a three-phase training method, expanding the context window to 1 million tokens, and ultimately extrapolating to 4 million tokens.
Employs a two-phase context expansion training, expanding the context window from 4K to 32K, then to 128K.
Training Cost
Not specified, but emphasizes high training efficiency.
2.788 million H800 GPU hours, total cost approximately 5.576 million USD.
Multimodal Capability
MiniMax-VL-01 expands the model’s multimodal understanding capability by integrating image encoders and image adapters.
No mention of multimodal capability.
Performance
Excels in long context processing, performing well in long context benchmarks such as Ruler and LongBench-V2.
Excels in most benchmarks, especially in mathematical and coding tasks, demonstrating strong capabilities in long context understanding tasks, such as performing well on FRAMES and LongBench v2.
Advantages
– Linear attention mechanism and hybrid architecture give it an edge in handling ultra-long contexts.
– MoE architecture and global routing strategies improve training efficiency.
– Variable-length ring attention and improved LASP algorithm further enhance long context processing capabilities.
– MLA and DeepSeekMoE architectures achieve strong performance while ensuring efficient training and inference.
– Auxiliary loss-independent load balancing strategies and multi-token prediction training objectives enhance model performance.
– FP8 mixed precision training framework reduces training costs.
Limitations
– Retains some softmax attention layers in the hybrid architecture, which may affect long context processing efficiency.
– Performance in complex programming tasks needs improvement.
– Lacks a deeper evaluation of long context retrieval and reasoning capabilities.
– Recommended deployment units are large, which may burden small teams.
– Inference speed still has room for improvement.

Conclusion of Comparison Between MiniMax-01 and DeepSeek-V3

Both MiniMax-01 and DeepSeek-V3 are innovative models aimed at breaking the performance bottlenecks of existing LLMs, each with its focus:

  • MiniMax-01 focuses more on long context processing capabilities, with its linear attention mechanism and hybrid architecture giving it an advantage in handling ultra-long sequences.
  • DeepSeek-V3 excels in maintaining efficient training and inference, performing well in mathematical and coding tasks, and demonstrating strong capabilities in long context understanding.

Both adopt MoE architecture and advanced training strategies, enhancing model performance while considering training costs and efficiency.

In the future, with continuous advancements in hardware and algorithms, both MiniMax-01 and DeepSeek-V3 are expected to achieve greater breakthroughs in their respective fields, driving the development of LLMs.

 

Original: https://zhuanlan.zhihu.com/p/18653363414, Compiled by: Qingke

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