Although OpenAI released its latest O3 models recently, which sparked discussions about Artificial Intelligence, yet another breakthrough has been reported: DeepSeek’s DeepSeek-V3 model, which has surpassed GPT-4 and Claude 3.5 in different benchmarks. This Chinese AI model was comparatively trained on a much smaller budget and with fewer resources. To say the least, waves are being made in the AI community by it for its innovation and cost-efficiency.

What is DeepSeek- V3?
DeepSeek-V3 is a Mixture-of-Experts (MoE) model with 671 billion parameters that is noted for its impressive training cost of just $5.5M. Basically, MoE models work like a team of specialists collaborating to answer questions. The way DeepSeek- V3 AI model is outperforming the leading AI models, it is going to be a gamechanger in the AI landscape.
Features of DeepSeek- V3:
It is the biggest leap forward yet, with:
- 60 tokens/second (3x faster than V2!)
- Enhanced capabilities
- API compatibility intact
- Fully open-source models & papers

DeepSeek’s defining features are that it uses advanced techniques to make memory usage more efficient, especially for tasks which require a lot of computing power. DeepSeek-V3 reduces performance slowdowns with its “auxiliary-loss-free load balancing“. The model is not only cost effective but also uses less memory and works comparatively faster.
DeepSeek- V3 can process up to 128,000 tokens at once which makes it ideal for complicated tasks like legal document review and academic research. Additionally, its multi-token prediction (MTP) predicts multiple words simultaneously, making it up to 1.8 times faster than traditional models.
DeepSeek’s Model Summary:
- Creative Architecture and Load Balancing
DeepSeek-V3 utilizes an advanced load balancing strategy which helps in minimising performance issues typically caused by balancing various workloads. It also incorporates Multi-Token Prediction (MTP), which accelerates the processing and improves performance by predicting several words at once.
- Pre Training: With Ultimate Focus On Training Efficiency
Its FP8 mixed precision training framework significantly reduces costs and training time. An interesting fact is that developers trained DeepSeek-V3 with just 2.664 million GPU hours on a 14.8 trillion token dataset. The additional training stages require minimal GPU time.
- Post Training: Knowledge Distillation for Improved Reasoning
In post-training, DeepSeek-V3 improves reasoning skills through knowledge distillation from the DeepSeek-R1 model. What this does is it improves the model’s reasoning abilities while still maintaining control over the output’s style as well as its length.
Performance:

The evaluation shows the best results in bold. DeepSeek is achieving the best performance on most of the benchmarks. As mentioned earlier, while it is outperforming models like OpenAI’s GPT-4o and Claude 3.5 Sonnet in various aspects, it is also excelling in coding and mathematics. It surpasses models like LiveCodeBench and Math-500 in coding and mathematics benchmarks.
Isn’t this advancement and growth just amusing? Developments like these continue to push the boundaries of what Artificial Intelligence can achieve. DeepSeek-V3 is set to redefine the future of artificial intelligence. Explore DeepSeek-V3 by chatting directly on its official website, chat.deepseek.com.
Are you curious about how you can use these new models like a pro? Learn how to unlock AI’s full potential through prompt generators by clicking here.