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o1-preview VS o1-mini
The artificial intelligence landscape has shifted significantly with OpenAI's release of the o1 series. These models, specifically o1-preview and o1-mini, utilize reinforcement learning to perform "chain-of-thought" reasoning before responding. While both are built for complex problem-solving, they serve vastly different roles in terms of performance, speed, and cost-efficiency.
This comprehensive guide analyzes the technical specifications, benchmark performance, and real-world testing results to help you decide which model fits your specific workflow. Content inspired by the analysis in Benchmarks and specs.
Technical Specifications Comparison
| Specification | o1-preview | o1-mini |
|---|---|---|
| Context Window | 128K Tokens | 128K Tokens |
| Max Output Tokens | 32,768 | 65,536 |
| Processing Speed | ~23 Tokens/sec | ~74 Tokens/sec |
| Knowledge Cutoff | October 2023 | October 2023 |
Key Insight: Interestingly, the o1-mini offers a larger output capacity and significantly higher speed, making it the "workhorse" for generation-heavy tasks.
Standardized Benchmarks
Benchmarks reveal that while o1-preview is a generalist with superior graduate-level reasoning, o1-mini punches significantly above its weight in STEM and Coding.
- 📊 MMLU (Knowledge): o1-preview (90.8%) vs o1-mini (85.2%)
- 🎓 GPQA (Reasoning): o1-preview (73.3%) vs o1-mini (60.0%)
- 💻 HumanEval (Coding): Both models tied at 92.4%
- 🔢 MATH Benchmark: o1-mini (90.0%) slightly beats o1-preview (85.5%)
Real-World Practical Testing
Test 1: Advanced Mathematics
Query: Find the greatest real number less than BD² for a rhombus on a hyperbola.
Detailed but reached incorrect limit.
Solved in 23s (Answer: 480).
Test 2: Nuance & Trick Questions
Query: Analysis of marbles in a cup turned upside down.
The preview model excels at understanding "tricks" and physical nuances that smaller models miss. It correctly identified that gravity would remove marbles from an inverted cup.
Cost-Benefit Analysis
For developers and enterprises, the cost difference is the most deciding factor after reasoning capabilities.
💰 o1-preview: $15.00 per 1M input tokens / $60.00 per 1M output tokens.
💰 o1-mini: $3.00 per 1M input tokens / $12.00 per 1M output tokens.
The o1-mini is roughly 80% cheaper than the preview model.
Final Verdict: Which should you choose?
Select o1-mini if: You are building applications for competitive coding, solving complex math, or require high-speed reasoning at a lower price point.
Select o1-preview if: You need broad general knowledge, deep philosophical reasoning, or high-level creative writing that requires a sophisticated understanding of context.
Frequently Asked Questions (FAQ)
Q1: Does o1-mini replace GPT-4o?
No. While o1-mini is better at reasoning, GPT-4o is still superior for tasks requiring real-time browsing, file uploads, and lower latency for simple chats.
Q2: Why did o1-mini beat o1-preview in math tests?
o1-mini is specifically optimized for STEM fields. Its "reasoning chain" is tuned for logic and calculation rather than broad linguistic nuance.
Q3: Can these models handle large datasets?
Both models feature a 128K context window, allowing them to process substantial documents, though o1-mini can generate twice as much text in a single response.
Q4: Is the reasoning process visible?
In the API and ChatGPT interface, you can see a summary of the reasoning "thought process," though the full raw tokens are not always exposed.