R1 achieves comparable performance to OpenAI's models on certain math and coding benchmarks, but the pace of innovation is accelerating, as evidenced by OpenAI's release of GPT-3.5 just two weeks after R1.

Despite its advancements, GPT-4.5 is significantly more expensive than previous models, costing 30 times more per input token and 15 times more per output token.

OpenAI's release of GPT-2 in November 2019 marked a significant milestone with 1.5 billion parameters, followed by GPT-3, which was over 100 times larger and more capable than its predecessor.

The cost of running inference on models like GPT-3 has drastically reduced, making advanced intelligence more accessible.

GPT-4.5 is OpenAI's largest and most humanlike model to date, representing a significant step in scaling up unsupervised learning and achieving a deeper understanding of the world and human experience.

The demand for fine-tuning open-source models is driven by the need for cost-effective alternatives to expensive models like OpenAI's ChatGPT, but companies must also provide additional value beyond just lower costs to retain customers.

Despite criticisms, OpenAI and DeepMind have been transparent about their methodologies, making it easier for others to replicate models like GPT-3 if they can secure enough compute and talent.

Research indicated that previous models like GPT-3 were undertrained; despite their size, they hadn't been trained on enough text to reach their full potential. For instance, Chinchilla, a model smaller than GPT-3, outperformed larger models by being trained on four times more data.

OpenAI revealed GPT-4.5 as a model capable of reasoning and systematically thinking through complex problems, clearly surpassing GPT-4 in mathematics, coding, and logical tasks.