DeepSeek-V4-Pro-Max Benchmark
The new model achieves 95% accuracy on MMLU, rivaling GPT-4...
DeepSeek AI, a leading Chinese AI lab, launched DeepSeek-V4-Pro-Max, a 300 billion parameter transformer model that sets new records on key benchmarks. It achieves 95% accuracy on MMLU (massive multitask language understanding) and 92% on HumanEval (code generation), outpacing GPT-4 by 5% and 8% respectively. The model also excels in long-context tasks with a 128K token window and is 2.5x faster on inference using NVIDIA H100 GPUs due to optimized attention mechanisms and sparse activation.
This open-weight release on Hugging Face allows developers to fine-tune and deploy the model for enterprise applications, from code generation to complex reasoning. DeepSeek claims the model was trained on a 10 trillion token dataset, including curated code and scientific papers, with a 40% reduction in training cost compared to GPT-4. The model supports multiple languages and excels in math, science, and programming benchmarks, making it a strong competitor for both research and production use.
- 300B parameters with 95% MMLU and 92% HumanEval accuracy
- 2.5x faster inference than GPT-4 on H100 GPUs
- 128K context window with open-weight release on Hugging Face
Why It Matters
DeepSeek-V4-Pro-Max challenges GPT-4 with open weights, enabling cheaper, faster AI deployment for enterprises.