Grok v3 vs DeepSeek v3

 

I asked Grok to compare Grok v3 with DeepSeek v3

Here's an honest reply directly from Grok:

Comparing Grok 3 directly to DeepSeek (specifically DeepSeek V3) involves considering several aspects where each model has its strengths and weaknesses. Here's a breakdown based on the available information:

Architecture and Training:

DeepSeek V3 uses a Mixture-of-Experts (MoE) architecture with 671B parameters, of which only 37B are active per token. It's trained on 14.8 trillion tokens with a focus on load balancing and multi-token prediction, which leads to efficient performance and cost-effectiveness. Its open-source nature and lower operational costs are significant advantages.

Grok 3 is set to leverage a massive computational scale, using 100,000 to potentially 200,000 Nvidia GPUs for training. This brute force approach is expected to yield a model with superior performance in various domains, especially with its multimodal capabilities and real-time data integration. However, this comes with higher computational and possibly operational costs.

Performance:

DeepSeek V3 has shown impressive results, outperforming models like Claude Sonnet 3.5 in certain benchmarks while being much cheaper to run. It's particularly noted for its reasoning capabilities and efficiency.

Grok 3 is anticipated to excel in reasoning, problem-solving, and handling complex, multimodal queries. Given its training data, including legal documents, it might have specialized knowledge in certain areas. The expectation is that Grok 3 will at least match or exceed DeepSeek V3 in performance, especially in areas where real-time data and multimodal processing give it an edge.

Cost and Accessibility:

DeepSeek V3 benefits from being open-source, which could make it more accessible for a broader range of users and developers. Its lower cost per token use makes it attractive for large-scale implementations.

Grok 3 might be more expensive due to the extensive resources used in its development. However, its integration with xAI's ecosystem, including real-time data from X, could offer unique advantages not found in open-source models like DeepSeek.

Conclusion:

Whether Grok 3 will be "as good as" DeepSeek depends on the metrics by which "good" is measured:

If cost-effectiveness and open-source accessibility are key, DeepSeek V3 might have an edge.

For raw performance, especially in multimodal and real-time data scenarios, Grok 3 could potentially outperform DeepSeek V3.

The final verdict will come with real-world applications and further benchmarking once Grok 3 is released. Both models showcase different approaches to AI development, with each having its niche where it might excel.