The AI landscape is dominated by OpenAI (GPT-4), Google (Gemini), and Anthropic (Claude), each backed by billions in investment and large teams. As most of you already know, DeepSeek, a Chinese startup, has challenged this status quo, developing competitive AI models with under $6 million in reported funding and a team of fewer than 200 employees (based on DeepSeek's report), compared to thousands at its Western counterparts.
DeepSeek’s Mixture-of-Experts (MoE) architecture, activating only 37 billion of its 671 billion parameters per task, has made it an efficiency leader. A key feature of DeepSeek-R1 is its ability to explain its reasoning, providing insights beyond simple answers—enhancing learning, critical thinking, and problem-solving.
However, OpenAI and others have raised concerns that DeepSeek may have trained on outputs from existing LLMs using a technique called distillation, where a smaller model learns from a larger one. While no definitive evidence has been presented, the debate raises ethical and legal questions about AI training practices.
In response to DeepSeek’s rise, OpenAI launched o3-mini, a model optimized for advanced reasoning with reduced computational costs, signaling an effort to stay ahead in the evolving AI landscape.
In this blog, we compare DeepSeek with leading LLMs, analyzing architecture, strengths, limitations, and best-fit applications to see how it stacks up against the industry’s most powerful models.
DeepSeek's Unique Approach
DeepSeek has released multiple AI models, including DeepSeek-V3 and DeepSeek-R1, both utilizing the Mixture-of-Experts (MoE) approach to optimize performance and computational efficiency.
DeepSeek-V3 is a state-of-the-art model that balances efficiency with powerful reasoning capabilities.
DeepSeek-R1 stands out because, unlike most models that just provide answers, it also explains its reasoning process, making it more interpretable and transparent.
DeepSeek’s approach allows it to deliver high performance at a fraction of the cost of traditional AI models while promoting open-source collaboration.
OpenAI's o3-mini Model
In response to emerging competition, OpenAI has developed o3-mini, a compact and efficient model designed to deliver advanced reasoning capabilities with reduced computational requirements.
Key Features:
Advanced Reasoning: o3-mini excels in tasks requiring complex problem-solving and logical reasoning, making it suitable for applications in science, mathematics, and coding.
Cost-Effective: The model is optimized to provide high performance while minimizing computational costs, making it accessible to a broader range of users.
Integration: o3-mini is integrated into OpenAI's ChatGPT and API services, allowing for seamless deployment across various applications.
Comparison with Leading LLMs
Model | Org. | Params. | Emp. | Invest. | Key Features |
---|---|---|---|---|---|
DeepSeek-V3 & R1 | DeepSeek | 671B (MoE) | <200 | <$6 million | Mixture-of-Experts, open-source, efficiency-driven, R1 explains reasoning |
o3-mini | OpenAI | Not specified | ~1,700 | $10 billion+ | Advanced reasoning, cost-effective, integrated with ChatGPT and API services |
GPT-4 | OpenAI | 1T+ | ~1,700 | $10 billion+ | Transformer-based, proprietary, extensive training data |
Gemini | 1T+ | ~190,000 | Part of AB's $1.5T valuation | Integrated with Google search, proprietary, large-scale infrastructure | |
Claude | Anthropic | 1T+ | ~1,035 | $4 billion+ | AI safety & alignment focus, proprietary, substantial computational resources |
Pros and Cons of Each Model
1. DeepSeek-V3 & R1
Pros:
High Efficiency: Uses an MoE architecture, requiring fewer active parameters per task.
Open-Source: Encourages innovation and transparency.
Explains Reasoning (R1): Provides not only answers but also insights into its thought process.
Cons:
Scalability Concerns: May face challenges when expanding to more complex AI applications.
Limited Resources: Smaller team and budget compared to competitors.
2. o3-mini (OpenAI)
Pros:
Advanced Reasoning: Excels in complex problem-solving, particularly in STEM fields.
Cost-Effective: Optimized to deliver high performance while minimizing costs.
Seamless Integration: Easily deployable across various applications through OpenAI's platforms.
Cons:
Proprietary Model: Limited transparency and community collaboration.
Resource Intensive: Despite optimizations, still requires substantial resources for training and deployment.
3. GPT-4 (OpenAI)
Pros:
Top Performance: State-of-the-art in reasoning and language capabilities.
Extensive Data: Trained on a massive dataset enabling versatility across various domains.
Cons:
High Cost: Requires significant computational resources, making it expensive to operate.
Closed-Source: Limits external collaboration and customization opportunities.
4. Gemini (Google)
Pros:
Multimodal Capabilities: Designed for text, images, and audio processing.
Deep Integration: Leverages Google's vast search infrastructure and cloud ecosystem.
Cons:
Resource-Intensive: High computational cost for training and inference.
Lack of Transparency: Details on architecture and training are not fully disclosed.
5. Claude (Anthropic)
Pros:
AI Safety-Focused: Strong emphasis on alignment and ethical AI development.
Expanding Capabilities: Rapidly improving in natural language understanding.
Cons:
Still Growing: Not as widely adopted as GPT-4 or Gemini.
Proprietary: Limited external access for modification or training.
Conclusion
The release of OpenAI's o3-mini suggests a response to emerging competitors like DeepSeek, aiming to provide efficient, high-performing models that reduce operational costs while maintaining strong reasoning capabilities. Meanwhile, DeepSeek’s R1 and V3 models highlight that resource efficiency and open-source collaboration can be strong differentiators in a field dominated by high-cost, proprietary AI systems.
While OpenAI and Google continue to dominate in scale and investment, DeepSeek’s innovative Mixture-of-Experts approach, coupled with its commitment to transparency, has positioned it as a serious challenger. As AI development accelerates, the competition between high-budget, large-scale models and streamlined, efficient architectures will shape the future of AI accessibility and innovation.
Which model is best?
For cost-effective AI with transparency: DeepSeek-R1 and o3-mini provide efficient, accessible alternatives.
For top-tier AI performance and deep integration: GPT-4 and Gemini currently continue to lead.
For AI alignment and safety-focused development: Claude offers a structured and ethical approach.
What’s Next?
The AI race is far from over, and the industry will continue to see advancements in efficiency, cost reduction, and reasoning capabilities. As we move forward, expect more compact, efficient models from big tech and continued innovation from emerging AI labs like DeepSeek.
For now, DeepSeek R1 vs. OpenAI's o3-mini marks a new chapter in AI competition, one that prioritizes resourceful engineering over brute computational power.

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