OpenAI, the pioneering artificial intelligence research organization, has launched GPT-4.5, codenamed Orion, as its most advanced large language model to date. Available initially to ChatGPT Pro subscribers and select developers, the model represents a significant leap in computational scale and training data.
However, its high operational costs—ranging from $75 to $150 per million tokens—and mixed performance across benchmarks have sparked debate about the sustainability of traditional scaling approaches in AI development.
The launch underscores a broader industry shift toward reasoning models, which simulate human-like decision-making processes, raising questions about the future trajectory of AI innovation.
GPT-4.5 builds on OpenAI’s legacy of pushing the boundaries of natural language processing. Trained with unprecedented computing power and data, the model claims improvements in factual accuracy, emotional intelligence, and creative tasks.
For instance, it outperforms its predecessor, GPT-4o, on benchmarks like SimpleQA, reducing instances of “hallucination,” or generating false information. In informal tests, GPT-4.5 demonstrated superior emotional sensitivity, offering nuanced responses to personal struggles, and excelled in creative challenges such as generating SVG graphics.
However, the model falls short in complex tasks requiring advanced reasoning. On coding benchmarks like Swe-Bench Verified and academic problem-solving tests like AIME, GPT-4.5 lags behind competitors’ reasoning models, such as Anthropic’s Claude 3.7 Sonnet and DeepSeek’s R1. This disparity highlights the limitations of traditional scaling methods, where increasing model size and data alone no longer guarantee consistent performance gains.
The launch of GPT-4.5 occurs against a backdrop of rapid evolution in AI methodologies. Competitors such as Anthropic, DeepSeek, and Perplexity have introduced reasoning models that employ techniques like chain-of-thought reasoning to simulate human decision-making. These models often outperform traditional large language models in tasks requiring multi-step logic, consistency, and precision.
OpenAI co-founder and former chief scientist Ilya Sutskever acknowledged this shift in a December 2024 statement, noting, “We’ve achieved peak data, and pre-training as we know it will unquestionably end.” This sentiment reflects broader skepticism among AI researchers and investors about the viability of continued scaling.
To address these challenges, OpenAI plans to integrate traditional and reasoning approaches in future models, beginning with GPT-5 later in 2025. This hybrid strategy aims to combine the strengths of both paradigms, potentially redefining industry standards.
The high cost of GPT-4.5 raises significant questions about its accessibility and long-term sustainability. At $75-$150 per million tokens, the model’s pricing far exceeds that of GPT-4o ($2.50-$10), making it prohibitively expensive for many users.
OpenAI has expressed uncertainty about whether GPT-4.5 will remain available through its API indefinitely, citing operational expenses and internal expectations that were not fully met during development.
Ethical considerations also loom large. The removal of a line from GPT-4.5’s white paper stating it was “not a frontier AI model” has drawn attention. Frontier AI models, as defined by the OECD, pose potential risks to national security or public health and could trigger regulatory scrutiny under the Biden administration’s 2023 executive order on AI.
By omitting this designation, OpenAI may be seeking to avoid heightened oversight, though the move could also reflect an acknowledgment of the model’s advanced capabilities.
For now, the high costs and mixed performance of GPT-4.5 may delay widespread adoption, but its advancements in emotional intelligence and creative tasks offer glimpses of transformative potential. As Dr. Smith aptly noted, “The future of AI lies not just in bigger models but in smarter ones.”
As OpenAI navigates these challenges, the broader AI landscape continues to evolve, with profound implications for innovation, regulation, and accessibility.
The question remains: Can the industry sustain its breakneck pace of progress while addressing the ethical and economic constraints that accompany it? Only time—and rigorous research—will tell.
