OpenAI's experimental model
OpenAI has just surpassed another milestone in artificial intelligence - achieving performance equivalent to a gold medal at the prestigious International Mathematical Olympiad (IMO). However, this breakthrough is not just an academic achievement. For Czech companies, it represents a turning point that will fundamentally affect how we work with data, processes, and decision-making in the coming years.
AI Mathematical Performance
"This result represents a fundamental shift in AI's practical capabilities," says
David Strejc, CTO of Apertia Tech.
"The model was able to fully solve 5 out of 6 extremely challenging problems within the standard 9-hour time limit. Such performance corresponds to a gold medal, which only 67 out of 630 human participants achieved this year."
Model Performance Metrics:
- Solution success rate: 5 out of 6 problems (83.3%)
- Total score: 35/42 (83.3%)
- Solution time: 2 x 4.5 hours (IMO standard)
- Computation time per problem: 100-150 minutes of active processing
- Comparison with 2024: 25% performance increase (DeepMind 2024: 28/42 points)
Technological Breakthrough in Numbers
The
OpenAI model brings a new architecture and approach to solving complex mathematical problems:
- Universal model instead of separate specializations (DeepMind 2024 used 2 models)
- 100% natural language processing, without formal verification
- Significant acceleration: from several days to 9 hours
- Advanced reinforcement learning techniques: including dynamic computation scaling during testing
Thousand-fold Jump in Computational Complexity
"From a computational complexity standpoint, the progress is truly exceptional," adds Strejc.
"AI has managed the transition from GSM8K-type problems (0.1 minutes per problem) through AIME (10 minutes) to IMO (100 minutes). This represents more than a thousand-fold increase in complexity handled by the model within 18 months."
Evolution of AI Performance in Mathematics (2023-2025)
| Problem Type |
Time Period |
Average Solution Time |
Complexity Level |
Practical Application |
| GSM8K |
2023 |
0.1 minutes |
Basic arithmetic |
Simple invoicing, calculations |
| AIME |
2024 |
10 minutes |
High school mathematics |
Financial modeling, reporting |
| IMO |
2025 |
100 minutes |
Olympiad level |
Predictive analysis, process optimization |
Complexity increase: 1000x within 24 months
This progress is accompanied by revolutionary learning methodology, including dynamic computation scaling that allows the model to adapt its computational strategy in real-time based on the difficulty of the problem being solved.
Implications for the Czech Business Sector
"This development has fundamental implications for our clients and the entire European market," summarizes Strejc.
Three key impact areas:
- Acceleration of AI capabilities - We expect commercial use of similar models within 6-12 months
- Investment opportunities - Companies that integrate AI early will gain a significant advantage in research and development
- Education transformation - Education, especially in mathematics, computer science, and logic, will need to respond to the capabilities of modern AI models
1. Transformation of Analytical Processes
For companies using
ERP systems, this breakthrough means significant acceleration in:
- Predictive analytics: AI capable of solving complex mathematical problems can predict demand, optimize inventory, and model cash flow scenarios with unprecedented accuracy.
- Process optimization: Complex logistics and manufacturing processes can be optimized in real-time using advanced algorithms that previously required teams of analysts.
- Risk modeling: Financial and operational risks can be modeled with mathematical precision previously unavailable to small and medium enterprises.
2. Democratization of Advanced Analytical Tools
"The greatest value of this breakthrough lies in the democratization of complex analytical processes," adds Strejc.
"Tools that were previously the domain of large corporations with extensive analytical teams will become accessible to Czech SMEs through AI-powered ERP systems."
Challenges and Realistic Expectations
Although this is an extraordinary advancement, Apertia Tech also points out the limitations of current technology:
- Extreme computational demands: estimated costs of $100-1000 per IMO problem solution
- Low result reproducibility: publicly available models achieve only 15/42 points (approximately 35.7% of OpenAI's performance)
- Failure on problem #6: a complex combinatorial problem with a 2025x2025 grid remained unsolved
- Lack of formal proof verification: the model cannot independently verify the correctness of its own conclusions
| Company Size |
Current Analytical Costs |
AI Implementation (Annual) |
Potential Savings |
ROI Expectation |
| Small (10-50 employees) |
500,000 CZK |
200,000 CZK |
60% |
12-18 months |
| Medium (50-250 employees) |
2,000,000 CZK |
600,000 CZK |
70% |
8-12 months |
| Large (250+ employees) |
8,000,000 CZK |
1,500,000 CZK |
80% |
6-9 months |
Estimates based on Apertia Tech analysis and implementation experience from AutoERP clients 
