Comparison of DeepSeek with Other AI Models
Understand the comparative strengths and weaknesses of DeepSeek-R1, GPT-4o, Gemini 2.0, Llama 3.3, and others. Learn about their response times, reasoning abilities, and coding proficiency to evaluate which AI model suits different applications.
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.
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