OpenAI's largest model GPT-4.5 is here: is it the last of its kind?

OpenAI expanded access to its newest, largest and most knowledgable large language model (LLM) GPT-4.5 earlier this week, making it available outside the $200 monthly ChatGPT Pro tier (though it will still require a more affordable Plus or Team subscription, each of which offers lower usage amounts).

But what's the impact been so far? And what does it tell us about where the fast-moving generative AI marketplace is moving?

Evaluating the initial impact of GPT-4.5

GPT-4.5 seems to me to be OpenAI's biggest swing yet at providing general-purpose AI — that is, a model that works pretty well for everyone — which of course is all part of the company's founding goal of creating artificial general intelligence (AGI), which it defines as AI that outperforms humans at most "economically valuable" tasks.

According to Ezra Klein of 纽约时报 , individuals in government are now starting to take this possibility seriously and on a faster timeline than many previously anticipated, within the next 3 years.

And there's no doubt that GPT-4.5 is a leap forward, with advanced conversational fluency, emotional intelligence, and adaptability.

Users praise its ability to interpret subtle cues and engage in more human-like dialogue, but they criticize its limited improvements in reasoning, coding, and math compared to OpenAI’s "o-series" models released previously.

Also, GPT-4.5's steep pricing—$75 per million input tokens and $150 per million output tokens—has frustrated developers who prioritize efficiency over conversational prowess. GPT-4.5 focuses more on broad general use than targeted analytical tasks, highlighting a divergence in AI development priorities.

In contrast, OpenAI's "o" series models—such as o1, o3, and o3-mini—prioritize advanced reasoning capabilities through deliberate processing techniques. The o1 model, introduced in September 2024, employs chain-of-thought processing to emulate human-like reasoning, enhancing its performance in complex tasks like coding and scientific problem-solving. Its successor, o3, released in December 2024, further improves upon these capabilities by allocating additional deliberation time for step-by-step logical reasoning.

This approach enables o3 to excel in areas requiring structured problem-solving, marking a significant advancement over its predecessors. These models represent a strategic shift towards AI systems tailored for specialized analytical tasks for more advanced fields, contrasting with the more general-purpose design of GPT-4.5. In addition, the pricing of the "o" series models through OpenAI's API remains far more affordable than GPT-4.5.

More specialized and reasoning models arrive — many open source

At the same time, other AI companies are pushing the boundaries of what might be called Large Reasoning Models (LRMs)—AI systems optimized for complex problem-solving and structured reasoning.

Alibaba’s QwQ-32B, released this week, is a 32-billion-parameter model that employs multi-stage reinforcement learning (RL) to refine mathematical reasoning, coding proficiency, and logical problem-solving.

Unlike OpenAI’s proprietary models, QwQ-32B is open-source under an Apache 2.0 license, allowing enterprises to fine-tune and deploy it without restrictions. Impressively, despite its smaller footprint, it competes with much larger models like DeepSeek-R1, highlighting the efficiency gains of RL-driven AI.

Another major release this week, Light-R1-32B, further showcases the power of targeted, problem-specific AI. Developed by an independent research team, Light-R1-32B was trained in just six hours on $1,000 worth of compute and surpasses significantly larger models on advanced math benchmarks.

This model is further open source, exemplifing a growing shift toward cost-effective, high-performance alternatives to proprietary AI.

Meanwhile, Cohere introduced Aya Vision, a multilingual, multimodal AI model capable of interpreting images in 23 different languages. Though available under a restrictive non-commercial license, Aya Vision pushes the boundaries of AI-driven translation and accessibility, indicating growing interest in multimodal AI.

Finally, Mistral has made strides in document processing with its newly launched OCR API, which claims top performance in optical character recognition. Unlike traditional OCR solutions that simply extract text, Mistral’s API integrates with large language models to provide structured document understanding, making it a powerful tool for enterprises handling vast amounts of unstructured data.

Its performance reportedly outpaces established players like Google Document AI and OpenAI’s GPT-4o, with the added benefit of high-speed processing—up to 2,000 pages per minute.

Is the age of the general, non-reasoning LLM over?

This innovation underscores a broader industry trend: AI is increasingly being designed not just for general conversation but for specialized, high-accuracy applications in business and research.

As AI development branches into distinct areas—broad-use conversational models like GPT-4.5 versus targeted reasoning and domain-specific models—the industry is witnessing a diversification in AI priorities.

On the social network X, OpenAI CEO Sam Altman has announced plans to unify the company's different AI model families, phasing out manual selection with GPT-4.5 "Orion" before launching GPT-5 with advanced reasoning.

This suggests that GPT-4.5 may be the last of its kind, as the forthcoming GPT-5 aims to integrate reasoning capabilities directly into the model, eliminating the need for separate versions. Already, we've seen other AI providers such as Anthropic and Nous Research take this approach with toggle on and off for reasoning capabilities.

OpenAI continues refining the classical LLM approach, while competitors are building leaner, more efficient models focused on precise reasoning and problem-solving. This move reflects a maturation in the AI market, where companies are developing products tailored to specific use cases and segments, moving away from one-size-fits-all solutions.

As AI technology continues to evolve, we may look back on GPT-4.5 as a pivotal point where the industry began to prioritize specialized functionalities and unified capabilities in AI models. The coming months will likely see further advances in both directions, shaping the next generation of enterprise and consumer AI applications.

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