An in-depth analysis of performance, pricing, usability, and capabilities for developers and enterprises
Introduction
Today, after many requests we do an enhanced comparison between the current most promising AI models, DeepSeek-R1 and OpenAI o1. Both models have been reported to be highly capable deep reasoning models and are serious candidates for AGI.
1. Performance
Mathematical & Coding Benchmarks
- MATH-500: DeepSeek-R1 edges out OpenAI-o1 with a 97.3% pass@1 vs. 96.4%, demonstrating superior mathematical reasoning.
- SWE-bench: DeepSeek-R1 resolves 49.2% of software engineering tasks vs. OpenAI-o1’s 48.9%, showing slight superiority in code debugging.
- Codeforces: OpenAI-o1 leads slightly in competitive coding (96.6 percentile vs. 96.3).
General Knowledge
- MMLU: OpenAI-o1 scores 91.8% vs. DeepSeek-R1’s 90.8%, reflecting stronger broad-domain understanding.
- GPQA Diamond: OpenAI-o1 excels in PhD-level questions (75.7% vs. 71.5%).
Architecture & Context
| Feature | DeepSeek-R1 | OpenAI-o1 |
|---|---|---|
| Context Window | 128K tokens | 200K tokens |
| Parameter Efficiency | MoE (37B active of 671B) | Reasoning tokens |
| Modalities | Text-only | Text + Image |
DeepSeek’s MoE architecture ensures efficient computation, while OpenAI-o1’s reasoning tokens prioritize step-by-step problem-solving.
2. Pricing
Cost Comparison (Per Million Tokens)
| Cost Type | DeepSeek-R1 | OpenAI-o1 | Savings |
|---|---|---|---|
| Input Tokens | $0.55 | $15.00 | 27.3x |
| Output Tokens | $2.19 | $60.00 | 27.4x |
DeepSeek’s pricing is ~27x cheaper, making it ideal for startups and high-volume use cases. Additionally, its open-source MIT license allows free modification and commercial use, unlike OpenAI’s proprietary model.
3. Ease of Use
Integration & Deployment
- DeepSeek-R1:
- OpenAI-Compatible API: Directly integrates with existing OpenAI SDKs by modifying the base URL.
- Local Deployment: Supports Ollama, vLLM, and SGLang for offline use.
- Streaming Support: Available but may face occasional latency (19.86s TTFT).
- OpenAI-o1:
- Enterprise Integration: Seamless with Azure and ChatGPT Plus for enterprise workflows.
- Vision API: Supports image processing, a feature absent in DeepSeek.
4. Coding Examples
Please note that API uses of DeepSeek and OpenAI are identical. Only one example is given.
Fibonacci Sequence
DeepSeek-R1 (Iterative, Efficient)
“`python
def fib(n):
if n == 0:
return 0
elif n == 1:
return 1
prev1, prev2 = 0, 1
for i in range(2, n+1):
current = prev1 + prev2
prev1, prev2 = prev2, current
return prev2
*Explanation: Iterative approach avoids recursion stack limits, ideal for large `n`.*
**OpenAI-o1 (Recursive, Simple)** python
def fib(n):
if n <= 1:
return n
else:
return fib(n-1) + fib(n-2)`` *Explanation: Beginner-friendly but inefficient for largen` due to exponential time complexity.*
5. Benchmarks Summary
| Benchmark | DeepSeek-R1 | OpenAI-o1 | Winner |
|---|---|---|---|
| MATH-500 | 97.3% | 96.4% | DeepSeek-R1 |
| MMLU | 90.8% | 91.8% | OpenAI-o1 |
| GPQA Diamond | 71.5% | 75.7% | OpenAI-o1 |
| SWE-bench | 49.2% | 48.9% | DeepSeek-R1 |
DeepSeek excels in math/code tasks, while OpenAI leads in general knowledge.
6. References
- Official Documentation:
- DeepSeek-R1 GitHub
- OpenAI-o1 API Docs
- Benchmark Analyses:
- Analytics Vidhya Comparison
- DeepSeek-R1 Pricing
- Code Examples:
- DeepSeek API Tutorial
Final Recommendation
- Personally I already switched recently from OpenAI to DeepSeek for my Content Writing and coding tasks and am happy with the choice. For the images in this blog I still use OpenAI ChatGPT. OpenAI has already announced that ChatGPT will come available for free soon.
- IStartups & Budget-Conscious Teams: Choose DeepSeek-R1 for cost efficiency and open-source flexibility.
- Enterprises & Multimodal Needs: Opt for OpenAI-o1 for enterprise reliability and image processing.
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