o3
Multimodal
Zero-eval
#1ARC-AGI
#1MathVista
#1Tau2 Airline
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by OpenAI
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About
o3 is a multimodal language model developed by OpenAI. It achieves strong performance with an average score of 67.5% across 22 benchmarks. It excels particularly in COLLIE (98.4%), AIME 2024 (91.6%), ARC-AGI (88.0%). It supports a 300K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents OpenAI's latest advancement in AI technology.
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Pricing Range
Input (per 1M)$2.00 -$2.00
Output (per 1M)$8.00 -$8.00
Providers1
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Timeline
AnnouncedApr 16, 2025
ReleasedApr 16, 2025
Knowledge CutoffMay 31, 2024
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Specifications
Capabilities
Multimodal
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License & Family
License
Proprietary
Performance Overview
Performance metrics and category breakdown
Overall Performance
22 benchmarks
Average Score
67.5%
Best Score
98.4%
High Performers (80%+)
10Performance Metrics
Max Context Window
300.0KAvg Throughput
50.0 tok/sAvg Latency
20ms+
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All Benchmark Results for o3
Complete list of benchmark scores with detailed information
| COLLIE | text | 0.98 | 98.4% | Self-reported | |
| AIME 2024 | text | 0.92 | 91.6% | Self-reported | |
| ARC-AGI | image | 0.88 | 88.0% | Self-reported | |
| MathVista | multimodal | 0.87 | 86.8% | Self-reported | |
| AIME 2025 | text | 0.86 | 86.4% | Self-reported | |
| VideoMMMU | multimodal | 0.83 | 83.3% | Self-reported | |
| GPQA | text | 0.83 | 83.3% | Self-reported | |
| MMMU | multimodal | 0.83 | 82.9% | Self-reported | |
| Aider-Polyglot | text | 0.81 | 81.3% | Self-reported | |
| Tau2 Retail | text | 0.80 | 80.2% | Self-reported |
Showing 1 to 10 of 22 benchmarks