Comparison of DeepSeek with Other AI Models

In the last lesson, we understood DeepSeek. Now, we’ll evaluate leading AI models—GPT-4o, GPT-o1, Gemini 2.0, Llama 3.3, DeepSeek-V3, DeepSeek-R1, Mistral Large 2, and o3-mini—across critical benchmarks. These models represent some of the most advanced AI systems available, developed by leading AI research teams, ensuring a high-quality benchmark comparison. We’ll examine their response speed, accuracy, reasoning abilities, and coding proficiency to determine whether they meet expectations.

Total response time

Speed is a crucial factor in AI models, impacting their usability for real-time applications. Here, we discuss the total response time to output 100 tokens, including latency to generate the first token.

Note: These results represent average performance metrics and may vary depending on server load, API provider, hardware configurations, and specific query complexity. Real-world performance can differ based on implementation and optimization settings.

• GPT-4o: With a total response time of 1.8s, GPT-4o balances speed and reasoning capabilities well, making it one of the fastest proprietary models available. It optimizes real-time text, voice, and multimodal interactions, keeping responses smooth and natural.

• GPT-o1: This model is optimized for deep reasoning, but it sacrifices speed for accuracy, resulting in a significantly higher response time of around 31 seconds. This makes it less ideal for real-time interactions but more reliable in logic-heavy tasks that require in-depth processing.

• o3-mini: o3-mini is designed to be cost-efficient, but it struggles with speed. It takes 11.6s to generate 100 tokens, making it significantly slower than GPT-4o but faster than the o1 model. This delay impacts its usability in fast-paced applications, such as AI chatbots or live interactions.

• Llama 3.3 ...