The AI industry is witnessing a seismic shift with the introduction of DeepSeek-R1, a cutting-edge open-source reasoning model developed by the eponymous Chinese startup DeepSeek. Released on January 20, this model is challenging OpenAI’s o1 — a flagship AI system — by delivering comparable performance at a fraction of the cost. But how do these models stack up in real-world applications? And what does this mean for enterprises and developers?

In this article, we dive deep into hands-on testing, practical implications and actionable insights to help technical decision-makers understand which model best suits their needs.

Real-world implications: Why this comparison matters

The competition between DeepSeek-R1 and OpenAI o1 isn’t just about benchmarks — it’s about real-world impact. Enterprises are increasingly relying on AI for tasks like data analysis, customer service automation, decision-making and coding assistance. The choice between these models can significantly affect cost efficiency, workflow optimization and innovation potential.

Key Questions for Enterprises:

• Can DeepSeek-R1’s cost savings justify its adoption over OpenAI o1?
• How do these models perform in real-world scenarios like mathematical computation, reasoning based analysis, financial modeling or software development?
• What are the trade-offs between open-source flexibility (DeepSeek-R1) and proprietary robustness (OpenAI o1)?

To answer these questions, we conducted hands-on testing across reasoning, mathematical problem-solving, coding tasks and decision-making scenarios. Here’s what we found.

Hands-on testing: How DeepSeek and OpenAI o1 perform

Question 1: Logical inference

If A = B, B = C, and C ≠ D, what definitive conclusion can be drawn about A and D?

Analysis:

• OpenAI o1: Well-structured reasoning with formal statements.
• DeepSeek-R1: Equally accurate, more concise presentation.
• Processing time: DeepSeek (0.5s) versus OpenAI (2s).
• Winner: DeepSeek-R1 (equal accuracy, 4X faster, more concise).

Metrics:

• Tokens: DeepSeek (20) vs OpenAI (42).
• Cost: DeepSeek ($0.00004) vs OpenAI ($0.0008).

Key Insight: DeepSeek-R1 achieves the same logical clarity with better efficiency, making it ideal for high-volume, real-time applications.

Question 2: Set theory problem

In a room of 50 people, 30 like coffee, 25 like tea and 15 like both. How many people like neither coffee nor tea?

Analysis:

• OpenAI o1: Detailed mathematical notation.
• DeepSeek-R1: Direct solution with clear steps.
• Processing time: DeepSeek (1s) versus OpenAI (3s).
• Winner: DeepSeek-R1 (clearer presentation, 3x faster).

Metrics:

• Tokens: DeepSeek (40) vs OpenAI (64).
• Cost: DeepSeek ($0.00008) vs OpenAI ($0.0013).

Key Insight: DeepSeek-R1’s concise approach maintains clarity while improving speed.

Question 3: Mathematical calculation

Calculate the exact value of: √(144) + (15² ÷ 3) – 36.

Analysis:

• OpenAI o1: Numbered steps with detailed breakdown.
• DeepSeek-R1: Clear line-by-line calculation.
• Processing time: DeepSeek (1s) versus OpenAI (2s).
• Winner: DeepSeek-R1 (equal clarity, 2X faster).

Metrics:

• Tokens: DeepSeek (30) vs OpenAI (60).
• Cost: DeepSeek ($0.00006) vs OpenAI ($0.0012).

Key Insight: Both models are accurate; DeepSeek-R1 is more efficient.

Question 4: Advanced mathematics

If x + y = 10 and x² + y² = 50, what are the precise values of x and y?

Analysis:

• OpenAI o1: Comprehensive solution with detailed steps.
• DeepSeek-R1: Efficient solution with key steps highlighted.
• Processing time: DeepSeek (2s) versus OpenAI (5s).
• Winner: Tie (OpenAI better for learning; DeepSeek better for practice).

Metrics:

• Tokens: DeepSeek (60) vs OpenAI (134).
• Cost: DeepSeek ($0.00012) vs OpenAI ($0.0027).

Key Insight: Choice depends on use case — teaching versus practical application. DeepSeek-R1 excels in speed and accuracy for logical and mathematical tasks, making it ideal for industries like finance, engineering and data science.

Question 5: Investment analysis

A company has a $100,000 budget. Investment options: Option A yields a 7% return with 20% risk, while Option B yields a 5% return with 10% risk. Which option maximizes potential gain while minimizing risk?

Analysis:

• OpenAI o1: Detailed risk-return analysis.
• DeepSeek-R1: Direct comparison with key metrics.
• Processing time: DeepSeek (1.5s) versus OpenAI (4s).
• Winner: DeepSeek-R1 (Sufficient analysis, 2.7X faster).

Metrics:

• Tokens: DeepSeek (50) vs OpenAI (110).
• Cost: DeepSeek ($0.00010) vs OpenAI ($0.0022).

Key insight: Both models perform well in decision-making tasks, but DeepSeek-R1’s concise and actionable outputs make it more suitable for time-sensitive applications. DeepSeek-R1 provides actionable insights more efficiently.

Question 6: Efficiency calculation

You have three delivery routes with different distances and time constraints:

• Route A: 120 km, 2 hours
• Route B: 90 km, 1.5 hours
• Route C: 150 km, 2.5 hours

Which route is most efficient?

Analysis:

• OpenAI o1: Structured analysis with methodology.
• DeepSeek-R1: Clear calculations with direct conclusion,
• Processing time: DeepSeek (1.5s) versus OpenAI (3s).
• Winner: DeepSeek-R1 (Equal accuracy, 2X faster).

Metrics:

• Tokens: DeepSeek (50) vs OpenAI (112).
• Cost: DeepSeek ($0.00010) vs OpenAI ($0.0022).

Key insight: Both are accurate; DeepSeek-R1 is more time-efficient. 

Question 7: Coding task

Write a function to find the most frequent element in an array with O(n) time complexity.

Analysis:

• OpenAI o1: Well-documented code with explanations.
• DeepSeek-R1: Clean code with essential documentation.
• Processing time: DeepSeek (2s) versus OpenAI (4s).
• Winner: Depends on use case (DeepSeek for implementation, OpenAI for learning).

Metrics:

• Tokens: DeepSeek (70) vs OpenAI (174).
• Cost: DeepSeek ($0.00014) vs OpenAI ($0.0035).

Key insight: Both are effective, with different strengths for different needs. DeepSeek-R1’s coding proficiency and optimization capabilities make it a strong contender for software development and automation tasks.

Question 8: Algorithm design

Design an algorithm to check if a given number is a perfect palindrome without converting it to a string.

Analysis:

• OpenAI o1: Comprehensive solution with detailed explanation.
• DeepSeek-R1: Efficient implementation with key points.
• Processing time: DeepSeek (2s) versus OpenAI (5s).
• Winner: Depends on context (DeepSeek for implementation, OpenAI for understanding).

Metrics:

• Tokens: DeepSeek (70) vs OpenAI (220).
• Cost: DeepSeek ($0.00014) vs OpenAI ($0.0044).

Key Insight: Choice depends on primary need — speed versus detail.

Overall performance metrics

• Total processing time: DeepSeek (11.5s) vs OpenAI (28s).
• Total tokens: DeepSeek (390) versus OpenAI (916).
• Total cost: DeepSeek ($0.00078) versus OpenAI ($0.0183).

Recommendations

1. Production environment
   • Primary: DeepSeek-R1.
   • Benefits: Faster processing, lower costs, sufficient accuracy.
   • Best for: APIs, high-volume processing, real-time applications.
2. Educational/training
   • Primary: OpenAI o1.
   • Alternative: DeepSeek-R1 for practice exercises.
   • Best for: Detailed explanations, learning new concepts.
3. Enterprise development
   • Primary: DeepSeek-R1 for implementation.
   • Secondary: OpenAI o1 for documentation.
   • Consider: Hybrid approach based on specific needs.
4. Cost-sensitive operations
   • Strongly recommend: DeepSeek-R1.
   • Reason: 2.4X faster, ~23X more cost-efficient.
   • Note: Maintains quality while reducing resource usage.

Conclusion: Which model should you choose?

The choice between DeepSeek-R1 and OpenAI o1 depends on your specific needs and priorities.

Choose DeepSeek-R1 if:

• You prioritize cost efficiency, as it is 23X more cost-effective.
• Faster processing (2.4X faster on average) is crucial for your needs.
• Your focus is on real-time applications, high-volume processing or efficient mathematical computations.
• You’re a startup, researcher or developer seeking an affordable, open-source, customizable AI solution.

Choose OpenAI o1 if:

• You need detailed reasoning and step-by-step explanations for educational or training purposes.
• Broad reasoning capabilities and enterprise-grade reliability are critical for your projects.
• Budget is not a major constraint, and you value polished performance, comprehensive documentation and corporate support.

Choose a hybrid approach if:

• You have diverse needs across different projects.
• You want to use DeepSeek-R1 for rapid development and implementation.
• You need OpenAI o1 for creating detailed documentation or training materials.

Final thoughts

The rise of DeepSeek-R1 signifies a transformative shift in AI development, presenting a cost-effective, high-performance alternative to commercial models like OpenAI’s o1. Its open-source nature and robust reasoning capabilities position it as a game-changer for startups, developers and budget-conscious enterprises.

Performance analysis of DeepSeek-R1 indicates a substantial advancement in AI capabilities, delivering not only cost savings but also measurably faster processing (2.4X) and clearer outputs compared to OpenAI’s o1. The model’s combination of speed, efficiency and clarity makes it an ideal choice for production environments and real-time applications.

As the AI landscape evolves, the competition between DeepSeek-R1 and OpenAI o1 is likely to spur innovation and enhance accessibility, benefiting the entire ecosystem. Whether you are a technical decision-maker or an inquisitive developer, now is the moment to explore how these models can revolutionize your workflows and unlock new opportunities. The future of AI appears increasingly nuanced, with models being evaluated based on measurable performance rather than brand affiliation.