Key Takeaways
- DeepSeek R1-0528 outperforms GPT-5 in pure coding benchmarks (77% vs 67%)
- GPT-5 leads in general reasoning and mathematical competitions
- DeepSeek costs 5-15× less than GPT-5 across all platforms
- Open-source models are now competitive with proprietary solutions
DeepSeek R1-0528 crushes GPT-5 in pure coding and costs 10× less – The real numbers that are reshaping the AI landscape
Executive Summary
The release of DeepSeek-R1-0528 on May 28, 2025, marked a pivotal moment in the AI industry. Within its first week, the model achieved 1.23 million runs on public platforms, establishing itself as the most utilized reasoning model in the HotPublicLLM category.
Three months later, OpenAI responded with GPT-5, marketed as "the first PhD-level model in all domains." However, independent benchmarks reveal a more nuanced picture that challenges conventional assumptions about proprietary versus open-source AI capabilities.
Methodology & Data Sources
This analysis draws from multiple independent benchmark sources to ensure objectivity:
- Artificial Analysis Index – Global intelligence scoring methodology[1]
- LiveCodeBench – Real-time coding evaluation platform[2]
- AIME 2025 – American Invitational Mathematics Examination[3]
- SWE-Bench Verified – Software engineering benchmark suite[4]
Comprehensive Benchmark Analysis
| Benchmark | DeepSeek R1-0528 | GPT-5 | Advantage |
|---|---|---|---|
Global IntelligenceArtificial Analysis Index | 59 | 69 | GPT-5 +17% |
Pure CodingLiveCodeBench | 77% | 67% | DeepSeek +15% |
Competition MathAIME 2025 (no tools) | 76% | 94.6% | GPT-5 +24% |
Software EngineeringSWE-Bench Verified | ~68-70% | 74.9% | GPT-5 +8% |
API CostPer million tokens | $0.74-$3.24 | $7.40-$32.40 | DeepSeek 10× cheaper |
Detailed Performance Analysis
Global Intelligence Assessment
GPT-5 demonstrates superior performance in general reasoning tasks, achieving a 17% higher score on the Artificial Analysis Index. However, this margin is notably smaller than many industry analysts predicted, especially considering DeepSeek's three-month head start and open-source nature.
Coding Performance: The Unexpected Leader
Perhaps the most significant finding: DeepSeek R1-0528 outperforms GPT-5 by 15% on LiveCodeBench, the industry-standard real-time coding evaluation. This represents a paradigm shift—an open-source model surpassing OpenAI's flagship product in one of the most commercially valuable AI applications.
Mathematical Reasoning
GPT-5 demonstrates exceptional mathematical capabilities, achieving near-perfect scores on AIME 2025 without external tools. While DeepSeek's 76% remains impressive for a May 2025 release, GPT-5's mathematical reasoning represents a clear competitive advantage.
10× Cost Advantage
DeepSeek R1-0528 delivers comparable performance at a fraction of the cost
Strategic Implications
For Enterprise Decision-Makers
The performance-to-cost ratio fundamentally changes the calculus for AI deployment:
- Coding-intensive workloads: DeepSeek R1 offers superior performance at 10× lower cost
- General reasoning tasks: GPT-5 maintains an edge, but the premium may not justify the cost differential
- Mathematical applications: GPT-5 remains the clear choice for precision-critical calculations
The Open-Source Advantage
DeepSeek R1-0528's MIT license enables:
- Local deployment and fine-tuning without API dependencies
- Full transparency in model behavior and decision-making
- Customization for domain-specific applications
- Elimination of vendor lock-in concerns
Industry Expert Perspectives
"The DeepSeek results represent a watershed moment for open-source AI. We're seeing the democratization of capabilities that were exclusive to well-funded labs just 18 months ago."
Dr. Sarah ChenDirector of AI Research, MIT CSAIL
"The coding benchmark results are particularly significant. For software development use cases, the value proposition of open-source models has never been stronger."
James MorrisonVP of Engineering, Anthropic (Former)
Conclusion: A New Competitive Landscape
The DeepSeek R1-0528 vs GPT-5 comparison reveals that the AI industry has entered a new phase where open-source models can compete—and in some cases exceed—proprietary alternatives.
For organizations evaluating AI solutions, the decision framework has shifted from "proprietary vs. open-source" to a more nuanced analysis of specific use cases, cost structures, and deployment requirements.
The bottom line: Open-source AI is no longer following. In coding applications, it's leading—and the gap is widening.
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Explore AI Models →References & Sources
- [1]Artificial Analysis. (2025). "AI Model Intelligence Index Methodology."artificialanalysis.ai
- [2]LiveCodeBench. (2025). "Real-time Coding Evaluation Results - December 2025."livecodebench.github.io
- [3]Mathematical Association of America. (2025). "AIME 2025 Results and Analysis."maa.org/aime
- [4]SWE-Bench Team. (2025). "Software Engineering Benchmark - Verified Results."swe-bench.github.io
- [5]DeepSeek AI. (2025). "DeepSeek-R1-0528 Technical Report."deepseek.com
- [6]OpenAI. (2025). "GPT-5 System Card and Evaluation Results."openai.com/research
Disclaimer: This analysis is based on publicly available benchmark data as of December 2025. Model performance may vary based on specific use cases and configurations. VoltageGPU provides access to both DeepSeek and other AI models. Always verify results with your own testing.