What benchmarks has DeepSeek's AI models achieved?

DeepSeek’s AI models have demonstrated strong performance across multiple benchmarks in natural language processing, code generation, and mathematical reasoning. These benchmarks measure capabilities like text understanding, problem-solving, and task-specific accuracy. The models are tested on industry-standard datasets to ensure they meet practical requirements for real-world applications, particularly in developer-focused scenarios.

In natural language processing, DeepSeek models achieve competitive scores on datasets such as GLUE and SuperGLUE, which evaluate general language understanding. For example, on the HellaSwag commonsense reasoning benchmark, DeepSeek’s models report accuracy rates above 85%, comparable to models like GPT-3.5. In code generation, they perform well on HumanEval, a test measuring functional correctness of Python code, with pass@1 scores exceeding 70%—close to GPT-4’s performance. The models also handle multilingual coding tasks, scoring over 65% on MBPP (Mostly Basic Python Problems) for non-English prompts, demonstrating versatility in cross-linguistic environments.

For mathematical reasoning, DeepSeek models excel on benchmarks like MATH and GSM8K, which test problem-solving through step-by-step calculations. On MATH, a dataset with high-school-level competition problems, the models achieve over 45% accuracy, outperforming open-source alternatives like LLaMA-2. On GSM8K (grade-school math problems), they reach above 80% accuracy, showing robustness in handling arithmetic and logical steps. Additionally, in multimodal tasks like MMMU, which combines text and image analysis, DeepSeek models score over 60% accuracy, indicating strong cross-modal understanding. These results highlight the models’ ability to handle diverse technical challenges while maintaining efficiency in resource usage, making them practical for integration into developer tools and workflows.