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 (70B): The model's total response time is 1.9s. This makes it one of the fastest open-source models. It allows for quick text generation without compromising quality, making it ideal for developer applications and research use cases.

• Google Gemini 2.0 Pro: The fastest proprietary model in full response time, Gemini 2.0 Pro has a total response time of just 1.4s. This allows it to process and generate text efficiently, even in multimodal scenarios. Despite its strong multimodal capabilities, it does not lag behind in text-based tasks.

• DeepSeek-V3: With a response time of 8.7s, DeepSeek-V3 is slower than GPT-4o, Gemini, and Llama 3.3, making it less ideal for real-time applications. However, it is optimized for research and long-context tasks, making it useful for handling large-scale documents and structured reasoning applications.

• DeepSeek-R1: The slowest model in this comparison, DeepSeek-R1, has a response time of 57.6s. Its high response time makes it impractical for most standard AI use cases, even though it excels in deep logical reasoning and structured problem-solving.

• Mistral Large 2: Mistral Large 2's response time is 3.1s. This makes it one of the best options for real-time AI applications, excelling in quick response times, chat-based interactions, and dynamic processing tasks.