DeepSeek-R1, known for its low training cost and exceptional reasoning capabilities, has achieved state-of-the-art performance on various benchmarks. However, detailed evaluations for DeepSeek Series models from the perspective of real-world applications are lacking, making it challenging for users to select the most suitable DeepSeek models for their specific needs. To address this gap, we conduct a systematic evaluation of the DeepSeek-V3, DeepSeek-R1, DeepSeek-R1-Distill-Qwen series, DeepSeek-R1-Distill-Llama series, their corresponding 4-bit quantized models, and the reasoning model QwQ-32B using the enhanced A-Eval benchmark, A-Eval-2.0. Through a comparative analysis of original instruction-tuned models and their distilled counterparts, we investigate how reasoning enhancements impact performance across diverse practical tasks. To assist users in model selection, we quantify the capability boundary of DeepSeek models through performance tier classifications. Based on the quantification results, we develop a model selection handbook that clearly illustrates the relation among models, their capabilities and practical applications. This handbook enables users to select the most cost-effective models without efforts, ensuring optimal performance and resource efficiency in real-world applications. It should be noted that, despite our efforts to establish a comprehensive, objective, and authoritative evaluation benchmark, the selection of test samples, characteristics of data distribution, and the setting of evaluation criteria may inevitably introduce certain biases into the evaluation results. We will continuously optimize the evaluation benchmarks and periodically update this paper to provide more comprehensive and accurate evaluation results. Please refer to the latest version of the paper for the most current results and conclusions.

翻译：DeepSeek-R1以其低训练成本和卓越的推理能力著称，已在各类基准测试中取得最先进的性能。然而，目前缺乏从实际应用角度对DeepSeek系列模型进行的详细评估，这使得用户难以根据自身特定需求选择最合适的DeepSeek模型。为填补这一空白，我们使用增强版基准A-Eval-2.0，对DeepSeek-V3、DeepSeek-R1、DeepSeek-R1-Distill-Qwen系列、DeepSeek-R1-Distill-Llama系列、其对应的4位量化模型以及推理模型QwQ-32B进行了系统性评估。通过对原始指令微调模型及其蒸馏对应模型的比较分析，我们研究了推理增强如何影响不同实际任务中的性能表现。为辅助用户进行模型选择，我们通过性能层级分类量化了DeepSeek模型的能力边界。基于量化结果，我们开发了一份模型选择手册，清晰阐明了模型、其能力与实际应用之间的关系。该手册使用户能够轻松选择最具成本效益的模型，确保在实际应用中获得最优性能与资源效率。需要指出的是，尽管我们致力于建立一个全面、客观且权威的评估基准，但测试样本的选择、数据分布的特征以及评估标准的设定仍可能不可避免地给评估结果带来某些偏差。我们将持续优化评估基准，并定期更新本文，以提供更全面、准确的评估结果。请参阅论文最新版本以获取最新的结果与结论。